--- name: tech-debt-tracker description: Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation. --- # Tech Debt Tracker **Tier**: POWERFUL 🔥 **Category**: Engineering Process Automation **Expertise**: Code Quality, Technical Debt Management, Software Engineering ## Overview Tech debt is one of the most insidious challenges in software development - it compounds over time, slowing down development velocity, increasing maintenance costs, and reducing code quality. This skill provides a comprehensive framework for identifying, analyzing, prioritizing, and tracking technical debt across codebases. Tech debt isn't just about messy code - it encompasses architectural shortcuts, missing tests, outdated dependencies, documentation gaps, and infrastructure compromises. Like financial debt, it accrues "interest" through increased development time, higher bug rates, and reduced team velocity. ## What This Skill Provides This skill offers three interconnected tools that form a complete tech debt management system: 1. **Debt Scanner** - Automatically identifies tech debt signals in your codebase 2. **Debt Prioritizer** - Analyzes and prioritizes debt items using cost-of-delay frameworks 3. **Debt Dashboard** - Tracks debt trends over time and provides executive reporting Together, these tools enable engineering teams to make data-driven decisions about tech debt, balancing new feature development with maintenance work. ## Technical Debt Classification Framework → See references/debt-frameworks.md for details ## Implementation Roadmap ### Phase 1: Foundation (Weeks 1-2) 1. Set up debt scanning infrastructure 2. Establish debt taxonomy and scoring criteria 3. Scan initial codebase and create baseline inventory 4. Train team on debt identification and reporting ### Phase 2: Process Integration (Weeks 3-4) 1. Integrate debt tracking into sprint planning 2. Establish debt budgets and allocation rules 3. Create stakeholder reporting templates 4. Set up automated debt scanning in CI/CD ### Phase 3: Optimization (Weeks 5-6) 1. Refine scoring algorithms based on team feedback 2. Implement trend analysis and predictive metrics 3. Create specialized debt reduction initiatives 4. Establish cross-team debt coordination processes ### Phase 4: Maturity (Ongoing) 1. Continuous improvement of detection algorithms 2. Advanced analytics and prediction models 3. Integration with planning and project management tools 4. Organization-wide debt management best practices ## Success Criteria **Quantitative Metrics:** - 25% reduction in debt interest rate within 6 months - 15% improvement in development velocity - 30% reduction in production defects - 20% faster code review cycles **Qualitative Metrics:** - Improved developer satisfaction scores - Reduced context switching during feature development - Faster onboarding for new team members - Better predictability in feature delivery timelines ## Common Pitfalls and How to Avoid Them ### 1. Analysis Paralysis **Problem**: Spending too much time analyzing debt instead of fixing it. **Solution**: Set time limits for analysis, use "good enough" scoring for most items. ### 2. Perfectionism **Problem**: Trying to eliminate all debt instead of managing it. **Solution**: Focus on high-impact debt, accept that some debt is acceptable. ### 3. Ignoring Business Context **Problem**: Prioritizing technical elegance over business value. **Solution**: Always tie debt work to business outcomes and customer impact. ### 4. Inconsistent Application **Problem**: Some teams adopt practices while others ignore them. **Solution**: Make debt tracking part of standard development workflow. ### 5. Tool Over-Engineering **Problem**: Building complex debt management systems that nobody uses. **Solution**: Start simple, iterate based on actual usage patterns. Technical debt management is not just about writing better code - it's about creating sustainable development practices that balance short-term delivery pressure with long-term system health. Use these tools and frameworks to make informed decisions about when and how to invest in debt reduction. FILE:README.md # Tech Debt Tracker A comprehensive technical debt management system that helps engineering teams identify, prioritize, and track technical debt across codebases. This skill provides three interconnected tools for a complete debt management workflow. ## Overview Technical debt is like financial debt - it compounds over time and reduces team velocity if not managed systematically. This skill provides: - **Automated Debt Detection**: Scan codebases to identify various types of technical debt - **Intelligent Prioritization**: Use proven frameworks to prioritize debt based on business impact - **Trend Analysis**: Track debt evolution over time with executive-friendly dashboards ## Tools ### 1. Debt Scanner (`debt_scanner.py`) Scans codebases to automatically detect technical debt signals using AST parsing for Python and regex patterns for other languages. **Features:** - Detects 15+ types of technical debt (large functions, complexity, duplicates, security issues, etc.) - Multi-language support (Python, JavaScript, Java, C#, Go, etc.) - Configurable thresholds and rules - Dual output: JSON for tools, human-readable for reports **Usage:** ```bash # Basic scan python scripts/debt_scanner.py /path/to/codebase # With custom config and output python scripts/debt_scanner.py /path/to/codebase --config config.json --output report.json # Different output formats python scripts/debt_scanner.py /path/to/codebase --format both ``` ### 2. Debt Prioritizer (`debt_prioritizer.py`) Takes debt inventory and creates prioritized backlog using proven prioritization frameworks. **Features:** - Multiple prioritization frameworks (Cost of Delay, WSJF, RICE) - Business impact analysis with ROI calculations - Sprint allocation recommendations - Effort estimation with risk adjustment - Executive and engineering reports **Usage:** ```bash # Basic prioritization python scripts/debt_prioritizer.py debt_inventory.json # Custom framework and team size python scripts/debt_prioritizer.py inventory.json --framework wsjf --team-size 8 # Sprint capacity planning python scripts/debt_prioritizer.py inventory.json --sprint-capacity 80 --output backlog.json ``` ### 3. Debt Dashboard (`debt_dashboard.py`) Analyzes historical debt data to provide trend analysis, health scoring, and executive reporting. **Features:** - Health score trending over time - Debt velocity analysis (accumulation vs resolution) - Executive summary with business impact - Forecasting based on current trends - Strategic recommendations **Usage:** ```bash # Single directory of scans python scripts/debt_dashboard.py --input-dir ./debt_scans/ # Multiple specific files python scripts/debt_dashboard.py scan1.json scan2.json scan3.json # Custom analysis period python scripts/debt_dashboard.py data.json --period quarterly --team-size 6 ``` ## Quick Start ### 1. Scan Your Codebase ```bash # Scan your project python scripts/debt_scanner.py ~/my-project --output initial_scan.json # Review the results python scripts/debt_scanner.py ~/my-project --format text ``` ### 2. Prioritize Your Debt ```bash # Create prioritized backlog python scripts/debt_prioritizer.py initial_scan.json --output backlog.json # View sprint recommendations python scripts/debt_prioritizer.py initial_scan.json --format text ``` ### 3. Track Over Time ```bash # After multiple scans, analyze trends python scripts/debt_dashboard.py scan1.json scan2.json scan3.json --output dashboard.json # Generate executive report python scripts/debt_dashboard.py --input-dir ./scans/ --format text ``` ## Configuration ### Scanner Configuration Create `config.json` to customize detection rules: ```json { "max_function_length": 50, "max_complexity": 10, "max_nesting_depth": 4, "ignore_patterns": ["*.test.js", "build/", "node_modules/"], "file_extensions": { "python": [".py"], "javascript": [".js", ".jsx", ".ts", ".tsx"] } } ``` ### Team Configuration Adjust tools for your team size and sprint capacity: ```bash # 8-person team with 2-week sprints python scripts/debt_prioritizer.py inventory.json --team-size 8 --sprint-capacity 160 ``` ## Sample Data The `assets/` directory contains sample data for testing: - `sample_codebase/`: Example codebase with various debt types - `sample_debt_inventory.json`: Example debt inventory - `historical_debt_*.json`: Sample historical data for trending Try the tools on sample data: ```bash # Test scanner python scripts/debt_scanner.py assets/sample_codebase # Test prioritizer python scripts/debt_prioritizer.py assets/sample_debt_inventory.json # Test dashboard python scripts/debt_dashboard.py assets/historical_debt_*.json ``` ## Understanding the Output ### Health Score (0-100) - **85-100**: Excellent - Minimal debt, sustainable practices - **70-84**: Good - Manageable debt level, some attention needed - **55-69**: Fair - Debt accumulating, requires focused effort - **40-54**: Poor - High debt level, impacts productivity - **0-39**: Critical - Immediate action required ### Priority Levels - **Critical**: Security issues, blocking problems (fix immediately) - **High**: Significant impact on quality or velocity (next sprint) - **Medium**: Moderate impact, plan for upcoming work (next quarter) - **Low**: Minor issues, fix opportunistically (when convenient) ### Debt Categories - **Code Quality**: Large functions, complexity, duplicates - **Architecture**: Design issues, coupling problems - **Security**: Vulnerabilities, hardcoded secrets - **Testing**: Missing tests, poor coverage - **Documentation**: Missing or outdated docs - **Dependencies**: Outdated packages, license issues ## Integration with Development Workflow ### CI/CD Integration Add debt scanning to your CI pipeline: ```bash # In your CI script python scripts/debt_scanner.py . --output ci_scan.json # Compare with baseline, fail build if critical issues found ``` ### Sprint Planning 1. **Weekly**: Run scanner to detect new debt 2. **Sprint Planning**: Use prioritizer for debt story sizing 3. **Monthly**: Generate dashboard for trend analysis 4. **Quarterly**: Executive review with strategic recommendations ### Code Review Integration Use scanner output to focus code reviews: ```bash # Scan PR branch python scripts/debt_scanner.py . --output pr_scan.json # Compare with main branch baseline # Focus review on areas with new debt ``` ## Best Practices ### Debt Management Strategy 1. **Prevention**: Use scanner in CI to catch debt early 2. **Prioritization**: Always use business impact for priority 3. **Allocation**: Reserve 15-20% sprint capacity for debt work 4. **Measurement**: Track health score and velocity impact 5. **Communication**: Use dashboard reports for stakeholders ### Common Pitfalls to Avoid - **Analysis Paralysis**: Don't spend too long on perfect prioritization - **Technical Focus Only**: Always consider business impact - **Inconsistent Application**: Ensure all teams use same approach - **Ignoring Trends**: Pay attention to debt accumulation rate - **All-or-Nothing**: Incremental debt reduction is better than none ### Success Metrics - **Health Score Improvement**: Target 5+ point quarterly improvement - **Velocity Impact**: Keep debt velocity impact below 20% - **Team Satisfaction**: Survey developers on code quality satisfaction - **Incident Reduction**: Track correlation between debt and production issues ## Advanced Usage ### Custom Debt Types Extend the scanner for organization-specific debt patterns: 1. Add patterns to `config.json` 2. Modify detection logic in scanner 3. Update categorization in prioritizer ### Integration with External Tools - **Jira/GitHub**: Import debt items as tickets - **SonarQube**: Combine with static analysis metrics - **APM Tools**: Correlate debt with performance metrics - **Chat Systems**: Send debt alerts to team channels ### Automated Reporting Set up automated debt reporting: ```bash #!/bin/bash # Daily debt monitoring script python scripts/debt_scanner.py . --output daily_scan.json python scripts/debt_dashboard.py daily_scan.json --output daily_report.json # Send report to stakeholders ``` ## Troubleshooting ### Common Issues **Scanner not finding files**: Check `ignore_patterns` in config **Prioritizer giving unexpected results**: Verify business impact scoring **Dashboard shows flat trends**: Need more historical data points ### Performance Tips - Use `.gitignore` patterns to exclude irrelevant files - Limit scan depth for large monorepos - Run dashboard analysis on subset for faster iteration ### Getting Help 1. Check the `references/` directory for detailed documentation 2. Review sample data and expected outputs 3. Examine the tool source code for customization ideas ## Contributing This skill is designed to be customized for your organization's needs: 1. **Add Detection Rules**: Extend scanner patterns for your tech stack 2. **Custom Prioritization**: Modify scoring algorithms for your business context 3. **New Report Formats**: Add output formats for your stakeholders 4. **Integration Hooks**: Add connectors to your existing tools The codebase is designed with extensibility in mind - each tool is modular and can be enhanced independently. --- **Remember**: Technical debt management is a journey, not a destination. These tools help you make informed decisions about balancing new feature development with technical excellence. Start small, measure impact, and iterate based on what works for your team. FILE:assets/historical_debt_2024-01-15.json { "scan_metadata": { "directory": "/project/src", "scan_date": "2024-01-15T09:00:00", "scanner_version": "1.0.0" }, "summary": { "total_files_scanned": 25, "total_lines_scanned": 12543, "total_debt_items": 28, "health_score": 68.5, "debt_density": 1.12 }, "debt_items": [ { "id": "DEBT-0001", "type": "large_function", "description": "create_user function in user_service.py is 89 lines long", "file_path": "src/user_service.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0002", "type": "duplicate_code", "description": "Password validation logic duplicated in 3 locations", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0003", "type": "security_risk", "description": "Hardcoded API key in payment_processor.py", "file_path": "src/payment_processor.py", "severity": "critical", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0004", "type": "high_complexity", "description": "process_payment function has cyclomatic complexity of 24", "file_path": "src/payment_processor.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0005", "type": "missing_docstring", "description": "PaymentProcessor class missing docstring", "file_path": "src/payment_processor.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0006", "type": "todo_comment", "description": "TODO: Move this to configuration file", "file_path": "src/user_service.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0007", "type": "empty_catch_blocks", "description": "Empty catch block in update_user method", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0008", "type": "magic_numbers", "description": "Magic number 1800 used for lock timeout", "file_path": "src/user_service.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0009", "type": "deep_nesting", "description": "Deep nesting detected: 6 levels in preferences handling", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0010", "type": "long_line", "description": "Line too long: 156 characters", "file_path": "src/frontend.js", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0011", "type": "commented_code", "description": "Dead code left in comments", "file_path": "src/frontend.js", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0012", "type": "global_variables", "description": "Global variable userCache should be encapsulated", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0013", "type": "synchronous_ajax", "description": "Synchronous AJAX call blocks UI thread", "file_path": "src/frontend.js", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0014", "type": "hardcoded_values", "description": "Tax rates hardcoded in payment processing logic", "file_path": "src/payment_processor.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0015", "type": "no_error_handling", "description": "API calls without proper error handling", "file_path": "src/payment_processor.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0016", "type": "inefficient_algorithm", "description": "O(n) user search could be optimized with indexing", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0017", "type": "memory_leak_risk", "description": "Event listeners attached without cleanup", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0018", "type": "sql_injection_risk", "description": "Potential SQL injection in user query", "file_path": "src/database.py", "severity": "critical", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0019", "type": "outdated_dependency", "description": "jQuery version 2.1.4 has known security vulnerabilities", "file_path": "package.json", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0020", "type": "test_debt", "description": "No unit tests for critical payment processing logic", "file_path": "src/payment_processor.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0021", "type": "large_class", "description": "UserService class has 15 methods", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0022", "type": "unused_imports", "description": "Unused import: sys", "file_path": "src/utils.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0023", "type": "missing_type_hints", "description": "Function get_user_score missing type hints", "file_path": "src/user_service.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0024", "type": "circular_dependency", "description": "Circular import between user_service and auth_service", "file_path": "src/user_service.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0025", "type": "inconsistent_naming", "description": "Variable name userID should be user_id", "file_path": "src/auth.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0026", "type": "broad_exception", "description": "Catching generic Exception instead of specific types", "file_path": "src/database.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0027", "type": "deprecated_api", "description": "Using deprecated datetime.utcnow() method", "file_path": "src/utils.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0028", "type": "logging_issue", "description": "Using print() instead of proper logging", "file_path": "src/payment_processor.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" } ] } FILE:assets/historical_debt_2024-02-01.json { "scan_metadata": { "directory": "/project/src", "scan_date": "2024-02-01T14:30:00", "scanner_version": "1.0.0" }, "summary": { "total_files_scanned": 27, "total_lines_scanned": 13421, "total_debt_items": 22, "health_score": 74.2, "debt_density": 0.81 }, "debt_items": [ { "id": "DEBT-0001", "type": "large_function", "description": "create_user function in user_service.py is 89 lines long", "file_path": "src/user_service.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0002", "type": "duplicate_code", "description": "Password validation logic duplicated in 3 locations", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0004", "type": "high_complexity", "description": "process_payment function has cyclomatic complexity of 24", "file_path": "src/payment_processor.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0005", "type": "missing_docstring", "description": "PaymentProcessor class missing docstring", "file_path": "src/payment_processor.py", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0007", "type": "empty_catch_blocks", "description": "Empty catch block in update_user method", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0009", "type": "deep_nesting", "description": "Deep nesting detected: 6 levels in preferences handling", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0010", "type": "long_line", "description": "Line too long: 156 characters", "file_path": "src/frontend.js", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0011", "type": "commented_code", "description": "Dead code left in comments", "file_path": "src/frontend.js", "severity": "low", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0012", "type": "global_variables", "description": "Global variable userCache should be encapsulated", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0013", "type": "synchronous_ajax", "description": "Synchronous AJAX call blocks UI thread", "file_path": "src/frontend.js", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0014", "type": "hardcoded_values", "description": "Tax rates hardcoded in payment processing logic", "file_path": "src/payment_processor.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0015", "type": "no_error_handling", "description": "API calls without proper error handling", "file_path": "src/payment_processor.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0016", "type": "inefficient_algorithm", "description": "O(n) user search could be optimized with indexing", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0017", "type": "memory_leak_risk", "description": "Event listeners attached without cleanup", "file_path": "src/frontend.js", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0021", "type": "large_class", "description": "UserService class has 15 methods", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0024", "type": "circular_dependency", "description": "Circular import between user_service and auth_service", "file_path": "src/user_service.py", "severity": "high", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0026", "type": "broad_exception", "description": "Catching generic Exception instead of specific types", "file_path": "src/database.py", "severity": "medium", "detected_date": "2024-01-15T09:00:00", "status": "identified" }, { "id": "DEBT-0029", "type": "missing_validation", "description": "New API endpoint missing input validation", "file_path": "src/api.py", "severity": "high", "detected_date": "2024-02-01T14:30:00", "status": "identified" }, { "id": "DEBT-0030", "type": "performance_issue", "description": "N+1 query detected in user listing", "file_path": "src/user_service.py", "severity": "medium", "detected_date": "2024-02-01T14:30:00", "status": "identified" }, { "id": "DEBT-0031", "type": "css_debt", "description": "Inline styles should be moved to CSS files", "file_path": "templates/user_profile.html", "severity": "low", "detected_date": "2024-02-01T14:30:00", "status": "identified" }, { "id": "DEBT-0032", "type": "accessibility_issue", "description": "Missing alt text for images", "file_path": "templates/dashboard.html", "severity": "medium", "detected_date": "2024-02-01T14:30:00", "status": "identified" }, { "id": "DEBT-0033", "type": "configuration_debt", "description": "Environment-specific config hardcoded in application", "file_path": "src/config.py", "severity": "medium", "detected_date": "2024-02-01T14:30:00", "status": "identified" } ] } FILE:assets/sample_codebase/src/frontend.js // Frontend JavaScript with various technical debt examples // TODO: Move configuration to separate file const API_BASE_URL = "https://api.example.com"; const API_KEY = "abc123def456"; // FIXME: Should be in environment // Global variables - should be encapsulated var userCache = {}; var authToken = null; var currentUser = null; // HACK: Polyfill for older browsers - should use proper build system if (!String.prototype.includes) { String.prototype.includes = function(search) { return this.indexOf(search) !== -1; }; } class UserInterface { constructor() { this.components = {}; this.eventHandlers = []; // Long parameter list in constructor this.init(document, window, localStorage, sessionStorage, navigator, history, location); } // Function with too many parameters init(doc, win, localStorage, sessionStorage, nav, hist, loc) { this.document = doc; this.window = win; this.localStorage = localStorage; this.sessionStorage = sessionStorage; this.navigator = nav; this.history = hist; this.location = loc; // Deep nesting example if (this.localStorage) { if (this.localStorage.getItem('user')) { if (JSON.parse(this.localStorage.getItem('user'))) { if (JSON.parse(this.localStorage.getItem('user')).preferences) { if (JSON.parse(this.localStorage.getItem('user')).preferences.theme) { if (JSON.parse(this.localStorage.getItem('user')).preferences.theme === 'dark') { document.body.classList.add('dark-theme'); } else if (JSON.parse(this.localStorage.getItem('user')).preferences.theme === 'light') { document.body.classList.add('light-theme'); } else { document.body.classList.add('default-theme'); } } } } } } } // Large function that does too many things renderUserDashboard(userId, includeStats, includeRecent, includeNotifications, includeSettings, includeHelp) { let user = this.getUser(userId); if (!user) { console.log("User not found"); // Should use proper logging return; } let html = '

'; // Inline HTML generation - should use templates html += '

'; html += '

Welcome, ' + user.name + '

'; html += '

'; // Repeated validation pattern if (includeStats && includeStats === true) { html += '

'; html += '

Your Statistics

'; // Magic numbers everywhere if (user.loginCount > 100) { html += '

Frequent User (100+ logins)

'; } else if (user.loginCount > 50) { html += '

Regular User (50+ logins)

'; } else if (user.loginCount > 10) { html += '

Casual User (10+ logins)

'; } else { html += '

New User

'; } html += '

'; } if (includeRecent && includeRecent === true) { html += '

'; html += '

Recent Activity

'; // No error handling for API calls let recentActivity = this.fetchRecentActivity(userId); if (recentActivity && recentActivity.length > 0) { html += '

'; html += '' + activity.type + ''; html += '' + activity.description + ''; html += '' + this.formatTime(activity.timestamp) + ''; html += '

'; } else { html += '

No recent activity

'; } html += '

'; } if (includeNotifications && includeNotifications === true) { html += '

'; html += '

Notifications

'; let notifications = this.getNotifications(userId); // Duplicate HTML generation pattern if (notifications && notifications.length > 0) { html += '

'; html += '' + notification.title + ''; html += '' + notification.message + ''; html += '' + this.formatTime(notification.timestamp) + ''; html += '

'; } else { html += '

No notifications

'; } html += '

