Detecting Flaky Tests in CI/CD Using Machine Learning: A Research Approach

Detecting Flaky Tests in CI/CD Using Machine Learning: A Research Approach The Problem In modern CI/CD environments, automated tests are expected to provide fast and reliable feedback. However, flaky tests — tests that pass and fail intermittently without code changes — introduce instability into the pipeline. A flaky test may: Pass locally but fail in CI Fail due to timing issues or race conditions Fail because of shared state or environment dependencies Over time, flaky tests reduce trust in automation and slow down engineering velocity. Why It Damages CI/CD Velocity When a test fails, engineers must decide: Is this a real regression? Or just another flaky failure? This uncertainty causes: Repeated pipeline reruns Increased build time Delayed releases Developer frustration In high-frequency deployment environments, flaky tests silently become productivity killers. Why Traditional Approaches Fail Several mitigation strategies are commonly used: 1. Reruns Automatically rerunning failed