The Complete Guide to Detecting and Fixing Flaky Tests

Flaky tests aren’t inevitable. They’re a signal that your tests or system lack determinism—and they can be fixed with the right workflow.

Define flakiness clearly

A test is flaky if it fails without a code change that explains it. That means you need reliable metadata: commit SHA, environment, and run-to-run history.

Common root causes

• Race conditions and unstable async timing
• Mutable shared state between tests or workers
• Non-deterministic data or time-based assumptions
• Environment drift (different configs, services, or seeds)

Triage workflow

• Cluster failures by signature to avoid chasing noise
• Quarantine top offenders, but keep them visible
• Add debug artifacts (logs, screenshots, traces) on failure
• Re-run with controlled seeds to reproduce deterministically

Fixes that stick

• Make waits explicit (network idle, element state, retries with boundaries)
• Isolate test data and clean up side effects
• Use contract stubs or sandbox accounts for external dependencies

Measure improvement

Track flake rate per suite and per test. The goal is not just fewer flakes—it’s restoring confidence so teams stop ignoring failures.