Flakytest

Flakytest, commonly referred to as a flaky test, describes a test case that yields non-deterministic results: it may pass in one run and fail in another despite no changes to the production code. Flakey tests undermine confidence in automated test suites and are especially problematic in continuous integration pipelines where inconsistent results slow development and obscure real defects.

Causes of flakiness typically fall into several categories. Nondeterministic behavior arising from concurrency or timing can cause races or ordering issues. Tests that rely on external systems, network services, or variable environmental conditions may produce different results. Dependence on real timestamps, random data without fixed seeds, or shared mutable state between tests can also introduce instability. Tests that assume a particular execution order or resource availability are particularly prone to flakiness.

Detection and impact: Flaky tests are often identified by repeated executions showing inconsistent outcomes or via dedicated flakiness analysis tools. They complicate debugging, mask legitimate failures, and erode CI reliability, leading teams to waste time triaging non-reproducible errors.

Mitigation strategies emphasize determinism and isolation. Make tests deterministic by removing nondeterministic factors, using mocks or stubs for external dependencies, and fixing setup and teardown to avoid shared state. Use explicit seeds for randomness and consider virtual clocks to control time-related behavior. Prefer idempotent, independent tests and isolate them from one another. Some teams quarantine flaky tests, marking them for retries or separate analysis, and maintain dashboards to track flakiness trends and prioritize remediation.

Best practices involve deliberate test design changes and tooling support to detect, manage, and reduce flakiness. The goal is a fast, reliable, and trustworthy test suite where flaky tests are minimized rather than masked by continuous retries.