forensic-hotspot-finder

Code hotspots are identified by calculating a risk score that multiplies normalized change frequency by a complexity factor. This analysis applies to Git history across modules to surface high-risk files for targeted refactoring and quality improvements.

A code hotspot risk assessment combines change frequency with code complexity to highlight high-risk files. You need it during sprint planning to focus efforts on files most likely to harbor defects based on research-backed benchmarks.

Prioritize refactoring by scanning your repository to generate a ranked list of hotspot candidates. The workflow normalizes change frequency and complexity factors to a 0-1 scale, providing prioritization guidance for sprint planning.

Yes, forensic-hotspot-finder supports integration with ownership, debt-quantification, and complexity-trends analyses. After outputting a ranked list of hotspot candidates, you integrate with other forensic skills for these downstream analyses.

Hotspot analysis normalizes change frequency and complexity factors to a 0-1 scale. The risk score multiplies these normalized factors together, ensuring balanced weighting when surfacing high-risk files across different modules.

Change frequency alone misses structural complexity, leading to incomplete risk assessment. The hotspot approach multiplies normalized change frequency by a complexity factor, ensuring files that are both frequently changed and structurally complex surface as high-risk.