Tech Debt Tracker

Technical debt identification uses automated scanners with AST parsing and regex to find large functions, complexity, and security risks. Prioritization frameworks like Cost of Delay and WSJF then rank these debt items by business impact and effort.

Automated scanning detects various technical debt types including architectural debt, testing gaps, documentation deficiencies, dependency risks, and infrastructure debt, alongside specific code quality issues like large functions and security vulnerabilities.

To track technical debt trends over time, you run periodic codebase scans that log historical debt metrics. This trend analysis monitors progression, shows remediation progress, and forecasts future impact on developer productivity.

Technical debt tracking supports agile processes by generating a prioritized backlog of debt items based on Cost of Delay. Teams allocate specific items to their next sprint, balancing feature delivery with software maintenance to improve code quality.

AST parsing supports refactoring large functions and architectural debt by analyzing source code structure to detect complexity and security risks. It identifies specific refactoring targets to reduce development friction and improve codebase maintainability.

Managing dependency risks and infrastructure debt requires systematic scanning to identify vulnerabilities and documentation deficiencies. Utilizing prioritization frameworks like WSJF calculates their Cost of Delay to rank remediation efforts against business impact.