'; // Direct DOM manipulation without cleanup document.getElementById('main-content').innerHTML = html; // Event handler attachment without cleanup let buttons = document.querySelectorAll('.action-button'); for (let i = 0; i < buttons.length; i++) { buttons[i].addEventListener('click', function(event) { // Nested event handlers - memory leak risk let buttonType = event.target.getAttribute('data-type'); if (buttonType === 'edit') { // Inline event handling - should be separate methods let modal = document.createElement('div'); modal.className = 'modal'; modal.innerHTML = ''; document.body.appendChild(modal); } else if (buttonType === 'delete') { if (confirm('Are you sure?')) { // Using confirm - poor UX // No error handling fetch(API_BASE_URL + '/users/' + userId, { method: 'DELETE', headers: {'Authorization': 'Bearer ' + authToken} }); } } else if (buttonType === 'share') { // Hardcoded share logic if (navigator.share) { navigator.share({ title: 'Check out my profile', url: window.location.href }); } else { // Fallback for browsers without Web Share API let shareUrl = 'https://twitter.com/intent/tweet?url=' + encodeURIComponent(window.location.href); window.open(shareUrl, '_blank'); } } }); } } // Duplicate code - similar to above but for admin dashboard renderAdminDashboard(adminId) { let admin = this.getUser(adminId); if (!admin) { console.log("Admin not found"); return; } let html = '

'; html += '

Admin Panel - Welcome, ' + admin.name + '

'; html += '

'; // Same pattern repeated html += '

'; html += '

System Statistics

'; let stats = this.getSystemStats(); if (stats) { html += '

'; html += '

Total Users: ' + stats.totalUsers + '

'; html += '

Active Users: ' + stats.activeUsers + '

'; html += '

New Today: ' + stats.newToday + '

'; html += '

'; } html += '

'; html += '

'; document.getElementById('main-content').innerHTML = html; } getUser(userId) { // Check cache first - but cache never expires if (userCache[userId]) { return userCache[userId]; } // Synchronous AJAX - blocks UI let xhr = new XMLHttpRequest(); xhr.open('GET', API_BASE_URL + '/users/' + userId, false); xhr.setRequestHeader('Authorization', 'Bearer ' + authToken); xhr.send(); if (xhr.status === 200) { let user = JSON.parse(xhr.responseText); userCache[userId] = user; return user; } else { // Generic error handling console.error('Failed to fetch user'); return null; } } fetchRecentActivity(userId) { // Another synchronous call try { let xhr = new XMLHttpRequest(); xhr.open('GET', API_BASE_URL + '/users/' + userId + '/activity', false); xhr.setRequestHeader('Authorization', 'Bearer ' + authToken); xhr.send(); if (xhr.status === 200) { return JSON.parse(xhr.responseText); } else { return []; } } catch (error) { // Swallowing errors return []; } } getNotifications(userId) { // Yet another sync call - should be async let xhr = new XMLHttpRequest(); xhr.open('GET', API_BASE_URL + '/users/' + userId + '/notifications', false); xhr.setRequestHeader('Authorization', 'Bearer ' + authToken); xhr.send(); if (xhr.status === 200) { return JSON.parse(xhr.responseText); } else { return []; } } formatTime(timestamp) { // Basic time formatting - should use proper library let date = new Date(timestamp); return date.getMonth() + '/' + date.getDate() + '/' + date.getFullYear(); } // XXX: This method is never used formatCurrency(amount, currency) { if (currency === 'USD') { return '$' + amount.toFixed(2); } else if (currency === 'EUR') { return '€' + amount.toFixed(2); } else { return amount.toFixed(2) + ' ' + currency; } } getSystemStats() { // Hardcoded test data - should come from API return { totalUsers: 12534, activeUsers: 8765, newToday: 23 }; } } // Global functions - should be methods or modules function closeModal() { // Assumes modal exists - no error checking document.querySelector('.modal').remove(); } function validateEmail(email) { // Regex without explanation - magic pattern return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email); } function validatePassword(password) { // Duplicate validation logic from backend if (password.length < 8) return false; if (!/[A-Z]/.test(password)) return false; if (!/[a-z]/.test(password)) return false; if (!/\d/.test(password)) return false; return true; } // jQuery-style utility - reinventing the wheel function $(selector) { return document.querySelector(selector); } function $all(selector) { return document.querySelectorAll(selector); } // Global event handlers - should be encapsulated document.addEventListener('DOMContentLoaded', function() { // Inline anonymous function let ui = new UserInterface(); // Event delegation would be better document.body.addEventListener('click', function(event) { if (event.target.classList.contains('login-button')) { // Inline login logic let username = $('#username').value; let password = $('#password').value; if (!username || !password) { alert('Please enter username and password'); // Poor UX return; } // No CSRF protection fetch(API_BASE_URL + '/auth/login', { method: 'POST', headers: {'Content-Type': 'application/json'}, body: JSON.stringify({username: username, password: password}) }) .then(response => response.json()) .then(data => { if (data.success) { authToken = data.token; currentUser = data.user; localStorage.setItem('authToken', authToken); // Storing sensitive data localStorage.setItem('currentUser', JSON.stringify(currentUser)); window.location.reload(); // Poor navigation } else { alert('Login failed: ' + data.error); } }) .catch(error => { console.error('Login error:', error); alert('Login failed'); }); } }); }); // // Old code left as comments - should be removed // function oldRenderFunction() { // var html = '

