tech-debt

To identify technical debt, you can scan your codebase to detect indicators like TODOs, FIXMEs, large file sizes, long functions, and outdated dependency versions. The scanning process categorizes these items into a central register for tracking.

The best way to prioritize technical debt is to score each item based on its impact, required effort, and frequency. This generates actionable insights and a prioritized backlog to guide your repayment strategy during sprint planning.

Automated technical debt detection works by analyzing code comments, file sizes, function lengths, and dependency versions against predefined indicators. It automatically flags common issues like FIXMEs and code complexity to populate a debt register.

Yes, you can manually add technical debt items to the register. The system supports both automated debt detection and manual entry, allowing development teams to maintain a comprehensive, categorized list of all identified issues.

Tracking technical debt does not require specific external dependencies, but the analysis itself evaluates your project's dependency versions. It scans for outdated dependencies as a key indicator of maintainability issues within your codebase.