analyze-code-quality

To run a code quality audit, automated validation checks are executed against the source code repository to assess engineering risks. The process combines test assessment, lint review, type validation, and anti-pattern detection to generate prioritized quality findings and actionable engineering follow-ups.

Static analysis can detect missing behavioral state-transition scenarios by reviewing stateful modules and test coverage gaps. This behavioral risk audit uncovers missing transition scenarios that standard coverage reports often miss, ensuring comprehensive validation of state changes.

The best way to identify hidden code quality issues before a release is combining automated checks with targeted behavioral analysis. This approach runs coverage analysis, build validation, and code smell detection to produce a detailed quality report covering potential regressions and prioritized follow-ups.

You do need project tooling configured because the analysis requires execution of project tooling and inspection of source structure. Running lint review, type validation, and build validation depends on the existing environment setup to correctly generate the prioritized quality findings.

Standard coverage reports have limitations because they may miss missing transition scenarios in stateful modules. Relying only on surface-level metrics fails to uncover behavioral risks, requiring targeted behavioral analysis and anti-pattern detection to fully assess repository health.