Tech Debt Tracker

You can identify and track technical debt by running automated Python scanning scripts that use AST parsing and regex to detect code smells, large functions, and complexity, then monitor the evolution of these issues over time with trend analysis dashboards.

You can prioritize refactoring tasks by applying built-in prioritization frameworks like Cost of Delay, WSJF, or RICE, which rank identified technical debt items based on their business impact and development effort to create an actionable backlog.

Yes, you can automatically analyze code quality and complexity by executing Python scripts that perform AST parsing and regex matching to scan directories for security vulnerabilities, large functions, and structural code smells.

The best way to track debt reduction over time is to use dashboarding scripts that perform trend analysis and velocity tracking, generating reports that show management the progress and impact of refactoring efforts across sprints.

No external tools are required; the scanner relies solely on standard Python libraries included in its dependencies, such as os, re, sys, pathlib, typing, and dataclasses, to perform AST parsing and generate analysis reports.

Automated scanning can detect various types of technical debt, including large functions, high code complexity, and security vulnerabilities, by applying AST parsing and regex pattern matching directly to the source files in the target directory.