improve-codebase-architecture

You can identify TypeScript refactor candidates automatically by running an architecture scan that analyzes coding standards and architectural evidence across repository, directory, module, or file set scopes to pinpoint areas needing structural improvement.

Evidence-based refactoring uses architectural analysis and coding standards to identify code areas needing improvement. It evaluates evidence halos across repository, directory, feature, module, or file set scopes to find concrete refactor candidates rather than guessing.

Yes, code architecture scans support repository, directory, feature, module, file set, or concern scopes. You can target specific directories or feature modules to narrow the architectural analysis and find refactor candidates within those boundaries.

This approach automates architectural analysis and applies coding standards with evidence halos to generate concrete refactor candidates. This eliminates manual codebase review and provides structured, evidence-backed targets for improving code quality and structural integrity.

Coding standards are incorporated into the architectural analysis to identify refactor candidates based on evidence. Defining or providing these standards ensures the scan produces evidence-backed results aligned with your specific code quality and structural requirements.

The scan considers evidence halos and coding standards across various scopes to pinpoint refactor candidates. This method is ideal for large TypeScript codebases where developers or architects need to enhance code structure and quality without performing extensive manual analysis.