technical-debt-visualizer

To identify technical debt hotspots, aggregate code complexity, test coverage gaps, and git change frequency to visualize high-risk files. This generates a heatmap report highlighting modules that need targeted refactoring during sprint planning.

High-risk technical debt is indicated by aggregating code complexity, test coverage gaps, and change frequency. Files with high complexity, lacking tests, and frequently modified in git history represent the most critical hotspots requiring immediate refactoring.

Yes, you can analyze code complexity and git history without executing code by performing static analysis of code structure and historical data. This approach safely evaluates codebase health for architectural reviews and due diligence.

Prioritize refactoring targets by scanning the repository for files that maximize impact when debt is reduced. The analysis suggests specific files based on aggregated complexity and change frequency metrics to guide sprint planning.

The best way to assess codebase health before an acquisition is running static analysis to evaluate complexity, test coverage, and git change frequency. This due diligence process highlights architectural risks and high-interest technical debt hotspots.

Yes, technical debt analysis works for architectural reviews across repositories by aggregating complexity and change frequency metrics. It evaluates structural health and coverage gaps without executing code, making it suitable for large-scale architectural assessments.