complexity-audit

To audit codebase complexity, run the analysis to detect architectural and code smells like god modules, dead code, and scattered concerns. This process evaluates structural issues and outputs a detailed report with recommendations for refactoring and optimization to streamline feature development.

Codebase complexity assessment is the analysis of architectural structures to identify issues hindering feature development. You need this process when simplifying modules, tracing feature impact, or prioritizing optimization efforts to resolve duplicated state and scattered concerns across your codebase.

Yes, the complexity audit requires Python scripts to perform codebase analysis and generate reporting. The environment needs Python dependencies installed to inspect individual modules for structural issues and produce a detailed complexity map with actionable refactoring recommendations.

Yes, you can evaluate feature complexity by tracing its impact across the codebase to suggest simplifications. The analysis inspects subsystems, identifies duplicated state and dead code, and generates a report detailing areas of concern to streamline ongoing feature development.

The best way to identify god modules and dead code is to run a comprehensive architectural complexity audit. This analysis evaluates your codebase for scattered concerns and duplicated state, providing a detailed complexity map and actionable refactoring recommendations to prioritize optimization.

Automated code complexity analysis is limited to identifying structural and architectural smells like dead code and god modules based on the codebase provided. It requires Python scripts to execute the audit and generate reports, but does not automatically apply the refactoring recommendations it produces.