test-optimization

Quantify the compute and developer-time cost of flaky tests from JUnit XML - classify flakes vs genuine failures across reruns, rank them by cost. Python 3.11, stdlib only.

Selects only the tests affected by your code changes by mapping per-test runtime coverage against Git diffs, cutting regression suite execution time.

Playwright plugin that optimizes parallel test execution by distributing test files across shards based on historical execution times rather than file count.

Fail-closed deterministic test reuse for AI coding agents and CI on Linux/amd64.

Evidence-based GitHub Actions matrix diagnostics: measure failure signal, preserve coverage, and reduce CI only when history supports it.

CI Test Selector- A web app with a predictive ML Model to identify and run only the test cases affected by code changes to optimize CI/CD runtime. Built with Python, Scikit-learn, and FastAPI.

Prototype for evaluating coverage-based test selection in CI, measuring missed-failure risk and potential time savings.

An agent skill for efficient test verification and concise diagnostics across projects and languages.

How can SBT be used to distribute test execution easily

ML-powered GitHub pull request risk analysis and intelligent CI test selection using static analysis, dependency graphs, and XGBoost.

Finds PHPUnit and Pest tests whose covered lines other tests already cover, and shows what each one asserts beyond them. Per-test coverage from one parallel run, JSON for AI agents, 100% coverage with fewer tests.