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AI in Testing

Smart test selection

Also known as: predictive test selection, test prioritization

Smart test selection runs only the tests most likely to be affected by a given code change, instead of the entire suite, to cut CI time while preserving the chance of catching regressions.

As suites grow, running everything on every commit becomes the bottleneck. Smart selection uses the relationship between code and tests — and historical signals about which tests catch which changes — to choose a high-value subset to run first, deferring or skipping the rest.

It is closely related to test impact analysis: impact analysis maps which tests a change touches; smart selection adds prioritization (often ML-driven) on top. Done well it can cut test execution dramatically with minimal added risk.

  • Runs the subset of tests a change is most likely to affect.
  • Builds on test impact analysis with prioritization, often ML-driven.
  • Cuts CI time and shortens feedback loops as suites scale.

Related terms

Test impact analysis (TIA) Test parallelization DORA metrics Quality gate

Sources

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Last reviewed June 11, 2026