technical-debt-detector

To identify technical debt in Python projects, run an analysis that scans for deferred work, security issues, testing gaps, maintainability concerns, and outdated dependencies. The tool aggregates findings by severity and outputs a structured text or JSON report for prioritized refactoring.

Technical debt analysis systematically surfaces deferred work, security vulnerabilities, and testing gaps across repositories. It helps sprint planning by generating prioritized, actionable fix sketches, allowing teams to focus refactoring efforts on high-impact areas and reduce codebase risk.

Yes, this technical debt detection supports integration across repositories and CI pipelines. It applies modular analyzers to scan Python projects continuously, outputting structured JSON or text reports that highlight maintainability concerns and dependency health during automated builds.

Yes, Python technical debt analysis checks dependency health and security issues using dedicated modular analyzers. It scans your project to detect outdated dependencies, security vulnerabilities, and testing gaps, aggregating these findings into a prioritized, structured report.

You get a structured JSON report of codebase risk by running the master analysis on your Python project. The process evaluates maintainability, security, and dependency health, aggregating the findings with severity levels to output actionable data in either text or JSON format.