Flaky Test Detection with Machine Learning

Flaky tests are expensive because they hide real failures and reduce trust in CI. A practical strategy is to build a simple flaky-score model from historical runs and use it as a signal, not as the final authority.

Start with three data points per test: failure frequency, retry pass-rate, and execution-time variance. These metrics are easy to capture from your CI provider and already explain most unstable behavior. Add metadata like browser, environment, branch, and ownership team so you can segment hotspots.

Once you train the model, publish predictions into a nightly report. Do not auto-quarantine immediately. First, validate precision against manual triage for 2-3 weeks. Teams should review false positives and tune feature thresholds. This avoids hiding valid regression tests.

In production workflows, connect flaky scoring to:

1. pre-merge risk dashboards,
2. targeted rerun strategies,
3. backlog creation for highest-impact suites.

The key outcome is not "perfect prediction", but faster decision-making. If your team can identify unstable suites within minutes instead of hours, developer confidence increases and release flow gets smoother.