​​Прикольна ідея про Flaky тести. Нажаль повноцінно описаної статті не знайшовNothing kills velocity faster than a test suite nobody trusts. Fixed that with AI.💡 AI to the Rescue: How We Brought Trust Back to AutomationOne of the biggest challenges in any large-scale automation suite isn’t writing more tests, it’s keeping the right ones reliable.Our test automation suite had reached a breaking point.Flaky tests were everywhere: false positives, inconsistent runs, wasted CI/CD cycles and build pipeline started slowing down. Developers stopped trusting automation results altogether.So, I built an AI-based system to predict and isolate flaky tests using a Random Forest model, trained on test metadata (execution time, file change frequency, historical pass/fail variance, etc.)Within weeks, the impact was clear:· Automated tests executed on every PR: under 20 minutes· False positives dropped by 80%· Developer confidence in Automation was fully restoredThis wasn’t just a tech win, it was a trust win.Automation became a reliable validation system again, not a noise generator.The graphic below outlines how we used Random Forest classification to predict flaky tests before they ever ran, helping us focus CI/CD pipelines on stable, high-signal tests only.