Old implementation

'; // document.body.innerHTML = html; // } // Commented out feature - should be removed or implemented // function darkModeToggle() { // if (document.body.classList.contains('dark-theme')) { // document.body.classList.remove('dark-theme'); // document.body.classList.add('light-theme'); // } else { // document.body.classList.remove('light-theme'); // document.body.classList.add('dark-theme'); // } // } FILE:assets/sample_codebase/src/payment_processor.py """ Payment processing module - contains various technical debt examples """ import json import time import requests from decimal import Decimal from typing import Dict, Any class PaymentProcessor: def __init__(self): # TODO: These should come from environment or config self.stripe_key = "sk_test_1234567890" self.paypal_key = "paypal_secret_key_here" self.square_key = "square_api_key" def process_payment(self, amount, currency, payment_method, customer_data, billing_address, shipping_address, items, discount_code, tax_rate, processing_fee, metadata): """ Process a payment - this function is too large and complex """ # Input validation - should be extracted to separate function if not amount or amount <= 0: return {"success": False, "error": "Invalid amount"} if not currency: return {"success": False, "error": "Currency required"} if currency not in ["USD", "EUR", "GBP", "CAD", "AUD"]: # Hardcoded list return {"success": False, "error": "Unsupported currency"} if not payment_method: return {"success": False, "error": "Payment method required"} if not customer_data or "email" not in customer_data: return {"success": False, "error": "Customer email required"} # Tax calculation - complex business logic that should be separate service tax_amount = 0 if tax_rate: if currency == "USD": # US tax logic - hardcoded rules if billing_address and "state" in billing_address: state = billing_address["state"] if state == "CA": tax_amount = amount * 0.08 # California tax elif state == "NY": tax_amount = amount * 0.085 # New York tax elif state == "TX": tax_amount = amount * 0.0625 # Texas tax elif state == "FL": tax_amount = amount * 0.06 # Florida tax else: tax_amount = amount * 0.05 # Default tax elif currency == "EUR": # EU VAT logic - also hardcoded tax_amount = amount * 0.20 # 20% VAT elif currency == "GBP": tax_amount = amount * 0.20 # UK VAT # Discount calculation - another complex block discount_amount = 0 if discount_code: # FIXME: This should query a discount service if discount_code == "SAVE10": discount_amount = amount * 0.10 elif discount_code == "SAVE20": discount_amount = amount * 0.20 elif discount_code == "NEWUSER": discount_amount = min(50, amount * 0.25) # Max $50 discount elif discount_code == "LOYALTY": # Complex loyalty discount logic customer_tier = customer_data.get("tier", "bronze") if customer_tier == "gold": discount_amount = amount * 0.15 elif customer_tier == "silver": discount_amount = amount * 0.10 elif customer_tier == "bronze": discount_amount = amount * 0.05 # Calculate final amount final_amount = amount - discount_amount + tax_amount + processing_fee # Payment method routing - should use strategy pattern if payment_method["type"] == "credit_card": # Credit card processing if payment_method["provider"] == "stripe": try: # Stripe API call - no retry logic response = requests.post( "https://api.stripe.com/v1/charges", headers={"Authorization": f"Bearer {self.stripe_key}"}, data={ "amount": int(final_amount * 100), # Convert to cents "currency": currency.lower(), "source": payment_method["token"], "description": f"Payment for {len(items)} items" } ) if response.status_code == 200: stripe_response = response.json() # Store transaction - should be in database transaction = { "id": stripe_response["id"], "amount": final_amount, "currency": currency, "status": "completed", "timestamp": time.time(), "provider": "stripe", "customer": customer_data["email"], "items": items, "tax_amount": tax_amount, "discount_amount": discount_amount } # Send confirmation email - inline instead of separate service self.send_payment_confirmation_email(customer_data["email"], transaction) return {"success": True, "transaction": transaction} else: return {"success": False, "error": "Stripe payment failed"} except Exception as e: # Broad exception handling - should be more specific print(f"Stripe error: {e}") # Should use proper logging return {"success": False, "error": "Payment processing error"} elif payment_method["provider"] == "square": # Square processing - duplicate code structure try: response = requests.post( "https://connect.squareup.com/v2/payments", headers={"Authorization": f"Bearer {self.square_key}"}, json={ "source_id": payment_method["token"], "amount_money": { "amount": int(final_amount * 100), "currency": currency } } ) if response.status_code == 200: square_response = response.json() transaction = { "id": square_response["payment"]["id"], "amount": final_amount, "currency": currency, "status": "completed", "timestamp": time.time(), "provider": "square", "customer": customer_data["email"], "items": items, "tax_amount": tax_amount, "discount_amount": discount_amount } self.send_payment_confirmation_email(customer_data["email"], transaction) return {"success": True, "transaction": transaction} else: return {"success": False, "error": "Square payment failed"} except Exception as e: print(f"Square error: {e}") return {"success": False, "error": "Payment processing error"} elif payment_method["type"] == "paypal": # PayPal processing - more duplicate code try: response = requests.post( "https://api.paypal.com/v2/checkout/orders", headers={"Authorization": f"Bearer {self.paypal_key}"}, json={ "intent": "CAPTURE", "purchase_units": [{ "amount": { "currency_code": currency, "value": str(final_amount) } }] } ) if response.status_code == 201: paypal_response = response.json() transaction = { "id": paypal_response["id"], "amount": final_amount, "currency": currency, "status": "completed", "timestamp": time.time(), "provider": "paypal", "customer": customer_data["email"], "items": items, "tax_amount": tax_amount, "discount_amount": discount_amount } self.send_payment_confirmation_email(customer_data["email"], transaction) return {"success": True, "transaction": transaction} else: return {"success": False, "error": "PayPal payment failed"} except Exception as e: print(f"PayPal error: {e}") return {"success": False, "error": "Payment processing error"} else: return {"success": False, "error": "Unsupported payment method"} def send_payment_confirmation_email(self, email, transaction): # Email sending logic - should be separate service # HACK: Using print instead of actual email service print(f"Sending confirmation email to {email}") print(f"Transaction ID: {transaction['id']}") print(f"Amount: {transaction['currency']} {transaction['amount']}") # TODO: Implement actual email sending pass def refund_payment(self, transaction_id, amount=None): # Refund logic - incomplete implementation # TODO: Implement refund for different providers print(f"Refunding transaction {transaction_id}") if amount: print(f"Partial refund: {amount}") else: print("Full refund") # XXX: This doesn't actually process the refund return {"success": True, "message": "Refund initiated"} def get_transaction(self, transaction_id): # Should query database, but we don't have one # FIXME: Implement actual transaction lookup return {"id": transaction_id, "status": "unknown"} def validate_credit_card(self, card_number, expiry_month, expiry_year, cvv): # Basic card validation - should use proper validation library if not card_number or len(card_number) < 13 or len(card_number) > 19: return False # Luhn algorithm check - reimplemented poorly digits = [int(d) for d in card_number if d.isdigit()] checksum = 0 for i, digit in enumerate(reversed(digits)): if i % 2 == 1: digit *= 2 if digit > 9: digit -= 9 checksum += digit if checksum % 10 != 0: return False # Expiry validation if expiry_month < 1 or expiry_month > 12: return False current_year = int(time.strftime("%Y")) current_month = int(time.strftime("%m")) if expiry_year < current_year: return False elif expiry_year == current_year and expiry_month < current_month: return False # CVV validation if not cvv or len(cvv) < 3 or len(cvv) > 4: return False return True # Module-level functions that should be in class or separate module def calculate_processing_fee(amount, provider): """Calculate processing fee - hardcoded rates""" if provider == "stripe": return amount * 0.029 + 0.30 # Stripe rates elif provider == "paypal": return amount * 0.031 + 0.30 # PayPal rates elif provider == "square": return amount * 0.026 + 0.10 # Square rates else: return 0 def format_currency(amount, currency): """Format currency - basic implementation""" # Should use proper internationalization if currency == "USD": return f"${amount:.2f}" elif currency == "EUR": return f"€{amount:.2f}" elif currency == "GBP": return f"£{amount:.2f}" else: return f"{currency} {amount:.2f}" # Global state - anti-pattern payment_processor_instance = None def get_payment_processor(): global payment_processor_instance if payment_processor_instance is None: payment_processor_instance = PaymentProcessor() return payment_processor_instance FILE:assets/sample_codebase/src/user_service.py #!/usr/bin/env python3 """ User service module with various tech debt examples """ import hashlib import json import time import re from typing import Dict, List, Any, Optional # TODO: Move this to configuration file DATABASE_URL = "postgresql://user:password123@localhost:5432/mydb" API_KEY = "sk-1234567890abcdef" # FIXME: This should be in environment variables class UserService: def __init__(self): self.users = {} self.cache = {} # HACK: Using dict for now, should be proper database connection self.db_connection = None def create_user(self, name, email, password, age, phone, address, city, state, zip_code, country, preferences, notifications, billing_info): # Function with too many parameters - should use User dataclass if not name: return None if not email: return None if not password: return None if not age: return None if not phone: return None if not address: return None if not city: return None if not state: return None if not zip_code: return None if not country: return None # Duplicate validation logic - should be extracted if age < 13: print("User must be at least 13 years old") return None if age > 150: print("Invalid age") return None # More validation if not self.validate_email(email): print("Invalid email format") return None # Password validation - duplicated elsewhere if len(password) < 8: print("Password too short") return None if not re.search(r"[A-Z]", password): print("Password must contain uppercase letter") return None if not re.search(r"[a-z]", password): print("Password must contain lowercase letter") return None if not re.search(r"\d", password): print("Password must contain digit") return None # Deep nesting example if preferences: if 'notifications' in preferences: if preferences['notifications']: if 'email' in preferences['notifications']: if preferences['notifications']['email']: if 'frequency' in preferences['notifications']['email']: if preferences['notifications']['email']['frequency'] == 'daily': print("Daily email notifications enabled") elif preferences['notifications']['email']['frequency'] == 'weekly': print("Weekly email notifications enabled") else: print("Invalid notification frequency") # TODO: Implement proper user ID generation user_id = str(hash(email)) # XXX: This is terrible for production # Magic numbers everywhere password_hash = hashlib.sha256((password + "salt123").encode()).hexdigest() user_data = { "id": user_id, "name": name, "email": email, "password_hash": password_hash, "age": age, "phone": phone, "address": address, "city": city, "state": state, "zip_code": zip_code, "country": country, "preferences": preferences, "notifications": notifications, "billing_info": billing_info, "created_at": time.time(), "updated_at": time.time(), "last_login": None, "login_count": 0, "is_active": True, "is_verified": False, "verification_token": None, "reset_token": None, "failed_login_attempts": 0, "locked_until": None, "subscription_level": "free", "credits": 100 } self.users[user_id] = user_data return user_id def validate_email(self, email): # Duplicate validation logic - should be in utils if not email: return False if "@" not in email: return False if "." not in email: return False return True def authenticate_user(self, email, password): # More duplicate validation if not email: return None if not password: return None # Linear search through users - O(n) complexity for user_id, user_data in self.users.items(): if user_data["email"] == email: # Same password hashing logic duplicated password_hash = hashlib.sha256((password + "salt123").encode()).hexdigest() if user_data["password_hash"] == password_hash: # Update login stats user_data["last_login"] = time.time() user_data["login_count"] += 1 user_data["failed_login_attempts"] = 0 return user_id else: # Failed login handling user_data["failed_login_attempts"] += 1 if user_data["failed_login_attempts"] >= 5: # Magic number user_data["locked_until"] = time.time() + 1800 # 30 minutes return None return None def get_user(self, user_id): # No error handling return self.users[user_id] def update_user(self, user_id, updates): try: # Empty catch block - bad practice user = self.users[user_id] except: pass # More validation duplication if "age" in updates: if updates["age"] < 13: print("User must be at least 13 years old") return False if updates["age"] > 150: print("Invalid age") return False if "email" in updates: if not self.validate_email(updates["email"]): print("Invalid email format") return False # Direct dictionary manipulation without validation for key, value in updates.items(): user[key] = value user["updated_at"] = time.time() return True def delete_user(self, user_id): # print("Deleting user", user_id) # Commented out code # TODO: Implement soft delete instead del self.users[user_id] def search_users(self, query): results = [] # Inefficient search algorithm - O(n*m) for user_id, user_data in self.users.items(): if query.lower() in user_data["name"].lower(): results.append(user_data) elif query.lower() in user_data["email"].lower(): results.append(user_data) elif query in user_data.get("phone", ""): results.append(user_data) return results def export_users(self): # Security risk - no access control return json.dumps(self.users, indent=2) def import_users(self, json_data): # No validation of imported data imported_users = json.loads(json_data) self.users.update(imported_users) # def old_create_user(self, name, email): # # Old implementation kept as comment # return {"name": name, "email": email} def calculate_user_score(self, user_id): user = self.users[user_id] score = 0 # Complex scoring logic with magic numbers if user["login_count"] > 10: score += 50 elif user["login_count"] > 5: score += 30 elif user["login_count"] > 1: score += 10 if user["subscription_level"] == "premium": score += 100 elif user["subscription_level"] == "pro": score += 75 elif user["subscription_level"] == "basic": score += 25 # Age-based scoring with arbitrary rules if user["age"] >= 18 and user["age"] <= 65: score += 20 elif user["age"] > 65: score += 10 return score # Global variable - should be encapsulated user_service_instance = UserService() def get_user_service(): return user_service_instance # Utility function that should be in separate module def hash_password(password, salt="salt123"): # Hardcoded salt - security issue return hashlib.sha256((password + salt).encode()).hexdigest() # Another utility function with duplicate logic def validate_password(password): if len(password) < 8: return False, "Password too short" if not re.search(r"[A-Z]", password): return False, "Password must contain uppercase letter" if not re.search(r"[a-z]", password): return False, "Password must contain lowercase letter" if not re.search(r"\d", password): return False, "Password must contain digit" return True, "Valid password" FILE:assets/sample_debt_inventory.json [ { "id": "DEBT-0001", "type": "large_function", "description": "create_user function in user_service.py is 89 lines long", "file_path": "src/user_service.py", "line_number": 13, "severity": "high", "metadata": { "function_name": "create_user", "length": 89, "recommended_max": 50 }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0002", "type": "duplicate_code", "description": "Password validation logic duplicated in 3 locations", "file_path": "src/user_service.py", "line_number": 45, "severity": "medium", "metadata": { "duplicate_count": 3, "other_files": ["src/auth.py", "src/frontend.js"] }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0003", "type": "security_risk", "description": "Hardcoded API key in payment_processor.py", "file_path": "src/payment_processor.py", "line_number": 10, "severity": "critical", "metadata": { "security_issue": "hardcoded_credentials", "exposure_risk": "high" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0004", "type": "high_complexity", "description": "process_payment function has cyclomatic complexity of 24", "file_path": "src/payment_processor.py", "line_number": 19, "severity": "high", "metadata": { "function_name": "process_payment", "complexity": 24, "recommended_max": 10 }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0005", "type": "missing_docstring", "description": "PaymentProcessor class missing docstring", "file_path": "src/payment_processor.py", "line_number": 8, "severity": "low", "metadata": { "class_name": "PaymentProcessor" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0006", "type": "todo_comment", "description": "TODO: Move this to configuration file", "file_path": "src/user_service.py", "line_number": 8, "severity": "low", "metadata": { "comment": "TODO: Move this to configuration file" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0007", "type": "empty_catch_blocks", "description": "Empty catch block in update_user method", "file_path": "src/user_service.py", "line_number": 156, "severity": "medium", "metadata": { "method_name": "update_user", "exception_type": "generic" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0008", "type": "magic_numbers", "description": "Magic number 1800 used for lock timeout", "file_path": "src/user_service.py", "line_number": 98, "severity": "low", "metadata": { "value": 1800, "context": "account_lockout_duration" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0009", "type": "deep_nesting", "description": "Deep nesting detected: 6 levels in preferences handling", "file_path": "src/frontend.js", "line_number": 32, "severity": "medium", "metadata": { "nesting_level": 6, "recommended_max": 4 }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0010", "type": "long_line", "description": "Line too long: 156 characters", "file_path": "src/frontend.js", "line_number": 127, "severity": "low", "metadata": { "length": 156, "recommended_max": 120 }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0011", "type": "commented_code", "description": "Dead code left in comments", "file_path": "src/frontend.js", "line_number": 285, "severity": "low", "metadata": { "lines_of_commented_code": 8 }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0012", "type": "global_variables", "description": "Global variable userCache should be encapsulated", "file_path": "src/frontend.js", "line_number": 7, "severity": "medium", "metadata": { "variable_name": "userCache", "scope": "global" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0013", "type": "synchronous_ajax", "description": "Synchronous AJAX call blocks UI thread", "file_path": "src/frontend.js", "line_number": 189, "severity": "high", "metadata": { "method": "XMLHttpRequest", "async": false }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0014", "type": "hardcoded_values", "description": "Tax rates hardcoded in payment processing logic", "file_path": "src/payment_processor.py", "line_number": 45, "severity": "medium", "metadata": { "values": ["0.08", "0.085", "0.0625", "0.06"], "context": "tax_calculation" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0015", "type": "no_error_handling", "description": "API calls without proper error handling", "file_path": "src/payment_processor.py", "line_number": 78, "severity": "high", "metadata": { "api_endpoint": "stripe", "error_scenarios": ["network_failure", "invalid_response"] }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0016", "type": "inefficient_algorithm", "description": "O(n) user search could be optimized with indexing", "file_path": "src/user_service.py", "line_number": 178, "severity": "medium", "metadata": { "current_complexity": "O(n)", "recommended_complexity": "O(log n)", "method_name": "search_users" }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0017", "type": "memory_leak_risk", "description": "Event listeners attached without cleanup", "file_path": "src/frontend.js", "line_number": 145, "severity": "medium", "metadata": { "event_type": "click", "cleanup_missing": true }, "detected_date": "2024-02-10T10:30:00", "status": "identified" }, { "id": "DEBT-0018", "type": "sql_injection_risk", "description": "Potential SQL injection in user query", "file_path": "src/database.py", "line_number": 25, "severity": "critical", "metadata": { "query_type": "dynamic", "user_input": 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"duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140705", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0057", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 29, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140709", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0058", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 139, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140712", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0059", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 87, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140716", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0060", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 88, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140718", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0061", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 90, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140721", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0062", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 91, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140723", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0063", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 122, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140726", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0064", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 123, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140729", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0065", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 190, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140733", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0066", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 191, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140735", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0067", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 251, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140739", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0068", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 252, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140741", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0069", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 255, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140743", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0070", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 256, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140745", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0071", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 28, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140751", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0072", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 29, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140754", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0073", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 31, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140756", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0074", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 32, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140758", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0075", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 34, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140761", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0076", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 35, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140763", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0077", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 37, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140766", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0078", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 38, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140768", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0079", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 83, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140771", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0080", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 84, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140774", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0081", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 102, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140777", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0082", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 145, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140779", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0083", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 114, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140782", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0084", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 156, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140784", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0085", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 115, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140786", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0086", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 157, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140788", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0087", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 116, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140790", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0088", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 158, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140793", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0089", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 117, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140795", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0090", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 159, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140797", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0091", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 119, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140800", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0092", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 120, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140802", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0093", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 121, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140804", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0094", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 162, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140806", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0095", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 122, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140808", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0096", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 163, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140813", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0097", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 161, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140816", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0098", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 203, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140818", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0099", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 213, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140822", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0100", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 214, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140824", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0101", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 223, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140827", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0102", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 224, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140829", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0103", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 235, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140832", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0104", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 236, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140834", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0105", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 265, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140837", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0106", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 266, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140839", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0107", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 306, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140842", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0108", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/payment_processor.py", "severity": "medium", "metadata": { "line_number": 307, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140844", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0109", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 99, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140849", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0110", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 100, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140851", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0111", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 111, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140854", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0112", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 136, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140856", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0113", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 112, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140858", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0114", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 137, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140861", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0115", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 147, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140863", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0116", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 148, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140866", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0117", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 221, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140870", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0118", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 222, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140872", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0119", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 234, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140874", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0120", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 271, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140876", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0121", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 235, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140878", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0122", "type": "duplicate_code", "description": "Duplicate code block found in 2 files", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 272, "duplicate_count": 2, "other_files": [] }, "detected_date": "2026-02-16T12:59:28.140885", "status": "identified", "priority_score": 6, "priority": "medium" }, { "id": "DEBT-0006", "type": "too_many_parameters", "description": "Function 'create_user' has too many parameters: 14", "file_path": "src/user_service.py", "line_number": 24, "severity": "medium", "metadata": { "function_name": "create_user", "parameter_count": 14 }, "detected_date": "2026-02-16T12:59:28.115465", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0025", "type": "empty_catch", "description": "Code smell detected: empty_catch", "file_path": "src/user_service.py", "severity": "medium", "metadata": { "line_number": 170, "pattern": "except:\n pass\n " }, "detected_date": "2026-02-16T12:59:28.120298", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0005", "type": "too_many_parameters", "description": "Function 'process_payment' has too many parameters: 12", "file_path": "src/payment_processor.py", "line_number": 20, "severity": "medium", "metadata": { "function_name": "process_payment", "parameter_count": 12 }, "detected_date": "2026-02-16T12:59:28.125130", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0050", "type": "hardcoded_paths", "description": "Code smell detected: hardcoded_paths", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 166, "pattern": "'/users/'" }, "detected_date": "2026-02-16T12:59:28.139558", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0051", "type": "hardcoded_paths", "description": "Code smell detected: hardcoded_paths", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 233, "pattern": "'/users/'" }, "detected_date": "2026-02-16T12:59:28.139584", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0052", "type": "hardcoded_paths", "description": "Code smell detected: hardcoded_paths", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 252, "pattern": "'/users/'" }, "detected_date": "2026-02-16T12:59:28.139595", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0053", "type": "hardcoded_paths", "description": "Code smell detected: hardcoded_paths", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 270, "pattern": "'/users/'" }, "detected_date": "2026-02-16T12:59:28.139606", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0054", "type": "hardcoded_paths", "description": "Code smell detected: hardcoded_paths", "file_path": "src/frontend.js", "severity": "medium", "metadata": { "line_number": 355, "pattern": "'/auth/login'" }, "detected_date": "2026-02-16T12:59:28.139636", "status": "identified", "priority_score": 5, "priority": "medium" }, { "id": "DEBT-0001", "type": "missing_docstring", "description": "Class 'UserService' missing docstring", "file_path": "src/user_service.py", "line_number": 17, "severity": "low", "metadata": { "class_name": "UserService" }, "detected_date": "2026-02-16T12:59:28.114513", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0002", "type": "missing_docstring", "description": "Function '__init__' missing docstring", "file_path": "src/user_service.py", "line_number": 18, "severity": "low", "metadata": { "function_name": "__init__" }, "detected_date": "2026-02-16T12:59:28.114546", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0004", "type": "missing_docstring", "description": "Function 'create_user' missing docstring", "file_path": "src/user_service.py", "line_number": 24, "severity": "low", "metadata": { "function_name": "create_user" }, "detected_date": "2026-02-16T12:59:28.114684", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0007", "type": "missing_docstring", "description": "Function 'validate_email' missing docstring", "file_path": "src/user_service.py", "line_number": 126, "severity": "low", "metadata": { "function_name": "validate_email" }, "detected_date": "2026-02-16T12:59:28.116045", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0008", "type": "missing_docstring", "description": "Function 'authenticate_user' missing docstring", "file_path": "src/user_service.py", "line_number": 136, "severity": "low", "metadata": { "function_name": "authenticate_user" }, "detected_date": "2026-02-16T12:59:28.116159", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0009", "type": "missing_docstring", "description": "Function 'get_user' missing docstring", "file_path": "src/user_service.py", "line_number": 162, "severity": "low", "metadata": { "function_name": "get_user" }, "detected_date": "2026-02-16T12:59:28.116637", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0010", "type": "missing_docstring", "description": "Function 'update_user' missing docstring", "file_path": "src/user_service.py", "line_number": 166, "severity": "low", "metadata": { "function_name": "update_user" }, "detected_date": "2026-02-16T12:59:28.116694", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0011", "type": "missing_docstring", "description": "Function 'delete_user' missing docstring", "file_path": "src/user_service.py", "line_number": 194, "severity": "low", "metadata": { "function_name": "delete_user" }, "detected_date": "2026-02-16T12:59:28.117074", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0012", "type": "missing_docstring", "description": "Function 'search_users' missing docstring", "file_path": "src/user_service.py", "line_number": 199, "severity": "low", "metadata": { "function_name": "search_users" }, "detected_date": "2026-02-16T12:59:28.117131", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0013", "type": "missing_docstring", "description": "Function 'export_users' missing docstring", "file_path": "src/user_service.py", "line_number": 211, "severity": "low", "metadata": { "function_name": "export_users" }, "detected_date": "2026-02-16T12:59:28.117460", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0014", "type": "missing_docstring", "description": "Function 'import_users' missing docstring", "file_path": "src/user_service.py", "line_number": 215, "severity": "low", "metadata": { "function_name": "import_users" }, "detected_date": "2026-02-16T12:59:28.117523", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0015", "type": "missing_docstring", "description": "Function 'calculate_user_score' missing docstring", "file_path": "src/user_service.py", "line_number": 224, "severity": "low", "metadata": { "function_name": "calculate_user_score" }, "detected_date": "2026-02-16T12:59:28.117609", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0016", "type": "missing_docstring", "description": "Function 'get_user_service' missing docstring", "file_path": "src/user_service.py", "line_number": 256, "severity": "low", "metadata": { "function_name": "get_user_service" }, "detected_date": "2026-02-16T12:59:28.118051", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0017", "type": "missing_docstring", "description": "Function 'hash_password' missing docstring", "file_path": "src/user_service.py", "line_number": 261, "severity": "low", "metadata": { "function_name": "hash_password" }, "detected_date": "2026-02-16T12:59:28.118083", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0018", "type": "missing_docstring", "description": "Function 'validate_password' missing docstring", "file_path": "src/user_service.py", "line_number": 267, "severity": "low", "metadata": { "function_name": "validate_password" }, "detected_date": "2026-02-16T12:59:28.118172", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0001", "type": "missing_docstring", "description": "Class 'PaymentProcessor' missing docstring", "file_path": "src/payment_processor.py", "line_number": 12, "severity": "low", "metadata": { "class_name": "PaymentProcessor" }, "detected_date": "2026-02-16T12:59:28.124344", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0002", "type": "missing_docstring", "description": "Function '__init__' missing docstring", "file_path": "src/payment_processor.py", "line_number": 14, "severity": "low", "metadata": { "function_name": "__init__" }, "detected_date": "2026-02-16T12:59:28.124356", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0006", "type": "missing_docstring", "description": "Function 'send_payment_confirmation_email' missing docstring", "file_path": "src/payment_processor.py", "line_number": 217, "severity": "low", "metadata": { "function_name": "send_payment_confirmation_email" }, "detected_date": "2026-02-16T12:59:28.125733", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0007", "type": "missing_docstring", "description": "Function 'refund_payment' missing docstring", "file_path": "src/payment_processor.py", "line_number": 227, "severity": "low", "metadata": { "function_name": "refund_payment" }, "detected_date": "2026-02-16T12:59:28.125816", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0008", "type": "missing_docstring", "description": "Function 'get_transaction' missing docstring", "file_path": "src/payment_processor.py", "line_number": 239, "severity": "low", "metadata": { "function_name": "get_transaction" }, "detected_date": "2026-02-16T12:59:28.125889", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0009", "type": "missing_docstring", "description": "Function 'validate_credit_card' missing docstring", "file_path": "src/payment_processor.py", "line_number": 244, "severity": "low", "metadata": { "function_name": "validate_credit_card" }, "detected_date": "2026-02-16T12:59:28.125917", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0011", "type": "missing_docstring", "description": "Function 'get_payment_processor' missing docstring", "file_path": "src/payment_processor.py", "line_number": 311, "severity": "low", "metadata": { "function_name": "get_payment_processor" }, "detected_date": "2026-02-16T12:59:28.126436", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0044", "type": "long_line", "description": "Line too long: 140 characters", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 161, "length": 140 }, "detected_date": "2026-02-16T12:59:28.128066", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0045", "type": "long_line", "description": "Line too long: 122 characters", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 180, "length": 122 }, "detected_date": "2026-02-16T12:59:28.128072", "status": "identified", "priority_score": 2, "priority": "low" }, { "id": "DEBT-0019", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Move this to configuration file", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 12, "comment": "TODO: Move this to configuration file" }, "detected_date": "2026-02-16T12:59:28.118649", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0020", "type": "todo_comment", "description": "TODO/FIXME comment: FIXME: This should be in environment variables", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 14, "comment": "FIXME: This should be in environment variables" }, "detected_date": "2026-02-16T12:59:28.118681", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0021", "type": "todo_comment", "description": "TODO/FIXME comment: HACK: Using dict for now, should be proper database connection", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 21, "comment": "HACK: Using dict for now, should be proper database connection" }, "detected_date": "2026-02-16T12:59:28.118720", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0022", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Implement proper user ID generation", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 88, "comment": "TODO: Implement proper user ID generation" }, "detected_date": "2026-02-16T12:59:28.119140", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0023", "type": "todo_comment", "description": "TODO/FIXME comment: XXX: This is terrible for production", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 89, "comment": "XXX: This is terrible for production" }, "detected_date": "2026-02-16T12:59:28.119154", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0024", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Implement soft delete instead", "file_path": "src/user_service.py", "severity": "low", "metadata": { "line_number": 196, "comment": "TODO: Implement soft delete instead" }, "detected_date": "2026-02-16T12:59:28.119807", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0037", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: These should come from environment or config", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 15, "comment": "TODO: These should come from environment or config" }, "detected_date": "2026-02-16T12:59:28.126594", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0038", "type": "todo_comment", "description": "TODO/FIXME comment: FIXME: This should query a discount service", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 67, "comment": "FIXME: This should query a discount service" }, "detected_date": "2026-02-16T12:59:28.126782", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0039", "type": "todo_comment", "description": "TODO/FIXME comment: HACK: Using print instead of actual email service", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 219, "comment": "HACK: Using print instead of actual email service" }, "detected_date": "2026-02-16T12:59:28.127356", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0040", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Implement actual email sending", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 224, "comment": "TODO: Implement actual email sending" }, "detected_date": "2026-02-16T12:59:28.127385", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0041", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Implement refund for different providers", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 229, "comment": "TODO: Implement refund for different providers" }, "detected_date": "2026-02-16T12:59:28.127402", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0042", "type": "todo_comment", "description": "TODO/FIXME comment: XXX: This doesn't actually process the refund", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 236, "comment": "XXX: This doesn't actually process the refund" }, "detected_date": "2026-02-16T12:59:28.127434", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0043", "type": "todo_comment", "description": "TODO/FIXME comment: FIXME: Implement actual transaction lookup", "file_path": "src/payment_processor.py", "severity": "low", "metadata": { "line_number": 241, "comment": "FIXME: Implement actual transaction lookup" }, "detected_date": "2026-02-16T12:59:28.127455", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0046", "type": "todo_comment", "description": "TODO/FIXME comment: TODO: Move configuration to separate file", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 3, "comment": "TODO: Move configuration to separate file" }, "detected_date": "2026-02-16T12:59:28.138142", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0047", "type": "todo_comment", "description": "TODO/FIXME comment: FIXME: Should be in environment", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 5, "comment": "FIXME: Should be in environment" }, "detected_date": "2026-02-16T12:59:28.138158", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0048", "type": "todo_comment", "description": "TODO/FIXME comment: HACK: Polyfill for older browsers - should use proper build system", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 12, "comment": "HACK: Polyfill for older browsers - should use proper build system" }, "detected_date": "2026-02-16T12:59:28.138174", "status": "identified", "priority_score": 1, "priority": "low" }, { "id": "DEBT-0049", "type": "todo_comment", "description": "TODO/FIXME comment: XXX: This method is never used", "file_path": "src/frontend.js", "severity": "low", "metadata": { "line_number": 287, "comment": "XXX: This method is never used" }, "detected_date": "2026-02-16T12:59:28.139089", "status": "identified", "priority_score": 1, "priority": "low" } ], "file_statistics": { "src/user_service.py": { "path": "src/user_service.py", "lines": 276, "size_kb": 9.275390625, "language": "python", "debt_count": 16 }, "src/payment_processor.py": { "path": "src/payment_processor.py", "lines": 315, "size_kb": 13.041015625, "language": "python", "debt_count": 38 }, "src/frontend.js": { "path": "src/frontend.js", "lines": 395, "size_kb": 14.5419921875, "language": "javascript", "debt_count": 14 } }, "recommendations": [ "Extract duplicate code into reusable functions or modules. This reduces maintenance burden and potential for inconsistent changes.", "High debt density detected. Consider establishing coding standards and regular code review processes to prevent debt accumulation." ] } FILE:references/debt-classification-taxonomy.md # Technical Debt Classification Taxonomy ## Overview This document provides a comprehensive taxonomy for classifying technical debt across different dimensions. Consistent classification is essential for tracking, prioritizing, and managing technical debt effectively across teams and projects. ## Primary Categories ### 1. Code Debt **Definition**: Issues at the code level that make software harder to understand, modify, or maintain. **Subcategories**: - **Structural Issues** - `large_function`: Functions exceeding recommended size limits - `high_complexity`: High cyclomatic complexity (>10) - `deep_nesting`: Excessive indentation levels (>4) - `long_parameter_list`: Too many function parameters (>5) - `data_clumps`: Related data that should be grouped together - **Naming and Documentation** - `poor_naming`: Unclear or misleading variable/function names - `missing_docstring`: Functions/classes without documentation - `magic_numbers`: Hardcoded numeric values without explanation - `commented_code`: Dead code left in comments - **Duplication and Patterns** - `duplicate_code`: Identical or similar code blocks - `copy_paste_programming`: Evidence of code duplication - `inconsistent_patterns`: Mixed coding styles within codebase - **Error Handling** - `empty_catch_blocks`: Exception handling without proper action - `generic_exceptions`: Catching overly broad exception types - `missing_error_handling`: No error handling for failure scenarios **Severity Indicators**: - **Critical**: Security vulnerabilities, syntax errors - **High**: Functions >100 lines, complexity >20 - **Medium**: Functions 50-100 lines, complexity 10-20 - **Low**: Minor style issues, short functions with minor problems ### 2. Architecture Debt **Definition**: High-level design decisions that limit system flexibility, scalability, or maintainability. **Subcategories**: - **Structural Issues** - `monolithic_design`: Components that should be separated - `circular_dependencies`: Modules depending on each other cyclically - `god_object`: Classes/modules with too many responsibilities - `inappropriate_intimacy`: Excessive coupling between modules - **Layer Violations** - `abstraction_inversion`: Lower-level modules depending on higher-level ones - `leaky_abstractions`: Implementation details exposed through interfaces - `broken_hierarchy`: Inheritance relationships that don't make sense - **Scalability Issues** - `performance_bottlenecks`: Known architectural performance limitations - `resource_contention`: Shared resources creating bottlenecks - `single_point_failure`: Critical components without redundancy **Impact Assessment**: - **High Impact**: Affects system scalability, blocks major features - **Medium Impact**: Makes changes more difficult, affects team productivity - **Low Impact**: Minor architectural inconsistencies ### 3. Test Debt **Definition**: Inadequate testing infrastructure, coverage, or quality that increases risk and slows development. **Subcategories**: - **Coverage Issues** - `low_coverage`: Test coverage below team standards (<80%) - `missing_unit_tests`: No tests for critical business logic - `missing_integration_tests`: No tests for component interactions - `missing_end_to_end_tests`: No full system workflow validation - **Test Quality** - `flaky_tests`: Tests that pass/fail inconsistently - `slow_tests`: Test suite taking too long to execute - `brittle_tests`: Tests that break with minor code changes - `unclear_test_intent`: Tests without clear purpose or documentation - **Infrastructure** - `manual_testing_only`: No automated testing processes - `missing_test_data`: No proper test data management - `environment_dependencies`: Tests requiring specific environments **Priority Matrix**: - **Critical Path Coverage**: High priority for business-critical features - **Regression Risk**: High priority for frequently changed code - **Development Velocity**: Medium priority for developer productivity - **Documentation Value**: Low priority for test clarity improvements ### 4. Documentation Debt **Definition**: Missing, outdated, or poor-quality documentation that hinders understanding and maintenance. **Subcategories**: - **API Documentation** - `missing_api_docs`: No documentation for public APIs - `outdated_api_docs`: Documentation doesn't match implementation - `incomplete_examples`: No usage examples for complex APIs - **Code Documentation** - `missing_comments`: Complex algorithms without explanation - `outdated_comments`: Comments contradicting current implementation - `redundant_comments`: Comments that just restate the code - **System Documentation** - `missing_architecture_docs`: No high-level system design documentation - `missing_deployment_docs`: No deployment or operations guide - `missing_onboarding_docs`: No guide for new team members **Freshness Assessment**: - **Stale**: Documentation >6 months out of date - **Outdated**: Documentation 3-6 months out of date - **Current**: Documentation <3 months out of date ### 5. Dependency Debt **Definition**: Issues with external libraries, frameworks, and system dependencies. **Subcategories**: - **Version Management** - `outdated_dependencies`: Libraries with available updates - `vulnerable_dependencies`: Dependencies with known security issues - `deprecated_dependencies`: Dependencies no longer maintained - `version_conflicts`: Incompatible dependency versions - **License and Compliance** - `license_violations`: Dependencies with incompatible licenses - `license_unknown`: Dependencies without clear licensing - `compliance_risk`: Dependencies creating legal/regulatory risks - **Usage Optimization** - `unused_dependencies`: Dependencies included but not used - `oversized_dependencies`: Heavy libraries for simple functionality - `redundant_dependencies`: Multiple libraries solving same problem **Risk Assessment**: - **Security Risk**: Known vulnerabilities, unmaintained dependencies - **Legal Risk**: License conflicts, compliance issues - **Technical Risk**: Breaking changes, deprecation notices - **Maintenance Risk**: Outdated versions, unsupported libraries ### 6. Infrastructure Debt **Definition**: Operations, deployment, and infrastructure-related technical debt. **Subcategories**: - **Deployment and CI/CD** - `manual_deployment`: No automated deployment processes - `missing_pipeline`: No CI/CD pipeline automation - `brittle_deployments`: Deployment process prone to failure - `environment_drift`: Inconsistencies between environments - **Monitoring and Observability** - `missing_monitoring`: No application/system monitoring - `inadequate_logging`: Insufficient logging for troubleshooting - `missing_alerting`: No alerts for critical system conditions - `poor_observability`: Can't understand system behavior in production - **Configuration Management** - `hardcoded_config`: Configuration embedded in code - `manual_configuration`: No automated configuration management - `secrets_in_code`: Sensitive information stored in code - `inconsistent_environments`: Dev/staging/prod differences **Operational Impact**: - **Availability**: Affects system uptime and reliability - **Debuggability**: Affects ability to troubleshoot issues - **Scalability**: Affects ability to handle load increases - **Security**: Affects system security posture ## Severity Classification ### Critical (Score: 9-10) - Security vulnerabilities - Production-breaking issues - Legal/compliance violations - Blocking issues for team productivity ### High (Score: 7-8) - Significant technical risk - Major productivity impact - Customer-visible quality issues - Architecture limitations ### Medium (Score: 4-6) - Moderate productivity impact - Code quality concerns - Maintenance difficulties - Minor security concerns ### Low (Score: 1-3) - Style and convention issues - Documentation gaps - Minor optimizations - Cosmetic improvements ## Impact Dimensions ### Business Impact - **Customer Experience**: User-facing quality and performance - **Revenue**: Direct impact on business metrics - **Compliance**: Regulatory and legal requirements - **Market Position**: Competitive advantage considerations ### Technical Impact - **Development Velocity**: Speed of feature development - **Code Quality**: Maintainability and reliability - **System Reliability**: Uptime and performance - **Security Posture**: Vulnerability and risk exposure ### Team Impact - **Developer Productivity**: Individual efficiency - **Team Morale**: Job satisfaction and engagement - **Knowledge Sharing**: Team collaboration and learning - **Onboarding Speed**: New team member integration ## Effort Estimation Guidelines ### T-Shirt Sizing - **XS (1-4 hours)**: Simple fixes, documentation updates - **S (1-2 days)**: Minor refactoring, simple feature additions - **M (3-5 days)**: Moderate refactoring, component changes - **L (1-2 weeks)**: Major refactoring, architectural changes - **XL (3+ weeks)**: System-wide changes, major migrations ### Complexity Factors - **Technical Complexity**: How difficult is the change technically? - **Business Risk**: What's the risk if something goes wrong? - **Testing Requirements**: How much testing is needed? - **Team Knowledge**: Does the team understand this area well? - **Dependencies**: How many other systems/teams are involved? ## Usage Guidelines ### When Classifying Debt 1. Start with primary category (code, architecture, test, etc.) 2. Identify specific subcategory for precise tracking 3. Assess severity based on business and technical impact 4. Estimate effort using t-shirt sizing 5. Tag with relevant impact dimensions ### Consistency Rules - Use consistent terminology across teams - Document custom categories for domain-specific debt - Regular reviews to ensure classification accuracy - Training for team members on taxonomy usage ### Review and Updates - Quarterly review of taxonomy relevance - Add new categories as patterns emerge - Remove unused categories to keep taxonomy lean - Update severity and impact criteria based on experience This taxonomy should be adapted to your organization's specific context, technology stack, and business priorities. The key is consistency in application across teams and over time. FILE:references/debt-frameworks.md # tech-debt-tracker reference ## Technical Debt Classification Framework ### 1. Code Debt Code-level issues that make the codebase harder to understand, modify, and maintain. **Indicators:** - Long functions (>50 lines for complex logic, >20 for simple operations) - Deep nesting (>4 levels of indentation) - High cyclomatic complexity (>10) - Duplicate code patterns (>3 similar blocks) - Missing or inadequate error handling - Poor variable/function naming - Magic numbers and hardcoded values - Commented-out code blocks **Impact:** - Increased debugging time - Higher defect rates - Slower feature development - Knowledge silos (only original author understands the code) **Detection Methods:** - AST parsing for structural analysis - Pattern matching for common anti-patterns - Complexity metrics calculation - Duplicate code detection algorithms ### 2. Architecture Debt High-level design decisions that seemed reasonable at the time but now limit scalability or maintainability. **Indicators:** - Monolithic components that should be modular - Circular dependencies between modules - Violation of separation of concerns - Inconsistent data flow patterns - Over-engineering or under-engineering for current scale - Tightly coupled components - Missing abstraction layers **Impact:** - Difficult to scale individual components - Cascading changes required for simple modifications - Testing becomes complex and brittle - Onboarding new team members takes longer **Detection Methods:** - Dependency analysis - Module coupling metrics - Component size analysis - Interface consistency checks ### 3. Test Debt Inadequate or missing test coverage, poor test quality, and testing infrastructure issues. **Indicators:** - Low test coverage (<80% for critical paths) - Missing unit tests for complex logic - No integration tests for key workflows - Flaky tests that pass/fail intermittently - Slow test execution (>10 minutes for unit tests) - Tests that don't test meaningful behavior - Missing test data management strategy **Impact:** - Fear of refactoring ("don't touch it, it works") - Regression bugs in production - Slow feedback cycles during development - Difficulty validating complex business logic **Detection Methods:** - Coverage report analysis - Test execution time monitoring - Test failure pattern analysis - Test code quality assessment ### 4. Documentation Debt Missing, outdated, or poor-quality documentation that makes the system harder to understand and maintain. **Indicators:** - Missing API documentation - Outdated README files - No architectural decision records (ADRs) - Missing code comments for complex algorithms - No onboarding documentation for new team members - Inconsistent documentation formats - Documentation that contradicts actual implementation **Impact:** - Increased onboarding time for new team members - Knowledge loss when team members leave - Miscommunication between teams - Repeated questions in team channels **Detection Methods:** - Documentation coverage analysis - Freshness checking (last modified dates) - Link validation - Comment density analysis ### 5. Dependency Debt Issues related to external libraries, frameworks, and system dependencies. **Indicators:** - Outdated packages with known security vulnerabilities - Dependencies with incompatible licenses - Unused dependencies bloating the build - Version conflicts between packages - Deprecated APIs still in use - Heavy dependencies for simple tasks - Missing dependency pinning **Impact:** - Security vulnerabilities - Build instability - Longer build times - Legal compliance issues - Difficulty upgrading core frameworks **Detection Methods:** - Vulnerability scanning - License compliance checking - Usage analysis - Version compatibility checking ### 6. Infrastructure Debt Operations and deployment-related technical debt. **Indicators:** - Manual deployment processes - Missing monitoring and alerting - Inadequate logging - No disaster recovery plan - Inconsistent environments (dev/staging/prod) - Missing CI/CD pipelines - Infrastructure as code gaps **Impact:** - Deployment risks and downtime - Difficult troubleshooting - Inconsistent behavior across environments - Manual work that should be automated **Detection Methods:** - Infrastructure audit checklists - Configuration drift detection - Monitoring coverage analysis - Deployment process documentation review ## Severity Scoring Framework Each piece of tech debt is scored on multiple dimensions to determine overall severity: ### Impact Assessment (1-10 scale) **Development Velocity Impact** - 1-2: Negligible impact on development speed - 3-4: Minor slowdown, workarounds available - 5-6: Moderate impact, affects some features - 7-8: Significant slowdown, affects most work - 9-10: Critical blocker, prevents new development **Quality Impact** - 1-2: No impact on defect rates - 3-4: Minor increase in minor bugs - 5-6: Moderate increase in defects - 7-8: Regular production issues - 9-10: Critical reliability problems **Team Productivity Impact** - 1-2: No impact on team morale or efficiency - 3-4: Occasional frustration - 5-6: Regular complaints from developers - 7-8: Team actively avoiding the area - 9-10: Causing developer turnover **Business Impact** - 1-2: No customer-facing impact - 3-4: Minor UX degradation - 5-6: Moderate performance impact - 7-8: Customer complaints or churn - 9-10: Revenue-impacting issues ### Effort Assessment **Size (Story Points or Hours)** - XS (1-4 hours): Simple refactor or documentation update - S (1-2 days): Minor architectural change - M (3-5 days): Moderate refactoring effort - L (1-2 weeks): Major component restructuring - XL (3+ weeks): System-wide architectural changes **Risk Level** - Low: Well-understood change with clear scope - Medium: Some unknowns but manageable - High: Significant unknowns, potential for scope creep **Skill Requirements** - Junior: Can be handled by any team member - Mid: Requires experienced developer - Senior: Needs architectural expertise - Expert: Requires deep system knowledge ## Interest Rate Calculation Technical debt accrues "interest" - the additional cost of leaving it unfixed. This interest rate helps prioritize which debt to pay down first. ### Interest Rate Formula ``` Interest Rate = (Impact Score × Frequency of Encounter) / Time Period ``` Where: - **Impact Score**: Average severity score (1-10) - **Frequency of Encounter**: How often developers interact with this code - **Time Period**: Usually measured per sprint or month ### Cost of Delay Calculation ``` Cost of Delay = Interest Rate × Time Until Fix × Team Size Multiplier ``` ### Example Calculation **Scenario**: Legacy authentication module with poor error handling - Impact Score: 7 (causes regular production issues) - Frequency: 15 encounters per sprint (3 developers × 5 times each) - Team Size: 8 developers - Current sprint: 1, planned fix: sprint 4 ``` Interest Rate = 7 × 15 = 105 points per sprint Cost of Delay = 105 × 3 × 1.2 = 378 total cost points ``` This debt item should be prioritized over lower-cost items. ## Debt Inventory Management ### Data Structure Each debt item is tracked with the following attributes: ```json { "id": "DEBT-2024-001", "title": "Legacy user authentication module", "category": "code", "subcategory": "error_handling", "location": "src/auth/legacy_auth.py:45-120", "description": "Authentication error handling uses generic exceptions", "impact": { "velocity": 7, "quality": 8, "productivity": 6, "business": 5 }, "effort": { "size": "M", "risk": "medium", "skill_required": "mid" }, "interest_rate": 105, "cost_of_delay": 378, "priority": "high", "created_date": "2024-01-15", "last_updated": "2024-01-20", "assigned_to": null, "status": "identified", "tags": ["security", "user-experience", "maintainability"] } ``` ### Status Lifecycle 1. **Identified** - Debt detected but not yet analyzed 2. **Analyzed** - Impact and effort assessed 3. **Prioritized** - Added to backlog with priority 4. **Planned** - Assigned to specific sprint/release 5. **In Progress** - Actively being worked on 6. **Review** - Implementation complete, under review 7. **Done** - Debt resolved and verified 8. **Won't Fix** - Consciously decided not to address ## Prioritization Frameworks ### 1. Cost-of-Delay vs Effort Matrix Plot debt items on a 2D matrix: - X-axis: Effort (XS to XL) - Y-axis: Cost of Delay (calculated value) **Priority Quadrants:** - High Cost, Low Effort: **Immediate** (quick wins) - High Cost, High Effort: **Planned** (major initiatives) - Low Cost, Low Effort: **Opportunistic** (during related work) - Low Cost, High Effort: **Backlog** (consider for future) ### 2. Weighted Shortest Job First (WSJF) ``` WSJF Score = (Business Value + Time Criticality + Risk Reduction) / Effort ``` Where each component is scored 1-10: - **Business Value**: Direct impact on customer value - **Time Criticality**: How much value decreases over time - **Risk Reduction**: How much risk is mitigated by fixing this debt ### 3. Technical Debt Quadrant Based on Martin Fowler's framework: **Quadrant 1: Reckless & Deliberate** - "We don't have time for design" - Highest priority for remediation **Quadrant 2: Prudent & Deliberate** - "We must ship now and deal with consequences" - Schedule for near-term resolution **Quadrant 3: Reckless & Inadvertent** - "What's layering?" - Focus on education and process improvement **Quadrant 4: Prudent & Inadvertent** - "Now we know how we should have done it" - Normal part of learning, lowest priority ## Refactoring Strategies ### 1. Strangler Fig Pattern Gradually replace old system by building new functionality around it. **When to use:** - Large, monolithic systems - High-risk changes to critical paths - Long-term architectural migrations **Implementation:** 1. Identify boundaries for extraction 2. Create abstraction layer 3. Route new features to new implementation 4. Gradually migrate existing features 5. Remove old implementation ### 2. Branch by Abstraction Create abstraction layer to allow parallel implementations. **When to use:** - Need to support old and new systems simultaneously - High-risk changes with rollback requirements - A/B testing infrastructure changes **Implementation:** 1. Create abstraction interface 2. Implement abstraction for current system 3. Replace direct calls with abstraction calls 4. Implement new version behind same abstraction 5. Switch implementations via configuration 6. Remove old implementation ### 3. Feature Toggles Use configuration flags to control code execution. **When to use:** - Gradual rollout of refactored components - Risk mitigation during large changes - Experimental refactoring approaches **Implementation:** 1. Identify decision points in code 2. Add toggle checks at decision points 3. Implement both old and new paths 4. Test both paths thoroughly 5. Gradually move toggle to new implementation 6. Remove old path and toggle ### 4. Parallel Run Run old and new implementations simultaneously to verify correctness. **When to use:** - Critical business logic changes - Data processing pipeline changes - Algorithm improvements **Implementation:** 1. Implement new version alongside old 2. Run both versions with same inputs 3. Compare outputs and log discrepancies 4. Investigate and fix discrepancies 5. Build confidence through parallel execution 6. Switch to new implementation 7. Remove old implementation ## Sprint Allocation Recommendations ### Debt-to-Feature Ratio Maintain healthy balance between new features and debt reduction: **Team Velocity < 70% of capacity:** - 60% tech debt, 40% features - Focus on removing major blockers **Team Velocity 70-85% of capacity:** - 30% tech debt, 70% features - Balanced maintenance approach **Team Velocity > 85% of capacity:** - 15% tech debt, 85% features - Opportunistic debt reduction only ### Sprint Planning Integration **Story Point Allocation:** - Reserve 20% of sprint capacity for tech debt - Prioritize debt items with highest interest rates - Include "debt tax" in feature estimates when working in high-debt areas **Debt Budget Tracking:** - Track debt points completed per sprint - Monitor debt interest rate trend - Alert when debt accumulation exceeds team's paydown rate ### Quarterly Planning **Debt Initiatives:** - Identify 1-2 major debt themes per quarter - Allocate dedicated sprints for large-scale refactoring - Plan debt work around major feature releases **Success Metrics:** - Debt interest rate reduction - Developer velocity improvements - Defect rate reduction - Code review cycle time improvement ## Stakeholder Reporting ### Executive Dashboard **Key Metrics:** - Overall tech debt health score (0-100) - Debt trend direction (improving/declining) - Cost of delayed fixes (in development days) - High-risk debt items count **Monthly Report Structure:** 1. **Executive Summary** (3 bullet points) 2. **Health Score Trend** (6-month view) 3. **Top 3 Risk Items** (business impact focus) 4. **Investment Recommendation** (resource allocation) 5. **Success Stories** (debt reduced last month) ### Engineering Team Dashboard **Daily Metrics:** - New debt items identified - Debt items resolved - Interest rate by team/component - Debt hotspots (most problematic areas) **Sprint Reviews:** - Debt points completed vs. planned - Velocity impact from debt work - Newly discovered debt during feature work - Team sentiment on code quality ### Product Manager Reports **Feature Impact Analysis:** - How debt affects feature development time - Quality risk assessment for upcoming features - Debt that blocks planned features - Recommendations for feature sequence planning **Customer Impact Translation:** - Debt that affects performance - Debt that increases bug rates - Debt that limits feature flexibility - Investment required to maintain current quality FILE:references/prioritization-framework.md # Technical Debt Prioritization Framework ## Introduction Technical debt prioritization is a critical capability that separates high-performing engineering teams from those struggling with maintenance burden. This framework provides multiple approaches to systematically prioritize technical debt based on business value, risk, effort, and strategic alignment. ## Core Principles ### 1. Business Value Alignment Technical debt work must connect to business outcomes. Every debt item should have a clear story about how fixing it supports business goals. ### 2. Evidence-Based Decisions Use data, not opinions, to drive prioritization. Measure impact, track trends, and validate assumptions with evidence. ### 3. Cost-Benefit Optimization Balance the cost of fixing debt against the cost of leaving it unfixed. Sometimes living with debt is the right business decision. ### 4. Risk Management Consider both the probability and impact of negative outcomes. High-probability, high-impact issues get priority. ### 5. Sustainable Pace Debt work should be sustainable over time. Avoid boom-bust cycles of neglect followed by emergency remediation. ## Prioritization Frameworks ### Framework 1: Cost of Delay (CoD) **Best For**: Teams with clear business metrics and well-understood customer impact. **Formula**: `Priority Score = (Business Value + Urgency + Risk Reduction) / Effort` **Components**: **Business Value (1-10 scale)** - Customer impact: How many users affected? - Revenue impact: Direct effect on business metrics - Strategic value: Alignment with business goals - Competitive advantage: Market positioning benefits **Urgency (1-10 scale)** - Time sensitivity: How quickly does value decay? - Dependency criticality: Does this block other work? - Market timing: External deadlines or windows - Regulatory pressure: Compliance requirements **Risk Reduction (1-10 scale)** - Security risk mitigation: Vulnerability reduction - Reliability improvement: Stability gains - Compliance risk: Regulatory issue prevention - Technical risk: Architectural problem prevention **Effort Estimation** - Development time in story points or days - Risk multiplier for uncertainty (1.0-2.0x) - Skill requirements and availability - Cross-team coordination needs **Example Calculation**: ``` Authentication module refactor: - Business Value: 8 (affects all users, blocks SSO) - Urgency: 7 (blocks Q2 enterprise features) - Risk Reduction: 9 (high security risk) - Total Numerator: 24 - Effort: 3 weeks = 15 story points - CoD Score: 24/15 = 1.6 ``` ### Framework 2: Weighted Shortest Job First (WSJF) **Best For**: SAFe/Agile environments with portfolio-level planning. **Formula**: `WSJF = (Business Value + Time Criticality + Risk Reduction) / Job Size` **Scoring Guidelines**: **Business Value (1-20 scale)** - User/business value from fixing this debt - Direct revenue or cost impact - Strategic importance to business objectives **Time Criticality (1-20 scale)** - How user/business value declines over time - Dependency on other work items - Fixed deadlines or time-sensitive opportunities **Risk Reduction/Opportunity Enablement (1-20 scale)** - Risk mitigation value - Future opportunities this enables - Options this preserves or creates **Job Size (1-20 scale)** - Relative sizing compared to other debt items - Include uncertainty and risk factors - Consider dependencies and coordination overhead **WSJF Bands**: - **Highest (WSJF > 10)**: Do immediately - **High (WSJF 5-10)**: Next quarter priority - **Medium (WSJF 2-5)**: Planned work - **Low (WSJF < 2)**: Backlog ### Framework 3: RICE (Reach, Impact, Confidence, Effort) **Best For**: Product-focused teams with user-centric metrics. **Formula**: `RICE Score = (Reach × Impact × Confidence) / Effort` **Components**: **Reach (number or percentage)** - How many developers/users affected per period? - Percentage of codebase impacted - Number of features that would benefit **Impact (1-3 scale)** - 3 = Massive impact - 2 = High impact - 1 = Medium impact - 0.5 = Low impact - 0.25 = Minimal impact **Confidence (percentage)** - How confident are you in your estimates? - Based on evidence, not gut feeling - 100% = High confidence with data - 80% = Medium confidence with some data - 50% = Low confidence, mostly assumptions **Effort (story points or person-months)** - Total effort from all team members - Include design, development, testing, deployment - Account for coordination and communication overhead **Example**: ``` Legacy API cleanup: - Reach: 5 teams × 4 developers = 20 people per quarter - Impact: 2 (high - significantly improves developer experience) - Confidence: 80% (have done similar cleanups before) - Effort: 8 story points - RICE: (20 × 2 × 0.8) / 8 = 4.0 ``` ### Framework 4: Technical Debt Quadrants **Best For**: Teams needing to understand debt context and strategy. Based on Martin Fowler's framework, categorize debt into quadrants: **Quadrant 1: Reckless & Deliberate** - "We don't have time for design" - **Strategy**: Immediate remediation - **Priority**: Highest - created knowingly with poor justification **Quadrant 2: Prudent & Deliberate** - "We must ship now and deal with consequences" - **Strategy**: Planned remediation - **Priority**: High - was right decision at time, now needs attention **Quadrant 3: Reckless & Inadvertent** - "What's layering?" - **Strategy**: Education and process improvement - **Priority**: Medium - focus on preventing more **Quadrant 4: Prudent & Inadvertent** - "Now we know how we should have done it" - **Strategy**: Opportunistic improvement - **Priority**: Low - normal part of learning ### Framework 5: Risk-Impact Matrix **Best For**: Risk-averse organizations or regulated environments. Plot debt items on 2D matrix: - X-axis: Likelihood of negative impact (1-5) - Y-axis: Severity of negative impact (1-5) **Priority Quadrants**: - **Critical (High likelihood, High impact)**: Immediate action - **Important (High likelihood, Low impact OR Low likelihood, High impact)**: Planned action - **Monitor (Medium likelihood, Medium impact)**: Watch and assess - **Accept (Low likelihood, Low impact)**: Document decision to accept **Impact Categories**: - **Security**: Data breaches, vulnerability exploitation - **Reliability**: System outages, data corruption - **Performance**: User experience degradation - **Compliance**: Regulatory violations, audit findings - **Productivity**: Team velocity reduction, developer frustration ## Multi-Framework Approach ### When to Use Multiple Frameworks **Portfolio-Level Planning**: - Use WSJF for quarterly planning - Use CoD for sprint-level decisions - Use Risk-Impact for security review **Team Maturity Progression**: - Start with simple Risk-Impact matrix - Progress to RICE as metrics improve - Advanced teams can use CoD effectively **Context-Dependent Selection**: - **Regulated industries**: Risk-Impact primary, WSJF secondary - **Product companies**: RICE primary, CoD secondary - **Enterprise software**: CoD primary, WSJF secondary ### Combining Framework Results **Weighted Scoring**: ``` Final Priority = 0.4 × CoD_Score + 0.3 × RICE_Score + 0.3 × Risk_Score ``` **Tier-Based Approach**: 1. Security/compliance items (Risk-Impact) 2. High business value items (RICE/CoD) 3. Developer productivity items (WSJF) 4. Technical excellence items (Quadrants) ## Implementation Guidelines ### Setting Up Prioritization **Step 1: Choose Primary Framework** - Consider team maturity, organization culture, available data - Start simple, evolve complexity over time - Ensure framework aligns with business planning cycles **Step 2: Define Scoring Criteria** - Create rubrics for each scoring dimension - Use organization-specific examples - Train team on consistent application **Step 3: Establish Review Cadence** - Weekly: New urgent items - Bi-weekly: Sprint planning integration - Monthly: Portfolio review and reprioritization - Quarterly: Framework effectiveness review **Step 4: Tool Integration** - Use existing project management tools - Automate scoring where possible - Create dashboards for stakeholder communication ### Common Pitfalls **Analysis Paralysis** - **Problem**: Spending too much time on perfect prioritization - **Solution**: Use "good enough" decisions, iterate quickly **Ignoring Business Context** - **Problem**: Purely technical prioritization - **Solution**: Always include business stakeholder perspective **Inconsistent Application** - **Problem**: Different teams using different approaches - **Solution**: Standardize framework, provide training **Over-Engineering the Process** - **Problem**: Complex frameworks nobody uses - **Solution**: Start simple, add complexity only when needed **Neglecting Stakeholder Buy-In** - **Problem**: Engineering-only prioritization decisions - **Solution**: Include product, business stakeholders in framework design ### Measuring Framework Effectiveness **Leading Indicators**: - Framework adoption rate across teams - Time to prioritization decision - Stakeholder satisfaction with decisions - Consistency of scoring across team members **Lagging Indicators**: - Debt reduction velocity - Business outcome improvements - Technical incident reduction - Developer satisfaction improvements **Review Questions**: 1. Are we making better debt decisions than before? 2. Do stakeholders trust our prioritization process? 