Scaling Test Automation at Microsoft [Testμ 2026]

Microsoft Power Platform engineering described an agent-assisted test automation approach for a monorepo with more than 100 packages and 14,000 tests, where running every test on each pull request previously delayed feedback by three to four hours. Its architecture separates probabilistic LLM-based reasoning from deterministic Playwright execution, using specialized planner, generator, healer, and reporter agents while requiring human review for every generated test or repair. Risk-based test selection analyzes code changes through abstract syntax trees, dependency graphs, failure history, and business criticality to prioritize impacted tests, while full regression suites continue to run nightly and at release gates. The system also measures integration-test coverage by matching business-rule intent to test specifications, identifies gaps before pull requests merge, and uses evidence such as logs, DOM snapshots, screenshots, and execution history to classify failures as product, test, or infrastructure issues. Proposed self-healing fixes are repeatedly validated in isolated “gauntlet” runs before being submitted as draft pull requests. Reported pilot outcomes included 60% faster regression feedback, 40% less test repair effort, 30% less manual triage, and 90% accuracy in product-versus-test-bug classification, though these were presented as internal observations rather than general benchmarks. The presentation emphasized that successful large-scale automation depends on governance, auditability, data privacy, isolated environments, historical execution data, and phased adoption rather than unrestricted AI automation or replacing existing test suites outright.

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