Can AI Detect Flaky Tests or Predict Build Failures in CI/CD?

Flaky tests, which produce inconsistent results without code changes, pose significant challenges in continuous integration (CI) pipelines by eroding trust and increasing costs due to unnecessary reruns. These tests often result from timing assumptions, race conditions, shared states, reliance on external services, or resource variability in CI environments. AI can help detect and manage flaky tests by analyzing historical CI data, identifying patterns of statistical instability rather than individual failures, and predicting potential build failures. Although AI cannot automatically fix flaky tests, it provides probabilistic insights that help engineers identify and address root causes more efficiently. This approach enhances the reliability of CI pipelines, reduces noise, and optimizes resource usage by preventing blind reruns and focusing on genuine issues, but it requires sufficient historical data and structured reporting to be effective. While AI augments CI processes by providing adaptive responses and informed prioritization, it does not replace deterministic testing or the need for engineers to correct underlying problems.