python-refactor

To refactor Python code safely, use a four-phase workflow: Analysis, Planning, Execution, and Validation. This approach applies static analysis and golden-master checks to restructure tangled logic into modular, testable architectures while preserving existing behavior.

Restructuring Python modules with heavy global state involves converting script-like code into modular classes using dependency injection. This enforces single-responsibility principles and layered architecture, resulting in code that is significantly more testable and maintainable.

Yes, static analysis is integrated directly into the refactoring validation phase. By combining complexity metrics and golden-master checks, it monitors code structure changes to prevent behavioral regressions while you improve readability.

Complexipy is utilized as a core dependency to measure complexity during the analysis phase. It helps identify tangled logic and high-complexity areas within multi-module Python codebases, guiding the structured refactoring workflow toward targeted improvements.

A structured refactoring workflow is needed when projects suffer from tangled logic, heavy global state, or span multiple modules. It provides repeatable safety checks and migration protocols to ensure behavior remains unchanged while improving overall architecture.

The primary constraint is preserving existing behavior and performance characteristics during OOP transformation. While converting to modular, dependency-injected designs improves readability, the process requires strict regression safety checks to avoid introducing subtle bugs.