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What counts as a flaky test?
A flaky test produces different outcomes across runs in circumstances that appear equivalent. For example, it may fail once and pass when rerun, or pass in isolation but fail when run after another test. That inconsistency makes CI failures harder to interpret and creates additional rerun and investigation work. The pytest documentation describes common causes including uncontrolled system state and inadequate environment isolation (pytest: Flaky tests).
A test that fails every attempt is not a retry-pass flake: it remains a failure that needs debugging. Keep that distinction visible in results.
How to detect flaky tests
1. Preserve first-run and retry outcomes
Do not record only the final green or red status. Store the initial result, each retry result, and the final classification. A test that fails initially and then passes has a different diagnostic meaning from one that fails on every attempt. If the runner collapses retries into a single status, enable its detailed report or retain the per-attempt output in your CI artifacts.
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2. Repeat tests deliberately
Begin with the runner’s simplest repeat mechanism. In Playwright Test, retries are off by default in the documented guide. When retries are enabled, a test that fails on its first attempt and passes on retry is classified as flaky; a test that continues to fail remains failed (Playwright Test retries).
For a focused investigation, Playwright’s repeatEach setting runs each test repeatedly and is documented as useful for debugging flaky tests. Repetition is an experiment, not a fix: note the test, environment, and run conditions so results can be compared.
3. Compare the conditions around each outcome
For the same test, compare its position in the suite, preceding tests, shared resources, concurrency, environment, and whether it fails when run alone. Randomized test order can expose hidden dependence on previous tests or shared state. pytest’s flaky-test guidance also discusses plugins for rerunning failures, randomizing order, replaying observed failures, and classifying them (pytest: Flaky tests).
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4. Keep useful failure evidence
Retain logs and diagnostics that help reconstruct what the application and test were doing at the time. For UI tests, screenshots or video captured on failure can make a transient visual or state problem easier to understand; pytest specifically points to these as useful diagnostics. Record enough context to distinguish a product failure from a test-order or environment problem.
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Customize detection and CI behavior
Choose a retry signal and scope
Use retries when you need to identify fail-then-pass outcomes; use repeated runs when investigating whether a test is inconsistent. In Playwright, retries can be configured globally or for a group of tests. Set the narrowest scope that answers the question: global settings help establish suite-wide visibility, while group-level settings can focus investigation on a known unstable area.
There is no universal retry count that suits every suite. Set a small, explicit budget based on runtime and the impact of a missed failure. Playwright’s --retries=3 is an example in its documentation, not a general recommendation. Keep the retry outcome visible rather than treating a retry-pass as an ordinary pass.
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Separate detection from the build gate
Decide explicitly whether a flaky classification should fail the CI run or appear in reports without blocking it. Playwright Test provides failOnFlakyTests, documented as available since v1.52. Its configuration reference also lists repeatEach and retryStrategy; retryStrategy is documented as available since v1.62. Check the version of Playwright installed in your project before using versioned settings (Playwright TestConfig reference).
The current Playwright configuration reference describes immediate retries and an isolated retry strategy that retries at the end of the suite. Isolated retries can reduce interference between retry attempts and other tests, but can extend total run time. Choose based on whether isolation or a shorter feedback cycle matters more for the investigation.
Use framework-appropriate handling
Do not assume retry and quarantine semantics are identical across test frameworks. In pytest, plugins can provide reruns, random ordering, replay, or failure classification. pytest also warns that non-strict xfail can act as manual quarantine: it may stop a failure from breaking a build, but is dangerous as a permanent way to hide a failing test (pytest: Flaky tests).
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Azure Pipelines offers flaky-test detection through reruns or custom detection, reporting choices, and workflows for marking or unmarking tests and creating bugs. Microsoft notes that flaky-test data is available at the branch level; select reporting and build behavior according to how your team uses that data (Manage flaky tests – Azure Pipelines).
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Race conditions and shared state
Look for concurrent access to shared resources, timing assumptions, and checks that run before the application reaches the state the test expects. Log access to shared resources and synchronize on meaningful application state rather than on an arbitrary amount of elapsed time. Google’s testing guidance cautions that arbitrary delays can become flaky again and make tests slower (Google Testing Blog: Test Flakiness).
Order-dependent tests
Run a suspect test independently and compare that result with its behavior in the full suite. If it changes only in a particular order, inspect setup and teardown for leaked files, database records, browser state, or other shared resources. Make tests independent so one test does not rely on another to establish its starting state.
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Environment and test design
Check whether machine load, external services, timing, or inadequate environment isolation can change the result. Randomized order can help expose state leaks. If appropriate, split unit and integration suites so a slower or more environment-sensitive group does not obscure the signal from the rest. For a test whose coverage is duplicated or whose end-to-end setup is inherently unreliable, consider rewriting it at a lower level or removing it when equivalent coverage exists; pytest includes these as possible mitigation strategies.
Keep quarantine temporary and visible
If a failing test must be prevented from blocking work while it is investigated, preserve its failure and flaky history in reports, assign an owner, and track a follow-up. Do not let a retry-pass or non-strict expected-failure annotation quietly become a permanent substitute for diagnosis.
Troubleshooting common detection problems
- The build is green, but users still see test instability: inspect per-attempt results. A runner or CI policy may be surfacing only the final retry outcome.
- A test fails on every retry: keep it classified as a failure rather than a flake. Investigate the failing assertion, environment, and application behavior.
- A test passes alone but fails in the suite: randomize order and inspect shared state, cleanup, and concurrency.
- Retries make CI much slower: reduce the retry scope, reserve repeated runs for targeted investigation, or use an isolation strategy only where it provides useful evidence. Retries add execution time, particularly when they run after the suite.
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failOnFlakyTestsis documented since v1.52 andretryStrategysince v1.62. - A test is marked expected-failure and disappears from attention: review whether non-strict
xfailhas become indefinite quarantine. Restore visibility and assign a concrete investigation or removal decision.
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