provides a data-driven view of software quality. It helps engineering leaders and developers prioritize refactoring work by identifying files that are both complex and frequently changed—the "high-interest" technical debt.
When to Use
Sprint Planning
Run this before a dedicated refactoring sprint to identify the best "ROI" targets.
Architectural Reviews
Use to visualize the impact of legacy systems on the overall codebase health.
Due Diligence
Quickly assess the health of a new or acquired repository.
When NOT to Use
Performance Benchmarking
This tool measures code
structure
and
maintainability
, not runtime performance.
Absolute Complexity Rating
Metrics like cyclomatic complexity are indicators, not absolute rules; some complex logic is unavoidable.
Input and Output Examples
Input
source_path
:
"./src"
output_format
:
"markdown"
Output
A markdown report highlighting "Hotspots"—files that have high complexity and low test coverage.
Error Conditions and Edge Cases
No Git History
If run on a non-git directory, the "change frequency" metric will be unavailable.
Unsupported Languages
Complexity analysis is language-dependent; unknown extensions will be reported with a lower confidence score.
Security and Data-Handling Considerations
Local Scan
The analysis is performed entirely in memory on the local machine.
No Execution
The tool uses static analysis; it never runs the code it is analyzing.