code-analysis

To assess tech debt and code quality metrics, you analyze project structure, module dependencies, and identify deprecated patterns or missing error handling. This process reveals TODOs, architectural violations, and areas needing refactoring.

Verifying DDD layer separation and NestJS module boundaries involves analyzing service injection patterns and module dependencies. This architectural review ensures layers remain distinct and checks for dependency violations across your codebase.

Performing an architecture review through codebase exploration requires analyzing layers, boundaries, and dependencies alongside git history insights. This generates a detailed report highlighting potential structural violations and architectural issues.

Yes, code analysis supports Prisma schema consistency checks. It evaluates schema structures against your codebase to ensure data layer alignment, while verifying domain exceptions and error handling patterns.

Yes, you can identify missing error handling and domain exceptions during a code review. The analysis specifically targets these tech debt items, verifying that error handling correctly implements domain-specific exception patterns.

Use automated code analysis for refactoring recommendations when you need deep insights into code quality metrics, complexity, and duplication. It provides actionable recommendations for improving software maintainability during code reviews.