code-review

Automate code review in CI pipelines by applying structured workflows that check invariants, evaluate test coverage, and verify build artifacts to ensure correctness and performance. This reduces manual cognitive load and speeds up feedback.

An invariant-focused code review process grounds feedback in explicit system invariants and test alignment. It uses lightweight artifacts like test reports to verify correctness, ensuring changes do not introduce regressions or break concurrency rules.

Check test coverage and build artifacts during review by describing the PR in terms of invariants and running targeted local checks. The process evaluates test-report.json and build outputs to validate that tests correctly capture invariant failures.

Yes, automated code review can detect performance hotspots and data races by flagging invariants failing under concurrency. It enforces corresponding tests to report these failures in artifacts, ensuring regressions are caught during the CI process.

LLM-assisted code review requires explicit invariant definitions and test alignment to function effectively. Without structured guidance to ground feedback in artifacts and correctness criteria, traditional reviews remain error-prone and slow.

Avoid automated code review when a repository lacks defined invariants, test coverage, or CI artifact generation. The process depends on verifying build outputs and test reports, making it unsuitable for projects without these lightweight artifacts.