AI-Powered Impact-Based Testing for Faster Safer Releases [Testμ 2026]

At Testμ Conf 2026, UKG engineers Navneet Goyal and Srikanta Sahoo presented impact-based test selection as an alternative to running complete regression suites for every small code change, arguing that full runs create infrastructure costs, delayed feedback, developer context switching, and pipeline congestion. Their approach analyzes a change using direct and indirect dependencies, semantic relationships, historical failures, component criticality, quality indicators, dynamic risk factors, and machine-learning rankings to identify and explain the tests most likely to be affected. The system integrates with existing CI/CD tools through an API, lets engineering teams set selection thresholds, and uses safeguards including mandatory smoke and critical-path tests, broader selection for uncertain changes, periodic full-suite runs, and full regression for major upgrades. The presentation described lessons involving skipped builds, newly added tests, flaky failures, and gradual learning from execution outcomes, but its reported accuracy figures were inconsistent and lacked definitions, sources, baselines, or methodology. Speakers did not claim the system eliminates missed defects, instead emphasizing human oversight, conservative fallbacks, and the need for diligence while the model matures.

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