3. Are we delivering measurable business value from debt work? 4. Is the framework sustainable for long-term use? ## Stakeholder Communication ### For Engineering Leaders **Monthly Dashboard**: - Debt portfolio health score - Priority distribution by framework - Progress on high-priority items - Framework effectiveness metrics **Quarterly Business Review**: - Debt work business impact - Framework ROI analysis - Resource allocation recommendations - Strategic debt initiative proposals ### For Product Managers **Sprint Planning Input**: - Debt items affecting feature velocity - User experience impact from debt - Feature delivery risk from debt - Opportunity cost of debt work vs features **Roadmap Integration**: - Debt work timing with feature releases - Dependencies between debt work and features - Resource allocation for debt vs features - Customer impact communication ### for Executive Leadership **Executive Summary**: - Overall technical health trend - Business risk from technical debt - Investment recommendations - Competitive implications **Key Metrics**: - Debt-adjusted development velocity - Technical incident trends - Customer satisfaction correlations - Team retention and satisfaction This prioritization framework should be adapted to your organization's context, but the core principles of evidence-based, business-aligned, systematic prioritization should remain constant. FILE:references/stakeholder-communication-templates.md # Stakeholder Communication Templates ## Introduction Effective communication about technical debt is crucial for securing resources, setting expectations, and maintaining stakeholder trust. This document provides templates and guidelines for communicating technical debt status, impact, and recommendations to different stakeholder groups. ## Executive Summary Templates ### Monthly Executive Report **Subject**: Technical Health Report - [Month] [Year] --- **EXECUTIVE SUMMARY** **Overall Status**: [EXCELLENT/GOOD/FAIR/POOR] - Health Score: [X]/100 **Key Message**: [One sentence summary of current state and trend] **Immediate Actions Required**: [Yes/No] - [Brief explanation if yes] --- **BUSINESS IMPACT** • **Development Velocity**: [X]% impact on feature delivery speed • **Quality Risk**: [LOW/MEDIUM/HIGH] - [Brief explanation] • **Security Posture**: [X] critical issues, [X] high-priority issues • **Customer Impact**: [Direct customer-facing implications] **FINANCIAL IMPLICATIONS** • **Current Cost**: $[X]K monthly in reduced velocity • **Investment Needed**: $[X]K for critical issues (next quarter) • **ROI Projection**: [X]% velocity improvement, $[X]K annual savings • **Risk Cost**: Up to $[X]K if critical issues materialize **STRATEGIC RECOMMENDATIONS** 1. **[Priority 1]**: [Action] - [Business justification] - [Timeline] 2. **[Priority 2]**: [Action] - [Business justification] - [Timeline] 3. **[Priority 3]**: [Action] - [Business justification] - [Timeline] **TREND ANALYSIS** • Health Score: [Previous] → [Current] ([Improving/Declining/Stable]) • Debt Items: [Previous] → [Current] ([Net change]) • High-Priority Issues: [Previous] → [Current] --- **NEXT STEPS** • **This Quarter**: [Key initiatives and expected outcomes] • **Resource Request**: [Additional resources needed, if any] • **Dependencies**: [External dependencies or blockers] --- ### Quarterly Board-Level Report **Subject**: Technical Debt & Engineering Health - Q[X] [Year] --- **KEY METRICS** | Metric | Current | Target | Trend | |--------|---------|--------|--------| | Health Score | [X]/100 | [X]/100 | [↑/↓/→] | | Velocity Impact | [X]% | <[X]% | [↑/↓/→] | | Critical Issues | [X] | 0 | [↑/↓/→] | | Security Risk | [LOW/MED/HIGH] | LOW | [↑/↓/→] | **STRATEGIC CONTEXT** Technical debt represents deferred investment in our technology platform. Our current debt portfolio has [positive/negative/neutral] implications for: • **Growth Capacity**: [Impact on ability to scale] • **Competitive Position**: [Impact on market responsiveness] • **Risk Profile**: [Impact on operational risk] • **Team Retention**: [Impact on engineering talent] **INVESTMENT ANALYSIS** • **Current Annual Cost**: $[X]M in reduced productivity • **Proposed Investment**: $[X]M over [timeframe] • **Expected ROI**: [X]% productivity improvement, $[X]M NPV • **Risk Mitigation**: $[X]M in avoided incident costs **RECOMMENDATIONS** 1. **[Immediate]**: [Strategic action with business rationale] 2. **[This Year]**: [Medium-term initiative with expected outcomes] 3. **[Ongoing]**: [Process or cultural change needed] --- ## Product Management Templates ### Sprint Planning Discussion **Subject**: Tech Debt Impact on Sprint [X] Planning --- **SPRINT CAPACITY IMPACT** **Affected User Stories**: • [Story 1]: [X] point increase due to [debt issue] • [Story 2]: [X]% risk of scope reduction due to [debt issue] • [Story 3]: Blocked by [debt issue] - requires [X] points of debt work first **Recommended Debt Work This Sprint**: • **[Debt Item 1]** ([X] points): Unblocks [Story Y], reduces future story complexity • **[Debt Item 2]** ([X] points): Prevents [specific risk] in upcoming features **Trade-off Analysis**: • **If we fix debt**: [X] points for features, [benefits for future sprints] • **If we don't fix debt**: [X] points for features, [accumulated costs and risks] **Recommendation**: [Specific allocation suggestion with rationale] --- ### Feature Impact Assessment **Subject**: Technical Debt Impact Assessment - [Feature Name] --- **DEBT AFFECTING THIS FEATURE** | Debt Item | Impact | Effort to Fix | Recommendation | |-----------|--------|---------------|----------------| | [Item 1] | [Description] | [X] points | Fix before/Work around/Accept | | [Item 2] | [Description] | [X] points | Fix before/Work around/Accept | **DELIVERY IMPACT** • **Timeline Risk**: [LOW/MEDIUM/HIGH] - Base estimate: [X] points - Debt-adjusted estimate: [X] points ([X]% increase) - Risk factors: [Specific risks and probabilities] • **Quality Risk**: [LOW/MEDIUM/HIGH] - [Specific quality concerns from debt] - Mitigation strategies: [Options for reducing risk] • **Future Feature Impact**: - This feature will [add to/reduce/not affect] debt burden - Related future features will be [easier/harder/unaffected] **RECOMMENDATIONS** 1. **[Option 1]**: [Approach with pros/cons] 2. **[Option 2]**: [Alternative approach with trade-offs] 3. **Recommended**: [Chosen approach with justification] --- ## Engineering Team Templates ### Team Health Check **Subject**: Weekly Team Health Check - [Date] --- **DEBT BURDEN THIS WEEK** • **New Debt Identified**: [X] items ([categories]) • **Debt Resolved**: [X] items ([X] hours saved) • **Net Change**: [Positive/Negative] [X] items • **Top Pain Points**: [Developer-reported friction areas] **VELOCITY IMPACT** • **Stories Affected by Debt**: [X] of [Y] planned stories • **Estimated Overhead**: [X] hours of extra work due to debt • **Blocked Work**: [Any stories waiting on debt resolution] **TEAM SENTIMENT** • **Frustration Level**: [1-5 scale] ([trend]) • **Confidence in Codebase**: [1-5 scale] ([trend]) • **Top Complaints**: [Most common developer concerns] **ACTIONS THIS WEEK** • **Debt Work Planned**: [Specific items and assignees] • **Prevention Measures**: [Process improvements or reviews] • **Escalations**: [Issues needing management attention] --- ### Architecture Decision Record (ADR) Template **Subject**: ADR-[XXX]: [Decision Title] - Technical Debt Consideration --- **Status**: [Proposed/Accepted/Deprecated] **Date**: [YYYY-MM-DD] **Decision Makers**: [Names] **CONTEXT** [Background and current situation] **TECHNICAL DEBT ANALYSIS** • **Debt Created by This Decision**: - [Specific debt that will be introduced] - [Estimated effort to resolve later: X points] - [Interest rate: impact over time] • **Debt Resolved by This Decision**: - [Existing debt this addresses] - [Estimated effort saved: X points] - [Risk reduction achieved] • **Net Debt Impact**: [Positive/Negative/Neutral] **DECISION** [What we decided to do] **RATIONALE** [Why we made this decision, including debt trade-offs] **DEBT MANAGEMENT PLAN** • **Monitoring**: [How we'll track the debt introduced] • **Timeline**: [When we plan to address the debt] • **Success Criteria**: [How we'll know it's time to pay down the debt] **CONSEQUENCES** [Expected outcomes, including debt implications] --- ## Customer-Facing Templates ### Release Notes - Quality Improvements **Subject**: Platform Stability and Performance Improvements - Release [X.Y] --- **QUALITY IMPROVEMENTS** We've invested significant effort in improving the reliability and performance of our platform. While these changes aren't feature additions, they provide important benefits: **RELIABILITY ENHANCEMENTS** • **Reduced Error Rates**: [X]% fewer errors in [specific area] • **Improved Uptime**: [X]% improvement in system availability • **Faster Recovery**: [X]% faster recovery from service interruptions **PERFORMANCE IMPROVEMENTS** • **Page Load Speed**: [X]% faster loading for [specific features] • **API Response Time**: [X]% improvement in response times • **Resource Usage**: [X]% reduction in memory/CPU usage **SECURITY STRENGTHENING** • **Vulnerability Resolution**: Addressed [X] security findings • **Authentication Improvements**: Enhanced login security and reliability • **Data Protection**: Improved data encryption and access controls **WHAT THIS MEANS FOR YOU** • **Better User Experience**: Fewer interruptions, faster responses • **Increased Reliability**: Less downtime, more predictable performance • **Enhanced Security**: Your data is better protected We continue to balance new feature development with platform investments to ensure a reliable, secure, and performant experience. --- ### Service Incident Communication **Subject**: Service Update - [Brief Description] - [Status] --- **INCIDENT SUMMARY** • **Impact**: [Description of customer impact] • **Duration**: [Start time] - [End time / Ongoing] • **Root Cause**: [High-level, customer-appropriate explanation] • **Resolution**: [What was done to fix it] **TECHNICAL DEBT CONNECTION** This incident was [directly caused by / contributed to by / unrelated to] technical debt in our system. Specifically: • **Contributing Factors**: [How debt played a role, if any] • **Prevention Measures**: [Debt work planned to prevent recurrence] • **Timeline**: [When preventive measures will be completed] **IMMEDIATE ACTIONS** 1. [Action 1 with timeline] 2. [Action 2 with timeline] 3. [Action 3 with timeline] **LONG-TERM IMPROVEMENTS** We're investing in [specific technical improvements] to prevent similar issues: • **Infrastructure**: [Relevant infrastructure debt work] • **Monitoring**: [Observability improvements planned] • **Process**: [Development process improvements] We apologize for the inconvenience and appreciate your patience as we continue to strengthen our platform. --- ## Internal Communication Templates ### Engineering All-Hands Presentation **Slide Template: Technical Debt State of the Union** --- **SLIDE 1: Current State** - Health Score: [X]/100 [Trend arrow] - Total Debt Items: [X] ([X]% of codebase) - High Priority: [X] items requiring immediate attention - Team Impact: [X]% velocity reduction **SLIDE 2: What We've Accomplished** - Resolved [X] debt items ([X] hours of future work saved) - Improved health score by [X] points - Key wins: [2-3 specific examples with business impact] **SLIDE 3: Current Focus Areas** - [Category 1]: [X] items, [business impact] - [Category 2]: [X] items, [business impact] - [Category 3]: [X] items, [business impact] **SLIDE 4: Success Stories** - [Specific example]: [Problem] → [Solution] → [Outcome] - Metrics: [Before/after comparison] - Team feedback: [Developer quotes] **SLIDE 5: Looking Forward** - Q[X] Goals: [Specific targets] - Major Initiatives: [2-3 big-picture improvements] - How You Can Help: [Specific asks of the team] --- ### Retrospective Templates **Sprint Retrospective - Debt Focus** **What Went Well**: • Debt work completed: [Specific items and impact] • Process improvements: [What worked for debt management] • Team collaboration: [Cross-functional debt work successes] **What Didn't Go Well**: • Debt work challenges: [Obstacles encountered] • Scope creep: [Debt work that expanded beyond estimates] • Communication gaps: [Information that wasn't shared effectively] **Action Items**: • **Process**: [Changes to how we handle debt work] • **Planning**: [Improvements to debt estimation/prioritization] • **Prevention**: [Changes to prevent new debt creation] • **Tools**: [Tooling improvements needed] --- ## Communication Best Practices ### Do's and Don'ts **DO**: • Use business language, not technical jargon • Quantify impact with specific metrics • Provide clear timelines and expectations • Acknowledge trade-offs and constraints • Connect debt work to business outcomes • Be proactive in communication **DON'T**: • Blame previous decisions or developers • Use fear-based messaging exclusively • Overwhelm stakeholders with technical details • Make promises without clear plans • Ignore the business context • Assume stakeholders understand technical implications ### Tailoring Messages **For Executives**: Focus on business impact, ROI, and strategic implications **For Product**: Focus on feature impact, timeline risks, and user experience **For Engineering**: Focus on technical details, process improvements, and developer experience **For Customers**: Focus on reliability, performance, and security benefits ### Frequency Guidelines **Real-time**: Critical security issues, production incidents **Weekly**: Team health checks, sprint impacts **Monthly**: Stakeholder updates, trend analysis **Quarterly**: Strategic reviews, investment planning **As-needed**: Major decisions, significant changes These templates should be customized for your organization's communication style, stakeholder preferences, and business context. FILE:scripts/debt_dashboard.py #!/usr/bin/env python3 """ Tech Debt Dashboard Takes historical debt inventories (multiple scans over time) and generates trend analysis, debt velocity (accruing vs paying down), health score, and executive summary. Usage: python debt_dashboard.py historical_data.json python debt_dashboard.py data1.json data2.json data3.json python debt_dashboard.py --input-dir ./debt_scans/ --output dashboard_report.json python debt_dashboard.py historical_data.json --period quarterly --team-size 8 """ import json import argparse import sys import os from collections import defaultdict, Counter from datetime import datetime, timedelta from pathlib import Path from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, asdict from statistics import mean, median, stdev import re @dataclass class HealthMetrics: """Health metrics for a specific time period.""" overall_score: float # 0-100 debt_density: float # debt items per file velocity_impact: float # estimated velocity reduction % quality_score: float # 0-100 maintainability_score: float # 0-100 technical_risk_score: float # 0-100 @dataclass class TrendAnalysis: """Trend analysis for debt metrics over time.""" metric_name: str trend_direction: str # "improving", "declining", "stable" change_rate: float # rate of change per period correlation_strength: float # -1 to 1 forecast_next_period: float confidence_interval: Tuple[float, float] @dataclass class DebtVelocity: """Debt velocity tracking - how fast debt is being created vs resolved.""" period: str new_debt_items: int resolved_debt_items: int net_change: int velocity_ratio: float # resolved/new, >1 is good effort_hours_added: float effort_hours_resolved: float net_effort_change: float class DebtDashboard: """Main dashboard class for debt trend analysis and reporting.""" def __init__(self, team_size: int = 5): self.team_size = team_size self.historical_data = [] self.processed_snapshots = [] self.trend_analyses = {} self.health_history = [] self.velocity_history = [] # Configuration for health scoring self.health_weights = { "debt_density": 0.25, "complexity_score": 0.20, "test_coverage_proxy": 0.15, "documentation_proxy": 0.10, "security_score": 0.15, "maintainability": 0.15 } # Thresholds for categorization self.thresholds = { "excellent": 85, "good": 70, "fair": 55, "poor": 40 } def load_historical_data(self, file_paths: List[str]) -> bool: """Load multiple debt inventory files for historical analysis.""" self.historical_data = [] for file_path in file_paths: try: with open(file_path, 'r', encoding='utf-8') as f: data = json.load(f) # Normalize data format if isinstance(data, dict) and 'debt_items' in data: # Scanner output format snapshot = { "file_path": file_path, "scan_date": data.get("scan_metadata", {}).get("scan_date", self._extract_date_from_filename(file_path)), "debt_items": data["debt_items"], "summary": data.get("summary", {}), "file_statistics": data.get("file_statistics", {}) } elif isinstance(data, dict) and 'prioritized_backlog' in data: # Prioritizer output format snapshot = { "file_path": file_path, "scan_date": data.get("metadata", {}).get("analysis_date", self._extract_date_from_filename(file_path)), "debt_items": data["prioritized_backlog"], "summary": data.get("insights", {}), "file_statistics": {} } elif isinstance(data, list): # Raw debt items array snapshot = { "file_path": file_path, "scan_date": self._extract_date_from_filename(file_path), "debt_items": data, "summary": {}, "file_statistics": {} } else: raise ValueError(f"Unrecognized data format in {file_path}") self.historical_data.append(snapshot) except Exception as e: print(f"Error loading {file_path}: {e}") continue if not self.historical_data: print("No valid data files loaded.") return False # Sort by date self.historical_data.sort(key=lambda x: x["scan_date"]) print(f"Loaded {len(self.historical_data)} historical snapshots") return True def load_from_directory(self, directory_path: str, pattern: str = "*.json") -> bool: """Load all JSON files from a directory.""" directory = Path(directory_path) if not directory.exists(): print(f"Directory does not exist: {directory_path}") return False file_paths = [] for file_path in directory.glob(pattern): if file_path.is_file(): file_paths.append(str(file_path)) if not file_paths: print(f"No matching files found in {directory_path}") return False return self.load_historical_data(file_paths) def _extract_date_from_filename(self, file_path: str) -> str: """Extract date from filename if possible, otherwise use current date.""" filename = Path(file_path).name # Try to find date patterns in filename date_patterns = [ r"(\d{4}-\d{2}-\d{2})", # YYYY-MM-DD r"(\d{4}\d{2}\d{2})", # YYYYMMDD r"(\d{2}-\d{2}-\d{4})", # MM-DD-YYYY ] for pattern in date_patterns: match = re.search(pattern, filename) if match: date_str = match.group(1) try: if len(date_str) == 8: # YYYYMMDD date_str = f"{date_str[:4]}-{date_str[4:6]}-{date_str[6:]}" datetime.strptime(date_str, "%Y-%m-%d") return date_str + "T12:00:00" except ValueError: continue # Fallback to file modification time try: mtime = os.path.getmtime(file_path) return datetime.fromtimestamp(mtime).isoformat() except: return datetime.now().isoformat() def generate_dashboard(self, period: str = "monthly") -> Dict[str, Any]: """ Generate comprehensive debt dashboard. Args: period: Analysis period ("weekly", "monthly", "quarterly") Returns: Dictionary containing dashboard data and analysis """ print(f"Generating debt dashboard for {len(self.historical_data)} snapshots...") print(f"Analysis period: {period}") print("=" * 50) # Step 1: Process historical snapshots self._process_snapshots() # Step 2: Calculate health metrics for each snapshot self._calculate_health_metrics() # Step 3: Analyze trends self._analyze_trends(period) # Step 4: Calculate debt velocity self._calculate_debt_velocity(period) # Step 5: Generate forecasts forecasts = self._generate_forecasts() # Step 6: Create executive summary executive_summary = self._generate_executive_summary() # Step 7: Generate recommendations recommendations = self._generate_strategic_recommendations() # Step 8: Create visualizations data visualizations = self._generate_visualization_data() dashboard_data = { "metadata": { "generated_date": datetime.now().isoformat(), "analysis_period": period, "snapshots_analyzed": len(self.historical_data), "date_range": { "start": self.historical_data[0]["scan_date"] if self.historical_data else None, "end": self.historical_data[-1]["scan_date"] if self.historical_data else None }, "team_size": self.team_size }, "executive_summary": executive_summary, "current_health": self.health_history[-1] if self.health_history else None, "trend_analysis": {name: asdict(trend) for name, trend in self.trend_analyses.items()}, "debt_velocity": [asdict(v) for v in self.velocity_history], "forecasts": forecasts, "recommendations": recommendations, "visualizations": visualizations, "detailed_metrics": self._get_detailed_metrics() } return dashboard_data def _process_snapshots(self): """Process raw snapshots into standardized format.""" self.processed_snapshots = [] for snapshot in self.historical_data: processed = { "date": snapshot["scan_date"], "total_debt_items": len(snapshot["debt_items"]), "debt_by_type": Counter(item.get("type", "unknown") for item in snapshot["debt_items"]), "debt_by_severity": Counter(item.get("severity", "medium") for item in snapshot["debt_items"]), "debt_by_category": Counter(self._categorize_debt_item(item) for item in snapshot["debt_items"]), "total_files": snapshot["summary"].get("total_files_scanned", len(snapshot["file_statistics"])), "total_effort_estimate": self._calculate_total_effort(snapshot["debt_items"]), "high_priority_count": len([item for item in snapshot["debt_items"] if self._is_high_priority(item)]), "security_debt_count": len([item for item in snapshot["debt_items"] if self._is_security_related(item)]), "raw_data": snapshot } self.processed_snapshots.append(processed) def _categorize_debt_item(self, item: Dict[str, Any]) -> str: """Categorize debt item into high-level categories.""" debt_type = item.get("type", "unknown") categories = { "code_quality": ["large_function", "high_complexity", "duplicate_code", "long_line", "missing_docstring"], "architecture": ["architecture_debt", "large_file"], "security": ["security_risk", "hardcoded_secrets", "sql_injection_risk"], "testing": ["test_debt", "missing_tests", "low_coverage"], "maintenance": ["todo_comment", "commented_code"], "dependencies": ["dependency_debt", "outdated_packages"], "infrastructure": ["deployment_debt", "monitoring_gaps"], "documentation": ["missing_docstring", "outdated_docs"] } for category, types in categories.items(): if debt_type in types: return category return "other" def _calculate_total_effort(self, debt_items: List[Dict[str, Any]]) -> float: """Calculate total estimated effort for debt items.""" total_effort = 0.0 for item in debt_items: # Try to get effort from existing analysis if "effort_estimate" in item: total_effort += item["effort_estimate"].get("hours_estimate", 0) else: # Estimate based on debt type and severity effort = self._estimate_item_effort(item) total_effort += effort return total_effort def _estimate_item_effort(self, item: Dict[str, Any]) -> float: """Estimate effort for a debt item.""" debt_type = item.get("type", "unknown") severity = item.get("severity", "medium") base_efforts = { "todo_comment": 2, "missing_docstring": 2, "long_line": 1, "large_function": 8, "high_complexity": 16, "duplicate_code": 12, "large_file": 32, "syntax_error": 4, "security_risk": 20, "architecture_debt": 80, "test_debt": 16 } base_effort = base_efforts.get(debt_type, 8) severity_multipliers = { "low": 0.5, "medium": 1.0, "high": 1.5, "critical": 2.0 } return base_effort * severity_multipliers.get(severity, 1.0) def _is_high_priority(self, item: Dict[str, Any]) -> bool: """Determine if debt item is high priority.""" severity = item.get("severity", "medium") priority_score = item.get("priority_score", 0) debt_type = item.get("type", "") return (severity in ["high", "critical"] or priority_score >= 7 or debt_type in ["security_risk", "syntax_error", "architecture_debt"]) def _is_security_related(self, item: Dict[str, Any]) -> bool: """Determine if debt item is security-related.""" debt_type = item.get("type", "") description = item.get("description", "").lower() security_types = ["security_risk", "hardcoded_secrets", "sql_injection_risk"] security_keywords = ["password", "token", "key", "secret", "auth", "security"] return (debt_type in security_types or any(keyword in description for keyword in security_keywords)) def _calculate_health_metrics(self): """Calculate health metrics for each snapshot.""" self.health_history = [] for snapshot in self.processed_snapshots: # Debt density (lower is better) debt_density = snapshot["total_debt_items"] / max(1, snapshot["total_files"]) debt_density_score = max(0, 100 - (debt_density * 20)) # Scale to 0-100 # Complexity score (based on high complexity debt) complex_debt_ratio = (snapshot["debt_by_type"].get("high_complexity", 0) + snapshot["debt_by_type"].get("large_function", 0)) / max(1, snapshot["total_debt_items"]) complexity_score = max(0, 100 - (complex_debt_ratio * 100)) # Test coverage proxy (based on test debt) test_debt_ratio = snapshot["debt_by_category"].get("testing", 0) / max(1, snapshot["total_debt_items"]) test_coverage_proxy = max(0, 100 - (test_debt_ratio * 150)) # Documentation proxy (based on documentation debt) doc_debt_ratio = snapshot["debt_by_category"].get("documentation", 0) / max(1, snapshot["total_debt_items"]) documentation_proxy = max(0, 100 - (doc_debt_ratio * 100)) # Security score (based on security debt) security_debt_ratio = snapshot["security_debt_count"] / max(1, snapshot["total_debt_items"]) security_score = max(0, 100 - (security_debt_ratio * 200)) # Maintainability (based on architecture and code quality debt) maint_debt_count = (snapshot["debt_by_category"].get("architecture", 0) + snapshot["debt_by_category"].get("code_quality", 0)) maint_debt_ratio = maint_debt_count / max(1, snapshot["total_debt_items"]) maintainability = max(0, 100 - (maint_debt_ratio * 120)) # Calculate weighted overall score weights = self.health_weights overall_score = ( debt_density_score * weights["debt_density"] + complexity_score * weights["complexity_score"] + test_coverage_proxy * weights["test_coverage_proxy"] + documentation_proxy * weights["documentation_proxy"] + security_score * weights["security_score"] + maintainability * weights["maintainability"] ) # Velocity impact (estimated percentage reduction in team velocity) high_impact_ratio = snapshot["high_priority_count"] / max(1, snapshot["total_debt_items"]) velocity_impact = min(50, high_impact_ratio * 30 + debt_density * 5) # Technical risk (0-100, higher is more risky) risk_factors = snapshot["security_debt_count"] + snapshot["debt_by_type"].get("architecture_debt", 0) technical_risk = min(100, risk_factors * 10 + (100 - security_score)) health_metrics = HealthMetrics( overall_score=round(overall_score, 1), debt_density=round(debt_density, 2), velocity_impact=round(velocity_impact, 1), quality_score=round((complexity_score + maintainability) / 2, 1), maintainability_score=round(maintainability, 1), technical_risk_score=round(technical_risk, 1) ) # Add timestamp health_entry = asdict(health_metrics) health_entry["date"] = snapshot["date"] self.health_history.append(health_entry) def _analyze_trends(self, period: str): """Analyze trends in various metrics.""" self.trend_analyses = {} if len(self.health_history) < 2: return # Define metrics to analyze metrics_to_analyze = [ "overall_score", "debt_density", "velocity_impact", "quality_score", "technical_risk_score" ] for metric in metrics_to_analyze: values = [entry[metric] for entry in self.health_history] dates = [datetime.fromisoformat(entry["date"].replace('Z', '+00:00')) for entry in self.health_history] trend = self._calculate_trend(values, dates, metric) self.trend_analyses[metric] = trend def _calculate_trend(self, values: List[float], dates: List[datetime], metric_name: str) -> TrendAnalysis: """Calculate trend analysis for a specific metric.""" if len(values) < 2: return TrendAnalysis(metric_name, "stable", 0.0, 0.0, values[-1], (values[-1], values[-1])) # Calculate simple linear trend n = len(values) x = list(range(n)) # Time periods as numbers # Linear regression x_mean = mean(x) y_mean = mean(values) numerator = sum((x[i] - x_mean) * (values[i] - y_mean) for i in range(n)) denominator = sum((x[i] - x_mean) ** 2 for i in range(n)) if denominator == 0: slope = 0 else: slope = numerator / denominator # Correlation strength if n > 2 and len(set(values)) > 1: try: correlation = numerator / ( (sum((x[i] - x_mean) ** 2 for i in range(n)) * sum((values[i] - y_mean) ** 2 for i in range(n))) ** 0.5 ) except ZeroDivisionError: correlation = 0.0 else: correlation = 0.0 # Determine trend direction if abs(slope) < 0.1: trend_direction = "stable" elif slope > 0: if metric_name in ["overall_score", "quality_score"]: trend_direction = "improving" # Higher is better else: trend_direction = "declining" # Higher is worse else: if metric_name in ["overall_score", "quality_score"]: trend_direction = "declining" else: trend_direction = "improving" # Forecast next period forecast = values[-1] + slope # Confidence interval (simple approach) if n > 2: residuals = [values[i] - (y_mean + slope * (x[i] - x_mean)) for i in range(n)] std_error = (sum(r**2 for r in residuals) / (n - 2)) ** 0.5 confidence_interval = (forecast - std_error, forecast + std_error) else: confidence_interval = (forecast, forecast) return TrendAnalysis( metric_name=metric_name, trend_direction=trend_direction, change_rate=round(slope, 3), correlation_strength=round(correlation, 3), forecast_next_period=round(forecast, 2), confidence_interval=(round(confidence_interval[0], 2), round(confidence_interval[1], 2)) ) def _calculate_debt_velocity(self, period: str): """Calculate debt velocity between snapshots.""" self.velocity_history = [] if len(self.processed_snapshots) < 2: return for i in range(1, len(self.processed_snapshots)): current = self.processed_snapshots[i] previous = self.processed_snapshots[i-1] # Track debt by unique identifiers when possible current_debt_ids = set() previous_debt_ids = set() current_effort = current["total_effort_estimate"] previous_effort = previous["total_effort_estimate"] # Simple approach: compare total counts and effort debt_change = current["total_debt_items"] - previous["total_debt_items"] effort_change = current_effort - previous_effort # Estimate new vs resolved (rough approximation) if debt_change >= 0: new_debt_items = debt_change resolved_debt_items = 0 else: new_debt_items = 0 resolved_debt_items = abs(debt_change) # Calculate velocity ratio if new_debt_items > 0: velocity_ratio = resolved_debt_items / new_debt_items else: velocity_ratio = float('inf') if resolved_debt_items > 0 else 1.0 velocity = DebtVelocity( period=f"{previous['date'][:10]} to {current['date'][:10]}", new_debt_items=new_debt_items, resolved_debt_items=resolved_debt_items, net_change=debt_change, velocity_ratio=min(10.0, velocity_ratio), # Cap at 10 for display effort_hours_added=max(0, effort_change), effort_hours_resolved=max(0, -effort_change), net_effort_change=effort_change ) self.velocity_history.append(velocity) def _generate_forecasts(self) -> Dict[str, Any]: """Generate forecasts based on trend analysis.""" if not self.trend_analyses: return {} forecasts = {} # Overall health forecast health_trend = self.trend_analyses.get("overall_score") if health_trend: current_score = self.health_history[-1]["overall_score"] forecasts["health_score_3_months"] = max(0, min(100, current_score + (health_trend.change_rate * 3))) forecasts["health_score_6_months"] = max(0, min(100, current_score + (health_trend.change_rate * 6))) # Debt accumulation forecast if self.velocity_history: avg_net_change = mean([v.net_change for v in self.velocity_history[-3:]]) # Last 3 periods current_debt = self.processed_snapshots[-1]["total_debt_items"] forecasts["debt_count_3_months"] = max(0, current_debt + (avg_net_change * 3)) forecasts["debt_count_6_months"] = max(0, current_debt + (avg_net_change * 6)) # Risk forecast risk_trend = self.trend_analyses.get("technical_risk_score") if risk_trend: current_risk = self.health_history[-1]["technical_risk_score"] forecasts["risk_score_3_months"] = max(0, min(100, current_risk + (risk_trend.change_rate * 3))) return forecasts def _generate_executive_summary(self) -> Dict[str, Any]: """Generate executive summary of debt status.""" if not self.health_history: return {} current_health = self.health_history[-1] # Determine overall status score = current_health["overall_score"] if score >= self.thresholds["excellent"]: status = "excellent" status_message = "Code quality is excellent with minimal technical debt." elif score >= self.thresholds["good"]: status = "good" status_message = "Code quality is good with manageable technical debt." elif score >= self.thresholds["fair"]: status = "fair" status_message = "Code quality needs attention. Technical debt is accumulating." else: status = "poor" status_message = "Critical: High levels of technical debt requiring immediate action." # Key insights insights = [] if len(self.health_history) > 1: prev_health = self.health_history[-2] score_change = current_health["overall_score"] - prev_health["overall_score"] if score_change > 5: insights.append("Health score improving significantly") elif score_change < -5: insights.append("Health score declining - attention needed") if current_health["velocity_impact"] > 20: insights.append("High velocity impact detected - development speed affected") if current_health["technical_risk_score"] > 70: insights.append("High technical risk - security and stability concerns") # Debt velocity insight if self.velocity_history: recent_velocity = self.velocity_history[-1] if recent_velocity.velocity_ratio < 0.5: insights.append("Debt accumulating faster than resolution") elif recent_velocity.velocity_ratio > 1.5: insights.append("Good progress on debt reduction") return { "overall_status": status, "health_score": current_health["overall_score"], "status_message": status_message, "key_insights": insights, "total_debt_items": self.processed_snapshots[-1]["total_debt_items"] if self.processed_snapshots else 0, "estimated_effort_hours": self.processed_snapshots[-1]["total_effort_estimate"] if self.processed_snapshots else 0, "high_priority_items": self.processed_snapshots[-1]["high_priority_count"] if self.processed_snapshots else 0, "velocity_impact_percent": current_health["velocity_impact"] } def _generate_strategic_recommendations(self) -> List[Dict[str, Any]]: """Generate strategic recommendations for debt management.""" recommendations = [] if not self.health_history: return recommendations current_health = self.health_history[-1] current_snapshot = self.processed_snapshots[-1] if self.processed_snapshots else {} # Health-based recommendations if current_health["overall_score"] < 50: recommendations.append({ "priority": "critical", "category": "immediate_action", "title": "Initiate Emergency Debt Reduction", "description": "Current health score is critically low. Consider dedicating 50%+ of development capacity to debt reduction.", "impact": "high", "effort": "high" }) # Velocity impact recommendations if current_health["velocity_impact"] > 25: recommendations.append({ "priority": "high", "category": "productivity", "title": "Address Velocity Blockers", "description": f"Technical debt is reducing team velocity by {current_health['velocity_impact']:.1f}%. Focus on high-impact debt items first.", "impact": "high", "effort": "medium" }) # Security recommendations if current_health["technical_risk_score"] > 70: recommendations.append({ "priority": "high", "category": "security", "title": "Security Debt Review Required", "description": "High technical risk score indicates security vulnerabilities. Conduct immediate security debt audit.", "impact": "high", "effort": "medium" }) # Trend-based recommendations health_trend = self.trend_analyses.get("overall_score") if health_trend and health_trend.trend_direction == "declining": recommendations.append({ "priority": "medium", "category": "process", "title": "Implement Debt Prevention Measures", "description": "Health score is declining over time. Establish coding standards, automated quality gates, and regular debt reviews.", "impact": "medium", "effort": "medium" }) # Category-specific recommendations if current_snapshot: debt_by_category = current_snapshot["debt_by_category"] top_category = debt_by_category.most_common(1)[0] if debt_by_category else None if top_category and top_category[1] > 10: category, count = top_category recommendations.append({ "priority": "medium", "category": "focus_area", "title": f"Focus on {category.replace('_', ' ').title()} Debt", "description": f"{category.replace('_', ' ').title()} represents the largest debt category ({count} items). Consider targeted initiatives.", "impact": "medium", "effort": "medium" }) # Velocity-based recommendations if self.velocity_history: recent_velocities = self.velocity_history[-3:] if len(self.velocity_history) >= 3 else self.velocity_history avg_velocity_ratio = mean([v.velocity_ratio for v in recent_velocities]) if avg_velocity_ratio < 0.8: recommendations.append({ "priority": "medium", "category": "capacity", "title": "Increase Debt Resolution Capacity", "description": "Debt is accumulating faster than resolution. Consider increasing debt budget or improving resolution efficiency.", "impact": "medium", "effort": "low" }) return recommendations def _generate_visualization_data(self) -> Dict[str, Any]: """Generate data for dashboard visualizations.""" visualizations = {} # Health score timeline visualizations["health_timeline"] = [ { "date": entry["date"][:10], # Date only "overall_score": entry["overall_score"], "quality_score": entry["quality_score"], "technical_risk": entry["technical_risk_score"] } for entry in self.health_history ] # Debt accumulation trend visualizations["debt_accumulation"] = [ { "date": snapshot["date"][:10], "total_debt": snapshot["total_debt_items"], "high_priority": snapshot["high_priority_count"], "security_debt": snapshot["security_debt_count"] } for snapshot in self.processed_snapshots ] # Category distribution (latest snapshot) if self.processed_snapshots: latest_categories = self.processed_snapshots[-1]["debt_by_category"] visualizations["category_distribution"] = [ {"category": category, "count": count} for category, count in latest_categories.items() ] # Velocity chart visualizations["debt_velocity"] = [ { "period": velocity.period, "new_items": velocity.new_debt_items, "resolved_items": velocity.resolved_debt_items, "net_change": velocity.net_change, "velocity_ratio": velocity.velocity_ratio } for velocity in self.velocity_history ] # Effort estimation trend visualizations["effort_trend"] = [ { "date": snapshot["date"][:10], "total_effort": snapshot["total_effort_estimate"] } for snapshot in self.processed_snapshots ] return visualizations def _get_detailed_metrics(self) -> Dict[str, Any]: """Get detailed metrics for the current state.""" if not self.processed_snapshots: return {} current = self.processed_snapshots[-1] return { "debt_breakdown": dict(current["debt_by_type"]), "severity_breakdown": dict(current["debt_by_severity"]), "category_breakdown": dict(current["debt_by_category"]), "files_analyzed": current["total_files"], "debt_density": current["total_debt_items"] / max(1, current["total_files"]), "average_effort_per_item": current["total_effort_estimate"] / max(1, current["total_debt_items"]) } def format_dashboard_report(dashboard_data: Dict[str, Any]) -> str: """Format dashboard data into human-readable report.""" output = [] # Header output.append("=" * 60) output.append("TECHNICAL DEBT DASHBOARD") output.append("=" * 60) metadata = dashboard_data["metadata"] output.append(f"Generated: {metadata['generated_date'][:19]}") output.append(f"Analysis Period: {metadata['analysis_period']}") output.append(f"Snapshots Analyzed: {metadata['snapshots_analyzed']}") if metadata["date_range"]["start"]: output.append(f"Date Range: {metadata['date_range']['start'][:10]} to {metadata['date_range']['end'][:10]}") output.append("") # Executive Summary exec_summary = dashboard_data["executive_summary"] output.append("EXECUTIVE SUMMARY") output.append("-" * 30) output.append(f"Overall Status: {exec_summary['overall_status'].upper()}") output.append(f"Health Score: {exec_summary['health_score']:.1f}/100") output.append(f"Status: {exec_summary['status_message']}") output.append("") output.append("Key Metrics:") output.append(f" • Total Debt Items: {exec_summary['total_debt_items']}") output.append(f" • High Priority Items: {exec_summary['high_priority_items']}") output.append(f" • Estimated Effort: {exec_summary['estimated_effort_hours']:.1f} hours") output.append(f" • Velocity Impact: {exec_summary['velocity_impact_percent']:.1f}%") output.append("") if exec_summary["key_insights"]: output.append("Key Insights:") for insight in exec_summary["key_insights"]: output.append(f" • {insight}") output.append("") # Current Health if dashboard_data["current_health"]: health = dashboard_data["current_health"] output.append("CURRENT HEALTH METRICS") output.append("-" * 30) output.append(f"Overall Score: {health['overall_score']:.1f}/100") output.append(f"Quality Score: {health['quality_score']:.1f}/100") output.append(f"Maintainability: {health['maintainability_score']:.1f}/100") output.append(f"Technical Risk: {health['technical_risk_score']:.1f}/100") output.append(f"Debt Density: {health['debt_density']:.2f} items/file") output.append("") # Trend Analysis trends = dashboard_data["trend_analysis"] if trends: output.append("TREND ANALYSIS") output.append("-" * 30) for metric, trend in trends.items(): direction_symbol = { "improving": "↑", "declining": "↓", "stable": "→" }.get(trend["trend_direction"], "→") output.append(f"{metric.replace('_', ' ').title()}: {direction_symbol} {trend['trend_direction']}") output.append(f" Change Rate: {trend['change_rate']:.3f} per period") output.append(f" Forecast: {trend['forecast_next_period']:.1f}") output.append("") # Top Recommendations recommendations = dashboard_data["recommendations"] if recommendations: output.append("TOP RECOMMENDATIONS") output.append("-" * 30) for i, rec in enumerate(recommendations[:5], 1): output.append(f"{i}. [{rec['priority'].upper()}] {rec['title']}") output.append(f" {rec['description']}") output.append(f" Impact: {rec['impact']}, Effort: {rec['effort']}") output.append("") return "\n".join(output) def main(): """Main entry point for the debt dashboard.""" parser = argparse.ArgumentParser(description="Generate technical debt dashboard") parser.add_argument("files", nargs="*", help="Debt inventory files") parser.add_argument("--input-dir", help="Directory containing debt inventory files") parser.add_argument("--output", help="Output file path") parser.add_argument("--format", choices=["json", "text", "both"], default="both", help="Output format") parser.add_argument("--period", choices=["weekly", "monthly", "quarterly"], default="monthly", help="Analysis period") parser.add_argument("--team-size", type=int, default=5, help="Team size") args = parser.parse_args() # Initialize dashboard dashboard = DebtDashboard(args.team_size) # Load data if args.input_dir: success = dashboard.load_from_directory(args.input_dir) elif args.files: success = dashboard.load_historical_data(args.files) else: print("Error: Must specify either files or --input-dir") sys.exit(1) if not success: sys.exit(1) # Generate dashboard try: dashboard_data = dashboard.generate_dashboard(args.period) except Exception as e: print(f"Dashboard generation failed: {e}") sys.exit(1) # Output results if args.format in ["json", "both"]: json_output = json.dumps(dashboard_data, indent=2, default=str) if args.output: output_path = args.output if args.output.endswith('.json') else f"{args.output}.json" with open(output_path, 'w') as f: f.write(json_output) print(f"JSON dashboard written to: {output_path}") else: print("JSON DASHBOARD:") print("=" * 50) print(json_output) if args.format in ["text", "both"]: text_output = format_dashboard_report(dashboard_data) if args.output: output_path = args.output if args.output.endswith('.txt') else f"{args.output}.txt" with open(output_path, 'w') as f: f.write(text_output) print(f"Text dashboard written to: {output_path}") else: print("\nTEXT DASHBOARD:") print("=" * 50) print(text_output) if __name__ == "__main__": main() FILE:scripts/debt_prioritizer.py #!/usr/bin/env python3 """ Tech Debt Prioritizer Takes a debt inventory (from scanner or manual JSON) and calculates interest rate, effort estimates, and produces a prioritized backlog with recommended sprint allocation. Uses cost-of-delay vs effort scoring and various prioritization frameworks. Usage: python debt_prioritizer.py debt_inventory.json python debt_prioritizer.py debt_inventory.json --output prioritized_backlog.json python debt_prioritizer.py debt_inventory.json --team-size 6 --sprint-capacity 80 python debt_prioritizer.py debt_inventory.json --framework wsjf --output results.json """ import json import argparse import sys import math from collections import defaultdict, Counter from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, asdict @dataclass class EffortEstimate: """Represents effort estimation for a debt item.""" size_points: int hours_estimate: float risk_factor: float # 1.0 = low risk, 1.5 = medium, 2.0+ = high skill_level_required: str # junior, mid, senior, expert confidence: float # 0.0-1.0 @dataclass class BusinessImpact: """Represents business impact assessment for a debt item.""" customer_impact: int # 1-10 scale revenue_impact: int # 1-10 scale team_velocity_impact: int # 1-10 scale quality_impact: int # 1-10 scale security_impact: int # 1-10 scale @dataclass class InterestRate: """Represents the interest rate calculation for technical debt.""" daily_cost: float # cost per day if left unfixed frequency_multiplier: float # how often this code is touched team_impact_multiplier: float # how many developers affected compound_rate: float # how quickly this debt makes other debt worse class DebtPrioritizer: """Main class for prioritizing technical debt items.""" def __init__(self, team_size: int = 5, sprint_capacity_hours: int = 80): self.team_size = team_size self.sprint_capacity_hours = sprint_capacity_hours self.debt_items = [] self.prioritized_items = [] # Prioritization framework weights self.framework_weights = { "cost_of_delay": { "business_value": 0.3, "urgency": 0.3, "risk_reduction": 0.2, "team_productivity": 0.2 }, "wsjf": { "business_value": 0.25, "time_criticality": 0.25, "risk_reduction": 0.25, "effort": 0.25 }, "rice": { "reach": 0.25, "impact": 0.25, "confidence": 0.25, "effort": 0.25 } } def load_debt_inventory(self, file_path: str) -> bool: """Load debt inventory from JSON file.""" try: with open(file_path, 'r', encoding='utf-8') as f: data = json.load(f) # Handle different input formats if isinstance(data, dict) and 'debt_items' in data: self.debt_items = data['debt_items'] elif isinstance(data, list): self.debt_items = data else: raise ValueError("Invalid debt inventory format") print(f"Loaded {len(self.debt_items)} debt items from {file_path}") return True except Exception as e: print(f"Error loading debt inventory: {e}") return False def analyze_and_prioritize(self, framework: str = "cost_of_delay") -> Dict[str, Any]: """ Analyze debt items and create prioritized backlog. Args: framework: Prioritization framework to use Returns: Dictionary containing prioritized backlog and analysis """ print(f"Analyzing {len(self.debt_items)} debt items...") print(f"Using {framework} prioritization framework") print("=" * 50) # Step 1: Enrich debt items with estimates enriched_items = [] for item in self.debt_items: enriched_item = self._enrich_debt_item(item) enriched_items.append(enriched_item) # Step 2: Calculate prioritization scores for item in enriched_items: if framework == "cost_of_delay": item["priority_score"] = self._calculate_cost_of_delay_score(item) elif framework == "wsjf": item["priority_score"] = self._calculate_wsjf_score(item) elif framework == "rice": item["priority_score"] = self._calculate_rice_score(item) else: raise ValueError(f"Unknown prioritization framework: {framework}") # Step 3: Sort by priority score self.prioritized_items = sorted(enriched_items, key=lambda x: x["priority_score"], reverse=True) # Step 4: Generate sprint allocation recommendations sprint_allocation = self._generate_sprint_allocation() # Step 5: Generate insights and recommendations insights = self._generate_insights() # Step 6: Create visualization data charts_data = self._generate_charts_data() return { "metadata": { "analysis_date": datetime.now().isoformat(), "framework_used": framework, "team_size": self.team_size, "sprint_capacity_hours": self.sprint_capacity_hours, "total_items_analyzed": len(self.debt_items) }, "prioritized_backlog": self.prioritized_items, "sprint_allocation": sprint_allocation, "insights": insights, "charts_data": charts_data, "recommendations": self._generate_recommendations() } def _enrich_debt_item(self, item: Dict[str, Any]) -> Dict[str, Any]: """Enrich debt item with detailed estimates and impact analysis.""" enriched = item.copy() # Generate effort estimate effort = self._estimate_effort(item) enriched["effort_estimate"] = asdict(effort) # Generate business impact assessment business_impact = self._assess_business_impact(item) enriched["business_impact"] = asdict(business_impact) # Calculate interest rate interest_rate = self._calculate_interest_rate(item, business_impact) enriched["interest_rate"] = asdict(interest_rate) # Calculate cost of delay enriched["cost_of_delay"] = self._calculate_cost_of_delay(interest_rate, effort) # Assign categories and tags enriched["category"] = self._categorize_debt_item(item) enriched["impact_tags"] = self._generate_impact_tags(item, business_impact) return enriched def _estimate_effort(self, item: Dict[str, Any]) -> EffortEstimate: """Estimate effort required to fix debt item.""" debt_type = item.get("type", "unknown") severity = item.get("severity", "medium") # Base effort estimation by debt type base_efforts = { "todo_comment": (1, 2), "missing_docstring": (1, 4), "long_line": (0.5, 1), "large_function": (4, 16), "high_complexity": (8, 32), "duplicate_code": (6, 24), "large_file": (16, 64), "syntax_error": (2, 8), "security_risk": (4, 40), "architecture_debt": (40, 160), "test_debt": (8, 40), "dependency_debt": (4, 24) } min_hours, max_hours = base_efforts.get(debt_type, (4, 16)) # Adjust by severity severity_multipliers = { "low": 0.5, "medium": 1.0, "high": 1.5, "critical": 2.0 } multiplier = severity_multipliers.get(severity, 1.0) hours_estimate = (min_hours + max_hours) / 2 * multiplier # Convert to story points (assuming 6 hours per point) size_points = max(1, round(hours_estimate / 6)) # Determine risk factor risk_factor = 1.0 if debt_type in ["architecture_debt", "security_risk", "large_file"]: risk_factor = 1.8 elif debt_type in ["high_complexity", "duplicate_code"]: risk_factor = 1.4 elif debt_type in ["syntax_error", "dependency_debt"]: risk_factor = 1.2 # Determine skill level required skill_requirements = { "architecture_debt": "expert", "security_risk": "senior", "high_complexity": "senior", "large_function": "mid", "duplicate_code": "mid", "dependency_debt": "mid", "test_debt": "mid", "todo_comment": "junior", "missing_docstring": "junior", "long_line": "junior" } skill_level = skill_requirements.get(debt_type, "mid") # Confidence based on debt type clarity confidence_levels = { "todo_comment": 0.9, "missing_docstring": 0.9, "long_line": 0.95, "syntax_error": 0.8, "large_function": 0.7, "duplicate_code": 0.6, "high_complexity": 0.5, "architecture_debt": 0.3, "security_risk": 0.4 } confidence = confidence_levels.get(debt_type, 0.6) return EffortEstimate( size_points=size_points, hours_estimate=hours_estimate, risk_factor=risk_factor, skill_level_required=skill_level, confidence=confidence ) def _assess_business_impact(self, item: Dict[str, Any]) -> BusinessImpact: """Assess business impact of debt item.""" debt_type = item.get("type", "unknown") severity = item.get("severity", "medium") # Base impact scores by debt type (1-10 scale) impact_profiles = { "security_risk": (9, 8, 7, 9, 10), # customer, revenue, velocity, quality, security "architecture_debt": (6, 7, 9, 8, 4), "large_function": (3, 4, 7, 6, 2), "high_complexity": (4, 5, 8, 7, 3), "duplicate_code": (3, 4, 6, 6, 2), "syntax_error": (7, 6, 8, 9, 3), "test_debt": (5, 5, 7, 8, 3), "dependency_debt": (6, 5, 6, 7, 7), "todo_comment": (1, 1, 2, 2, 1), "missing_docstring": (2, 2, 4, 3, 1) } base_impacts = impact_profiles.get(debt_type, (3, 3, 5, 5, 3)) # Adjust by severity severity_adjustments = { "low": 0.6, "medium": 1.0, "high": 1.4, "critical": 1.8 } adjustment = severity_adjustments.get(severity, 1.0) # Apply adjustment and cap at 10 adjusted_impacts = [min(10, max(1, round(impact * adjustment))) for impact in base_impacts] return BusinessImpact( customer_impact=adjusted_impacts[0], revenue_impact=adjusted_impacts[1], team_velocity_impact=adjusted_impacts[2], quality_impact=adjusted_impacts[3], security_impact=adjusted_impacts[4] ) def _calculate_interest_rate(self, item: Dict[str, Any], business_impact: BusinessImpact) -> InterestRate: """Calculate interest rate for technical debt.""" # Base daily cost calculation velocity_impact = business_impact.team_velocity_impact quality_impact = business_impact.quality_impact # Daily cost in "developer hours lost" daily_cost = (velocity_impact * 0.5) + (quality_impact * 0.3) # Frequency multiplier based on code location and type file_path = item.get("file_path", "") debt_type = item.get("type", "unknown") # Estimate frequency based on file path patterns frequency_multiplier = 1.0 if any(pattern in file_path.lower() for pattern in ["main", "core", "auth", "api"]): frequency_multiplier = 2.0 elif any(pattern in file_path.lower() for pattern in ["util", "helper", "common"]): frequency_multiplier = 1.5 elif any(pattern in file_path.lower() for pattern in ["test", "spec", "config"]): frequency_multiplier = 0.5 # Team impact multiplier team_impact_multiplier = min(self.team_size, 8) / 5.0 # Normalize around team of 5 # Compound rate - how this debt creates more debt compound_rates = { "architecture_debt": 0.1, # Creates 10% more debt monthly "duplicate_code": 0.08, "high_complexity": 0.05, "large_function": 0.03, "test_debt": 0.04, "security_risk": 0.02, # Doesn't compound much, but high initial impact "todo_comment": 0.01 } compound_rate = compound_rates.get(debt_type, 0.02) return InterestRate( daily_cost=daily_cost, frequency_multiplier=frequency_multiplier, team_impact_multiplier=team_impact_multiplier, compound_rate=compound_rate ) def _calculate_cost_of_delay(self, interest_rate: InterestRate, effort: EffortEstimate) -> float: """Calculate total cost of delay if debt is not fixed.""" # Estimate delay in days (assuming debt gets fixed eventually) estimated_delay_days = effort.hours_estimate / (self.sprint_capacity_hours / 14) # 2-week sprints # Calculate cumulative cost daily_cost = (interest_rate.daily_cost * interest_rate.frequency_multiplier * interest_rate.team_impact_multiplier) # Add compound interest effect compound_effect = (1 + interest_rate.compound_rate) ** (estimated_delay_days / 30) total_cost = daily_cost * estimated_delay_days * compound_effect return round(total_cost, 2) def _categorize_debt_item(self, item: Dict[str, Any]) -> str: """Categorize debt item into high-level categories.""" debt_type = item.get("type", "unknown") categories = { "code_quality": ["large_function", "high_complexity", "duplicate_code", "long_line", "missing_docstring"], "architecture": ["architecture_debt", "large_file"], "security": ["security_risk", "hardcoded_secrets"], "testing": ["test_debt", "missing_tests"], "maintenance": ["todo_comment", "commented_code"], "dependencies": ["dependency_debt", "outdated_packages"], "infrastructure": ["deployment_debt", "monitoring_gaps"], "documentation": ["missing_docstring", "outdated_docs"] } for category, types in categories.items(): if debt_type in types: return category return "other" def _generate_impact_tags(self, item: Dict[str, Any], business_impact: BusinessImpact) -> List[str]: """Generate impact tags for debt item.""" tags = [] if business_impact.security_impact >= 7: tags.append("security-critical") if business_impact.customer_impact >= 7: tags.append("customer-facing") if business_impact.revenue_impact >= 7: tags.append("revenue-impact") if business_impact.team_velocity_impact >= 7: tags.append("velocity-blocker") if business_impact.quality_impact >= 7: tags.append("quality-risk") # Add effort-based tags effort_hours = item.get("effort_estimate", {}).get("hours_estimate", 0) if effort_hours <= 4: tags.append("quick-win") elif effort_hours >= 40: tags.append("major-initiative") return tags def _calculate_cost_of_delay_score(self, item: Dict[str, Any]) -> float: """Calculate priority score using cost-of-delay framework.""" business_impact = item["business_impact"] effort = item["effort_estimate"] # Business value (weighted average of impacts) business_value = ( business_impact["customer_impact"] * 0.3 + business_impact["revenue_impact"] * 0.3 + business_impact["quality_impact"] * 0.2 + business_impact["team_velocity_impact"] * 0.2 ) # Urgency (how quickly value decreases) urgency = item["interest_rate"]["daily_cost"] * 10 # Scale to 1-10 urgency = min(10, max(1, urgency)) # Risk reduction risk_reduction = business_impact["security_impact"] * 0.6 + business_impact["quality_impact"] * 0.4 # Team productivity impact team_productivity = business_impact["team_velocity_impact"] # Combine with weights weights = self.framework_weights["cost_of_delay"] numerator = ( business_value * weights["business_value"] + urgency * weights["urgency"] + risk_reduction * weights["risk_reduction"] + team_productivity * weights["team_productivity"] ) # Divide by effort (adjusted for risk) effort_adjusted = effort["hours_estimate"] * effort["risk_factor"] denominator = max(1, effort_adjusted / 8) # Normalize to story points return round(numerator / denominator, 2) def _calculate_wsjf_score(self, item: Dict[str, Any]) -> float: """Calculate priority score using Weighted Shortest Job First (WSJF).""" business_impact = item["business_impact"] effort = item["effort_estimate"] # Business value business_value = ( business_impact["customer_impact"] * 0.4 + business_impact["revenue_impact"] * 0.6 ) # Time criticality time_criticality = item["cost_of_delay"] / 10 # Normalize time_criticality = min(10, max(1, time_criticality)) # Risk reduction risk_reduction = ( business_impact["security_impact"] * 0.5 + business_impact["quality_impact"] * 0.5 ) # Job size (effort) job_size = effort["size_points"] # WSJF calculation numerator = business_value + time_criticality + risk_reduction denominator = max(1, job_size) return round(numerator / denominator, 2) def _calculate_rice_score(self, item: Dict[str, Any]) -> float: """Calculate priority score using RICE framework.""" business_impact = item["business_impact"] effort = item["effort_estimate"] # Reach (how many developers/users affected) reach = min(10, self.team_size * business_impact["team_velocity_impact"] / 5) # Impact impact = ( business_impact["customer_impact"] * 0.3 + business_impact["revenue_impact"] * 0.3 + business_impact["quality_impact"] * 0.4 ) # Confidence confidence = effort["confidence"] * 10 # Effort effort_score = effort["size_points"] # RICE calculation rice_score = (reach * impact * confidence) / max(1, effort_score) return round(rice_score, 2) def _generate_sprint_allocation(self) -> Dict[str, Any]: """Generate sprint allocation recommendations.""" # Calculate total effort needed total_effort_hours = sum(item["effort_estimate"]["hours_estimate"] for item in self.prioritized_items) # Assume 20% of sprint capacity goes to tech debt debt_capacity_per_sprint = self.sprint_capacity_hours * 0.2 # Allocate items to sprints sprints = [] current_sprint = {"sprint_number": 1, "items": [], "total_hours": 0, "capacity_used": 0} for item in self.prioritized_items: item_effort = item["effort_estimate"]["hours_estimate"] if current_sprint["total_hours"] + item_effort <= debt_capacity_per_sprint: current_sprint["items"].append(item) current_sprint["total_hours"] += item_effort current_sprint["capacity_used"] = current_sprint["total_hours"] / debt_capacity_per_sprint else: # Start new sprint sprints.append(current_sprint) current_sprint = { "sprint_number": len(sprints) + 1, "items": [item], "total_hours": item_effort, "capacity_used": item_effort / debt_capacity_per_sprint } # Add the last sprint if current_sprint["items"]: sprints.append(current_sprint) # Calculate summary statistics total_sprints_needed = len(sprints) high_priority_items = len([item for item in self.prioritized_items if item.get("priority", "medium") in ["high", "critical"]]) return { "total_debt_hours": round(total_effort_hours, 1), "debt_capacity_per_sprint": debt_capacity_per_sprint, "total_sprints_needed": total_sprints_needed, "high_priority_items": high_priority_items, "sprint_plan": sprints[:6], # Show