desloppify

To identify technical debt and dead code across multiple languages, you can run an automated health scan to detect code smells, god classes, and naming inconsistencies. This process generates a prioritized cleanup plan and a measurable health score to guide refactoring.

The best way to generate a refactoring plan for code smells is using automated static analysis to scan the repository. This produces a prioritized cleanup roadmap targeting large files, module coupling, and import cycles to systematically reduce technical debt.

Yes, you can track codebase health improvements by following a scan, plan, execute, and rescan workflow. Running another health scan after executing the refactoring plan provides an updated health score and auditable remediation metrics to verify quality gains.

Static analysis works effectively for detecting god classes and module coupling in large repositories. Automated scanning evaluates file sizes, import cycles, and structural dependencies to surface these specific maintainability risks across multi-language projects.

An automated codebase health scan detects code quality issues including dead code, huge files, god classes, code smells, naming inconsistencies, and import cycles. It assesses these risks to calculate a health score and generate actionable remediation guidance.

You do not need any specific dependencies to start scanning for code smells. You can execute a health scan directly on your project directory to immediately assess technical debt and receive a prioritized cleanup plan.