first 6 sprints "recommendations": [ f"Allocate {debt_capacity_per_sprint} hours per sprint to tech debt", f"Focus on {high_priority_items} high-priority items first", f"Estimated {total_sprints_needed} sprints to clear current backlog" ] } def _generate_insights(self) -> Dict[str, Any]: """Generate insights from the prioritized debt analysis.""" # Category distribution categories = Counter(item["category"] for item in self.prioritized_items) # Effort distribution total_effort = sum(item["effort_estimate"]["hours_estimate"] for item in self.prioritized_items) effort_by_category = defaultdict(float) for item in self.prioritized_items: effort_by_category[item["category"]] += item["effort_estimate"]["hours_estimate"] # Priority distribution priorities = Counter() for item in self.prioritized_items: score = item["priority_score"] if score >= 8: priorities["critical"] += 1 elif score >= 5: priorities["high"] += 1 elif score >= 2: priorities["medium"] += 1 else: priorities["low"] += 1 # Risk analysis high_risk_items = [item for item in self.prioritized_items if item["effort_estimate"]["risk_factor"] >= 1.5] # Quick wins identification quick_wins = [item for item in self.prioritized_items if (item["effort_estimate"]["hours_estimate"] <= 8 and item["priority_score"] >= 3)] # Cost analysis total_cost_of_delay = sum(item["cost_of_delay"] for item in self.prioritized_items) avg_interest_rate = sum(item["interest_rate"]["daily_cost"] for item in self.prioritized_items) / len(self.prioritized_items) return { "category_distribution": dict(categories), "total_effort_hours": round(total_effort, 1), "effort_by_category": {k: round(v, 1) for k, v in effort_by_category.items()}, "priority_distribution": dict(priorities), "high_risk_items_count": len(high_risk_items), "quick_wins_count": len(quick_wins), "total_cost_of_delay": round(total_cost_of_delay, 1), "average_daily_interest_rate": round(avg_interest_rate, 2), "top_categories_by_effort": sorted(effort_by_category.items(), key=lambda x: x[1], reverse=True)[:3] } def _generate_charts_data(self) -> Dict[str, Any]: """Generate data for charts and visualizations.""" # Priority vs Effort scatter plot data scatter_data = [] for item in self.prioritized_items: scatter_data.append({ "x": item["effort_estimate"]["hours_estimate"], "y": item["priority_score"], "label": item.get("description", "")[:50], "category": item["category"], "size": item["cost_of_delay"] }) # Category effort distribution (pie chart) effort_by_category = defaultdict(float) for item in self.prioritized_items: effort_by_category[item["category"]] += item["effort_estimate"]["hours_estimate"] pie_data = [{"category": k, "effort": round(v, 1)} for k, v in effort_by_category.items()] # Priority timeline (bar chart) timeline_data = [] cumulative_effort = 0 for i, item in enumerate(self.prioritized_items[:20]): # Top 20 items cumulative_effort += item["effort_estimate"]["hours_estimate"] timeline_data.append({ "item_rank": i + 1, "description": item.get("description", "")[:30], "effort": item["effort_estimate"]["hours_estimate"], "cumulative_effort": round(cumulative_effort, 1), "priority_score": item["priority_score"] }) # Interest rate trend (line chart data structure) interest_trend_data = [] for i, item in enumerate(self.prioritized_items): interest_trend_data.append({ "item_index": i, "daily_cost": item["interest_rate"]["daily_cost"], "category": item["category"] }) return { "priority_effort_scatter": scatter_data, "category_effort_distribution": pie_data, "priority_timeline": timeline_data, "interest_rate_trend": interest_trend_data[:50] # Limit for performance } def _generate_recommendations(self) -> List[str]: """Generate actionable recommendations based on analysis.""" recommendations = [] insights = self._generate_insights() # Quick wins recommendation if insights["quick_wins_count"] > 0: recommendations.append( f"Start with {insights['quick_wins_count']} quick wins to build momentum " "and demonstrate immediate value from tech debt reduction efforts." ) # High-risk items if insights["high_risk_items_count"] > 5: recommendations.append( f"Plan careful execution for {insights['high_risk_items_count']} high-risk items. " "Consider pair programming, extra testing, and incremental approaches." ) # Category focus top_category = insights["top_categories_by_effort"][0][0] recommendations.append( f"Focus initial efforts on '{top_category}' category debt, which represents " f"the largest effort investment ({insights['top_categories_by_effort'][0][1]:.1f} hours)." ) # Cost of delay urgency if insights["average_daily_interest_rate"] > 5: recommendations.append( f"High average daily interest rate ({insights['average_daily_interest_rate']:.1f}) " "suggests urgent action needed. Consider increasing tech debt budget allocation." ) # Sprint planning sprints_needed = len(self.prioritized_items) / 10 # Rough estimate if sprints_needed > 12: recommendations.append( "Large debt backlog detected. Consider dedicating entire sprints to debt reduction " "rather than trying to fit debt work around features." ) # Team capacity total_effort = insights["total_effort_hours"] weeks_needed = total_effort / (self.sprint_capacity_hours * 0.2) if weeks_needed > 26: # Half a year recommendations.append( f"With current capacity allocation, debt backlog will take {weeks_needed:.0f} weeks. " "Consider increasing tech debt budget or focusing on highest-impact items only." ) return recommendations def format_prioritized_report(analysis_result: Dict[str, Any]) -> str: """Format the prioritization analysis in human-readable format.""" output = [] # Header output.append("=" * 60) output.append("TECHNICAL DEBT PRIORITIZATION REPORT") output.append("=" * 60) metadata = analysis_result["metadata"] output.append(f"Analysis Date: {metadata['analysis_date']}") output.append(f"Framework: {metadata['framework_used'].upper()}") output.append(f"Team Size: {metadata['team_size']}") output.append(f"Sprint Capacity: {metadata['sprint_capacity_hours']} hours") output.append("") # Executive Summary insights = analysis_result["insights"] output.append("EXECUTIVE SUMMARY") output.append("-" * 30) output.append(f"Total Debt Items: {metadata['total_items_analyzed']}") output.append(f"Total Effort Required: {insights['total_effort_hours']} hours") output.append(f"Total Cost of Delay: ${insights['total_cost_of_delay']:,.0f}") output.append(f"Quick Wins Available: {insights['quick_wins_count']}") output.append(f"High-Risk Items: {insights['high_risk_items_count']}") output.append("") # Sprint Plan sprint_plan = analysis_result["sprint_allocation"] output.append("SPRINT ALLOCATION PLAN") output.append("-" * 30) output.append(f"Sprints Needed: {sprint_plan['total_sprints_needed']}") output.append(f"Hours per Sprint: {sprint_plan['debt_capacity_per_sprint']}") output.append("") for sprint in sprint_plan["sprint_plan"][:3]: # Show first 3 sprints output.append(f"Sprint {sprint['sprint_number']} ({sprint['capacity_used']:.0%} capacity):") for item in sprint["items"][:3]: # Top 3 items per sprint output.append(f" • {item['description'][:50]}...") output.append(f" Effort: {item['effort_estimate']['hours_estimate']:.1f}h, " f"Priority: {item['priority_score']}") output.append("") # Top Priority Items output.append("TOP 10 PRIORITY ITEMS") output.append("-" * 30) for i, item in enumerate(analysis_result["prioritized_backlog"][:10], 1): output.append(f"{i}. [{item['priority_score']:.1f}] {item['description']}") output.append(f" Category: {item['category']}, " f"Effort: {item['effort_estimate']['hours_estimate']:.1f}h, " f"Cost of Delay: ${item['cost_of_delay']:.0f}") if item["impact_tags"]: output.append(f" Tags: {', '.join(item['impact_tags'])}") output.append("") # Recommendations output.append("RECOMMENDATIONS") output.append("-" * 30) for i, rec in enumerate(analysis_result["recommendations"], 1): output.append(f"{i}. {rec}") output.append("") return "\n".join(output) def main(): """Main entry point for the debt prioritizer.""" parser = argparse.ArgumentParser(description="Prioritize technical debt backlog") parser.add_argument("inventory_file", help="Path to debt inventory JSON file") parser.add_argument("--output", help="Output file path") parser.add_argument("--format", choices=["json", "text", "both"], default="both", help="Output format") parser.add_argument("--framework", choices=["cost_of_delay", "wsjf", "rice"], default="cost_of_delay", help="Prioritization framework") parser.add_argument("--team-size", type=int, default=5, help="Team size") parser.add_argument("--sprint-capacity", type=int, default=80, help="Sprint capacity in hours") args = parser.parse_args() # Initialize prioritizer prioritizer = DebtPrioritizer(args.team_size, args.sprint_capacity) # Load inventory if not prioritizer.load_debt_inventory(args.inventory_file): sys.exit(1) # Analyze and prioritize try: analysis_result = prioritizer.analyze_and_prioritize(args.framework) except Exception as e: print(f"Analysis failed: {e}") sys.exit(1) # Output results if args.format in ["json", "both"]: json_output = json.dumps(analysis_result, indent=2, default=str) if args.output: output_path = args.output if args.output.endswith('.json') else f"{args.output}.json" with open(output_path, 'w') as f: f.write(json_output) print(f"JSON report written to: {output_path}") else: print("JSON REPORT:") print("=" * 50) print(json_output) if args.format in ["text", "both"]: text_output = format_prioritized_report(analysis_result) if args.output: output_path = args.output if args.output.endswith('.txt') else f"{args.output}.txt" with open(output_path, 'w') as f: f.write(text_output) print(f"Text report written to: {output_path}") else: print("\nTEXT REPORT:") print("=" * 50) print(text_output) if __name__ == "__main__": main() FILE:scripts/debt_scanner.py #!/usr/bin/env python3 """ Tech Debt Scanner Scans a codebase directory for tech debt signals using AST parsing (Python) and regex patterns (any language). Detects various forms of technical debt and generates both JSON inventory and human-readable reports. Usage: python debt_scanner.py /path/to/codebase python debt_scanner.py /path/to/codebase --config config.json python debt_scanner.py /path/to/codebase --output report.json --format both """ import ast import json import argparse import os import re import sys from collections import defaultdict, Counter from datetime import datetime from pathlib import Path from typing import Dict, List, Any, Optional, Set, Tuple class DebtScanner: """Main scanner class for detecting technical debt in codebases.""" def __init__(self, config: Optional[Dict[str, Any]] = None): self.config = self._load_default_config() if config: self.config.update(config) self.debt_items = [] self.stats = defaultdict(int) self.file_stats = {} # Compile regex patterns for performance self._compile_patterns() def _load_default_config(self) -> Dict[str, Any]: """Load default configuration for debt detection.""" return { "max_function_length": 50, "max_complexity": 10, "max_nesting_depth": 4, "max_file_size_lines": 500, "min_duplicate_lines": 3, "ignore_patterns": [ "*.pyc", "__pycache__", ".git", ".svn", "node_modules", "build", "dist", "*.min.js", "*.map" ], "file_extensions": { "python": [".py"], "javascript": [".js", ".jsx", ".ts", ".tsx"], "java": [".java"], "csharp": [".cs"], "cpp": [".cpp", ".cc", ".cxx", ".c", ".h", ".hpp"], "ruby": [".rb"], "php": [".php"], "go": [".go"], "rust": [".rs"], "kotlin": [".kt"] }, "comment_patterns": { "todo": r"(?i)(TODO|FIXME|HACK|XXX|BUG)[\s:]*(.+)", "commented_code": r"^\s*#.*[=(){}\[\];].*", "magic_numbers": r"\b\d{2,}\b", "long_strings": r'["\'](.{100,})["\']' }, "severity_weights": { "critical": 10, "high": 7, "medium": 5, "low": 2, "info": 1 } } def _compile_patterns(self): """Compile regex patterns for better performance.""" self.comment_regexes = {} for name, pattern in self.config["comment_patterns"].items(): self.comment_regexes[name] = re.compile(pattern) # Common code smells patterns self.smell_patterns = { "empty_catch": re.compile(r"except[^:]*:\s*pass\s*$", re.MULTILINE), "print_debug": re.compile(r"print\s*$[^)]*debug[^)]*$", re.IGNORECASE), "hardcoded_paths": re.compile(r'["\'][/\\][^"\']*[/\\][^"\']*["\']'), "sql_injection_risk": re.compile(r'["\'].*%s.*["\'].*execute', re.IGNORECASE), } def scan_directory(self, directory: str) -> Dict[str, Any]: """ Scan a directory for tech debt. Args: directory: Path to the directory to scan Returns: Dictionary containing debt inventory and statistics """ directory_path = Path(directory) if not directory_path.exists(): raise ValueError(f"Directory does not exist: {directory}") print(f"Scanning directory: {directory}") print("=" * 50) # Reset state self.debt_items = [] self.stats = defaultdict(int) self.file_stats = {} # Walk through directory for root, dirs, files in os.walk(directory): # Filter out ignored directories dirs[:] = [d for d in dirs if not self._should_ignore(d)] for file in files: if self._should_ignore(file): continue file_path = os.path.join(root, file) relative_path = os.path.relpath(file_path, directory) try: self._scan_file(file_path, relative_path) except Exception as e: print(f"Error scanning {relative_path}: {e}") self.stats["scan_errors"] += 1 # Post-process results self._detect_duplicates(directory) self._calculate_priorities() return self._generate_report(directory) def _should_ignore(self, name: str) -> bool: """Check if file/directory should be ignored.""" for pattern in self.config["ignore_patterns"]: if "*" in pattern: if re.match(pattern.replace("*", ".*"), name): return True elif pattern in name: return True return False def _scan_file(self, file_path: str, relative_path: str): """Scan a single file for tech debt.""" try: with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: content = f.read() lines = content.splitlines() except Exception as e: print(f"Cannot read {relative_path}: {e}") return file_ext = Path(file_path).suffix.lower() file_info = { "path": relative_path, "lines": len(lines), "size_kb": os.path.getsize(file_path) / 1024, "language": self._detect_language(file_ext), "debt_count": 0 } self.stats["files_scanned"] += 1 self.stats["total_lines"] += len(lines) # File size debt if len(lines) > self.config["max_file_size_lines"]: self._add_debt_item( "large_file", f"File is too large: {len(lines)} lines", relative_path, "medium", {"lines": len(lines), "recommended_max": self.config["max_file_size_lines"]} ) file_info["debt_count"] += 1 # Language-specific analysis if file_info["language"] == "python" and file_ext == ".py": self._scan_python_file(relative_path, content, lines) else: self._scan_generic_file(relative_path, content, lines, file_info["language"]) # Common patterns for all languages self._scan_common_patterns(relative_path, content, lines) self.file_stats[relative_path] = file_info def _detect_language(self, file_ext: str) -> str: """Detect programming language from file extension.""" for lang, extensions in self.config["file_extensions"].items(): if file_ext in extensions: return lang return "unknown" def _scan_python_file(self, file_path: str, content: str, lines: List[str]): """Scan Python files using AST parsing.""" try: tree = ast.parse(content) analyzer = PythonASTAnalyzer(self.config) debt_items = analyzer.analyze(tree, file_path, lines) self.debt_items.extend(debt_items) self.stats["python_files"] += 1 except SyntaxError as e: self._add_debt_item( "syntax_error", f"Python syntax error: {e}", file_path, "high", {"line": e.lineno, "error": str(e)} ) def _scan_generic_file(self, file_path: str, content: str, lines: List[str], language: str): """Scan non-Python files using pattern matching.""" # Detect long lines for i, line in enumerate(lines): if len(line) > 120: self._add_debt_item( "long_line", f"Line too long: {len(line)} characters", file_path, "low", {"line_number": i + 1, "length": len(line)} ) # Detect deep nesting (approximate) for i, line in enumerate(lines): indent_level = len(line) - len(line.lstrip()) if language in ["python"]: indent_level = indent_level // 4 # Python uses 4-space indents elif language in ["javascript", "java", "csharp", "cpp"]: # Count braces for brace-based languages brace_level = content[:content.find('\n'.join(lines[:i+1]))].count('{') - content[:content.find('\n'.join(lines[:i+1]))].count('}') if brace_level > self.config["max_nesting_depth"]: self._add_debt_item( "deep_nesting", f"Deep nesting detected: {brace_level} levels", file_path, "medium", {"line_number": i + 1, "nesting_level": brace_level} ) def _scan_common_patterns(self, file_path: str, content: str, lines: List[str]): """Scan for common patterns across all file types.""" # TODO/FIXME comments for i, line in enumerate(lines): for pattern_name, regex in self.comment_regexes.items(): match = regex.search(line) if match: if pattern_name == "todo": self._add_debt_item( "todo_comment", f"TODO/FIXME comment: {match.group(0)}", file_path, "low", {"line_number": i + 1, "comment": match.group(0).strip()} ) # Code smells for smell_name, pattern in self.smell_patterns.items(): matches = pattern.finditer(content) for match in matches: line_num = content[:match.start()].count('\n') + 1 self._add_debt_item( smell_name, f"Code smell detected: {smell_name}", file_path, "medium", {"line_number": line_num, "pattern": match.group(0)[:100]} ) def _detect_duplicates(self, directory: str): """Detect duplicate code blocks across files.""" # Simple duplicate detection based on exact line matches line_hashes = defaultdict(list) for file_path, file_info in self.file_stats.items(): try: full_path = os.path.join(directory, file_path) with open(full_path, 'r', encoding='utf-8', errors='ignore') as f: lines = f.readlines() for i in range(len(lines) - self.config["min_duplicate_lines"] + 1): block = ''.join(lines[i:i + self.config["min_duplicate_lines"]]) block_hash = hash(block.strip()) if len(block.strip()) > 50: # Only consider substantial blocks line_hashes[block_hash].append((file_path, i + 1, block)) except Exception: continue # Report duplicates for block_hash, occurrences in line_hashes.items(): if len(occurrences) > 1: for file_path, line_num, block in occurrences: self._add_debt_item( "duplicate_code", f"Duplicate code block found in {len(occurrences)} files", file_path, "medium", { "line_number": line_num, "duplicate_count": len(occurrences), "other_files": [f[0] for f in occurrences if f[0] != file_path] } ) def _calculate_priorities(self): """Calculate priority scores for debt items.""" severity_weights = self.config["severity_weights"] for item in self.debt_items: base_score = severity_weights.get(item["severity"], 1) # Adjust based on debt type type_multipliers = { "syntax_error": 2.0, "security_risk": 1.8, "large_function": 1.5, "high_complexity": 1.4, "duplicate_code": 1.3, "todo_comment": 0.5 } multiplier = type_multipliers.get(item["type"], 1.0) item["priority_score"] = int(base_score * multiplier) # Set priority category if item["priority_score"] >= 15: item["priority"] = "critical" elif item["priority_score"] >= 10: item["priority"] = "high" elif item["priority_score"] >= 5: item["priority"] = "medium" else: item["priority"] = "low" def _add_debt_item(self, debt_type: str, description: str, file_path: str, severity: str, metadata: Dict[str, Any]): """Add a debt item to the inventory.""" item = { "id": f"DEBT-{len(self.debt_items) + 1:04d}", "type": debt_type, "description": description, "file_path": file_path, "severity": severity, "metadata": metadata, "detected_date": datetime.now().isoformat(), "status": "identified" } self.debt_items.append(item) self.stats[f"debt_{debt_type}"] += 1 self.stats["total_debt_items"] += 1 if file_path in self.file_stats: self.file_stats[file_path]["debt_count"] += 1 def _generate_report(self, directory: str) -> Dict[str, Any]: """Generate the final debt report.""" # Sort debt items by priority score self.debt_items.sort(key=lambda x: x.get("priority_score", 0), reverse=True) # Calculate summary statistics priority_counts = Counter(item["priority"] for item in self.debt_items) type_counts = Counter(item["type"] for item in self.debt_items) # Calculate health score (0-100, higher is better) total_files = self.stats.get("files_scanned", 1) debt_density = len(self.debt_items) / total_files health_score = max(0, 100 - (debt_density * 10)) report = { "scan_metadata": { "directory": directory, "scan_date": datetime.now().isoformat(), "scanner_version": "1.0.0", "config": self.config }, "summary": { "total_files_scanned": self.stats.get("files_scanned", 0), "total_lines_scanned": self.stats.get("total_lines", 0), "total_debt_items": len(self.debt_items), "health_score": round(health_score, 1), "debt_density": round(debt_density, 2), "priority_breakdown": dict(priority_counts), "type_breakdown": dict(type_counts) }, "debt_items": self.debt_items, "file_statistics": self.file_stats, "recommendations": self._generate_recommendations() } return report def _generate_recommendations(self) -> List[str]: """Generate actionable recommendations based on findings.""" recommendations = [] # Priority-based recommendations high_priority_count = len([item for item in self.debt_items if item.get("priority") in ["critical", "high"]]) if high_priority_count > 10: recommendations.append( f"Address {high_priority_count} high-priority debt items immediately - " "they pose significant risk to code quality and maintainability." ) # Type-specific recommendations type_counts = Counter(item["type"] for item in self.debt_items) if type_counts.get("large_function", 0) > 5: recommendations.append( "Consider refactoring large functions into smaller, more focused units. " "This will improve readability and testability." ) if type_counts.get("duplicate_code", 0) > 3: recommendations.append( "Extract duplicate code into reusable functions or modules. " "This reduces maintenance burden and potential for inconsistent changes." ) if type_counts.get("todo_comment", 0) > 20: recommendations.append( "Review and address TODO/FIXME comments. Consider creating proper " "tickets for substantial work items." ) # General recommendations total_files = self.stats.get("files_scanned", 1) if len(self.debt_items) / total_files > 2: recommendations.append( "High debt density detected. Consider establishing coding standards " "and regular code review processes to prevent debt accumulation." ) if not recommendations: recommendations.append("Code quality looks good! Continue current practices.") return recommendations class PythonASTAnalyzer(ast.NodeVisitor): """AST analyzer for Python-specific debt detection.""" def __init__(self, config: Dict[str, Any]): self.config = config self.debt_items = [] self.current_file = "" self.lines = [] self.function_stack = [] def analyze(self, tree: ast.AST, file_path: str, lines: List[str]) -> List[Dict[str, Any]]: """Analyze Python AST for tech debt.""" self.debt_items = [] self.current_file = file_path self.lines = lines self.function_stack = [] self.visit(tree) return self.debt_items def visit_FunctionDef(self, node: ast.FunctionDef): """Analyze function definitions.""" self.function_stack.append(node.name) # Calculate function length func_length = node.end_lineno - node.lineno + 1 if func_length > self.config["max_function_length"]: self._add_debt( "large_function", f"Function '{node.name}' is too long: {func_length} lines", node.lineno, "medium", {"function_name": node.name, "length": func_length} ) # Check for missing docstring if not ast.get_docstring(node): self._add_debt( "missing_docstring", f"Function '{node.name}' missing docstring", node.lineno, "low", {"function_name": node.name} ) # Calculate cyclomatic complexity complexity = self._calculate_complexity(node) if complexity > self.config["max_complexity"]: self._add_debt( "high_complexity", f"Function '{node.name}' has high complexity: {complexity}", node.lineno, "high", {"function_name": node.name, "complexity": complexity} ) # Check parameter count param_count = len(node.args.args) if param_count > 5: self._add_debt( "too_many_parameters", f"Function '{node.name}' has too many parameters: {param_count}", node.lineno, "medium", {"function_name": node.name, "parameter_count": param_count} ) self.generic_visit(node) self.function_stack.pop() def visit_ClassDef(self, node: ast.ClassDef): """Analyze class definitions.""" # Check for missing docstring if not ast.get_docstring(node): self._add_debt( "missing_docstring", f"Class '{node.name}' missing docstring", node.lineno, "low", {"class_name": node.name} ) # Check for too many methods methods = [n for n in node.body if isinstance(n, ast.FunctionDef)] if len(methods) > 20: self._add_debt( "large_class", f"Class '{node.name}' has too many methods: {len(methods)}", node.lineno, "medium", {"class_name": node.name, "method_count": len(methods)} ) self.generic_visit(node) def _calculate_complexity(self, node: ast.FunctionDef) -> int: """Calculate cyclomatic complexity of a function.""" complexity = 1 # Base complexity for child in ast.walk(node): if isinstance(child, (ast.If, ast.While, ast.For, ast.AsyncFor)): complexity += 1 elif isinstance(child, ast.ExceptHandler): complexity += 1 elif isinstance(child, ast.BoolOp): complexity += len(child.values) - 1 return complexity def _add_debt(self, debt_type: str, description: str, line_number: int, severity: str, metadata: Dict[str, Any]): """Add a debt item to the collection.""" item = { "id": f"DEBT-{len(self.debt_items) + 1:04d}", "type": debt_type, "description": description, "file_path": self.current_file, "line_number": line_number, "severity": severity, "metadata": metadata, "detected_date": datetime.now().isoformat(), "status": "identified" } self.debt_items.append(item) def format_human_readable_report(report: Dict[str, Any]) -> str: """Format the report in human-readable format.""" output = [] # Header output.append("=" * 60) output.append("TECHNICAL DEBT SCAN REPORT") output.append("=" * 60) output.append(f"Directory: {report['scan_metadata']['directory']}") output.append(f"Scan Date: {report['scan_metadata']['scan_date']}") output.append(f"Scanner Version: {report['scan_metadata']['scanner_version']}") output.append("") # Summary summary = report["summary"] output.append("SUMMARY") output.append("-" * 30) output.append(f"Files Scanned: {summary['total_files_scanned']}") output.append(f"Lines Scanned: {summary['total_lines_scanned']:,}") output.append(f"Total Debt Items: {summary['total_debt_items']}") output.append(f"Health Score: {summary['health_score']}/100") output.append(f"Debt Density: {summary['debt_density']} items/file") output.append("") # Priority breakdown output.append("PRIORITY BREAKDOWN") output.append("-" * 30) for priority, count in summary["priority_breakdown"].items(): output.append(f"{priority.capitalize()}: {count}") output.append("") # Top debt items output.append("TOP DEBT ITEMS") output.append("-" * 30) top_items = report["debt_items"][:10] for i, item in enumerate(top_items, 1): output.append(f"{i}. [{item['priority'].upper()}] {item['description']}") output.append(f" File: {item['file_path']}") if 'line_number' in item: output.append(f" Line: {item['line_number']}") output.append("") # Recommendations output.append("RECOMMENDATIONS") output.append("-" * 30) for i, rec in enumerate(report["recommendations"], 1): output.append(f"{i}. {rec}") output.append("") return "\n".join(output) def main(): """Main entry point for the debt scanner.""" parser = argparse.ArgumentParser(description="Scan codebase for technical debt") parser.add_argument("directory", help="Directory to scan") parser.add_argument("--config", help="Configuration file (JSON)") parser.add_argument("--output", help="Output file path") parser.add_argument("--format", choices=["json", "text", "both"], default="both", help="Output format") args = parser.parse_args() # Load configuration config = None if args.config: try: with open(args.config, 'r') as f: config = json.load(f) except Exception as e: print(f"Error loading config: {e}") sys.exit(1) # Run scan scanner = DebtScanner(config) try: report = scanner.scan_directory(args.directory) except Exception as e: print(f"Scan failed: {e}") sys.exit(1) # Output results if args.format in ["json", "both"]: json_output = json.dumps(report, indent=2, default=str) if args.output: output_path = args.output if args.output.endswith('.json') else f"{args.output}.json" with open(output_path, 'w') as f: f.write(json_output) print(f"JSON report written to: {output_path}") else: print("\nJSON REPORT:") print("=" * 50) print(json_output) if args.format in ["text", "both"]: text_output = format_human_readable_report(report) if args.output: output_path = args.output if args.output.endswith('.txt') else f"{args.output}.txt" with open(output_path, 'w') as f: f.write(text_output) print(f"Text report written to: {output_path}") else: print("\nTEXT REPORT:") print("=" * 50) print(text_output) if __name__ == "__main__": main()

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