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A green retry is evidence of intermittent behavior, not proof that the first failure was harmless. Preserve the original assertion, logs, seed, ordering, environment, and CI job context. Then use the retry to classify the event, inspect fixture and dependency lifecycles, and apply a retry or quarantine policy that your team has written down.
This approach keeps a transient test problem from becoming a permanent blind spot in the merge gate. It also gives engineers a reproducible record for fixing the cause instead of repeatedly clicking rerun.
Why a passing rerun does not clear the first failure
Pytest describes a flaky test as one that fails sporadically or non-deterministically. When the same test passes on a later attempt, you have learned that its result depends on conditions that changed between attempts. You have not established that the original failure was irrelevant.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe first failure may represent a product defect, a broken test assumption, an isolation leak, or a timing race. If a retry hides it, developers can lose trust in the suite, overlook genuine regressions, and spend time rerunning jobs instead of fixing them. A retry is therefore a diagnostic action and, at most, a temporary mitigation for delivery flow.
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- Keep the complete first-attempt assertion and stack trace.
- Retain the test order, random seed, worker assignment, environment variables, dependency versions, and job URL when the runner exposes them.
- Record whether the next attempt passed, failed in the same way, or failed differently.
- Link the event to an owner and an investigation record rather than treating it as an anonymous green build.
Selective uses one explicit classification model: a test that fails and then passes on retry is counted as a flake occurrence, while a test that fails again remains a failure. That model is useful for illustration, but it is a vendor-defined behavior rather than an industry-wide standard. Your team should document its own event names and counting rules.
Use a four-stage failure lifecycle
1. First attempt: capture before changing anything
Configure the CI job to preserve the original result before a retry starts. Store the assertion output, screenshots or logs produced by the test, random seed, test selection expression, worker or shard, operating-system image, and relevant service versions. If the infrastructure provides a container or VM identifier, retain that too. Do not overwrite the first report with the retry report.
A useful record has a stable test identifier, commit SHA, pipeline and job identifiers, attempt number, start and end times, and the exact command line. This lets an investigator compare attempts instead of guessing which conditions changed.
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Run the retry under the same declared conditions whenever possible. A retry that passes is evidence that the failure is intermittent; it is not evidence that the test can be ignored. A repeated failure should remain a normal failing result and should block the merge according to the ordinary gate.
Retries are most informative when they are bounded. An unlimited loop can turn a deterministic failure into a green job and can consume runner capacity without adding useful information. Set a maximum attempt count, emit every attempt to the report, and make the limit visible in the job configuration.
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3. Investigation: compare the boundaries
Compare what was supposed to be isolated with what was actually shared between attempts. Look at fixture setup and teardown, temporary resources, service readiness, clocks, random data, test order, parallel workers, and background activity. The objective is to identify the state transition that made the second attempt different.
4. Disposition: fix, rewrite, or quarantine deliberately
Prefer a code or test fix. Rewriting the test at a less stateful level may be appropriate when an end-to-end check is duplicating a unit-level contract while depending on many unstable services. If immediate stabilization is impossible, quarantine the test under an explicit policy with an owner, evidence, and a review date.
What hooks should do (and what they should not do)
There is no universal hook sequence for every test runner or CI system. Treat a hook as an implementation-specific point where your team captures metadata, applies the retry limit, emits an attempt-level report, or enforces the flake policy. Verify names and behavior against the versions of the framework and CI runner you use.
A framework-neutral event flow looks like this:
on_test_attempt_start(context):
record(test_id, attempt, seed, order, worker, environment)
on_test_attempt_end(result):
persist(assertion, logs, duration, status, attempt)
if result.failed and attempt < retry_limit:
schedule_retry()
on_test_complete(attempts):
classify(repeated_failure, retry_pass, single_pass)
publish_all_attempts()
enforce_budget_policy()This is pseudocode, not a drop-in recipe. In pytest, for example, teams commonly use runner hooks or plugins to observe test reports, while CI systems can add job-level retry behavior. Those layers have different visibility: a runner hook can identify an individual test attempt, whereas a CI retry may rerun an entire job. If you use both, define which layer owns the count and prevent one from silently masking the other.
Hooks should never delete the original report, convert a failure directly into an unconditional success, or mark a test quarantined without recording why. They should also avoid introducing shared mutable state that changes test behavior. Keep collection, execution, reporting, and merge-gate decisions separate enough that an outage in analytics does not alter the test result itself.
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Inspect fixtures and lifecycle leaks first
Fixtures are natural investigation points because they create, share, and destroy the state that tests depend on. Pytest identifies inadequate isolation, ordering dependencies, uncleaned data, parallel-execution interactions, global state, timing-sensitive assertions, and thread-safety issues as common sources of flaky behavior.
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Check setup and teardown symmetry
For every resource created in setup, identify the corresponding cleanup path and verify that cleanup runs on assertion failure, timeout, cancellation, and fixture-scope changes. A session-scoped fixture that mutates configuration can affect every later test. A function-scoped fixture that leaves a database row or environment variable behind can affect only certain orders, making the failure appear random.
- Record the initial value of environment variables, working directories, locale, and process-wide configuration.
- Restore those values in teardown, even when the test raises an exception.
- Prefer unique resource names derived from the test or worker rather than a shared constant.
- Make cleanup idempotent so a partially completed setup can still be safely removed.
Audit temporary files, databases, and ports
Use isolated temporary directories and database schemas for each test or worker where practical. Close file handles before a fixture removes a file. Reset database state with a transaction rollback or a known-clean fixture instead of relying on test order. Allocate ports dynamically and release them in a guaranteed cleanup path; a fixed port can collide with another worker or with a process left by an earlier attempt.
Look for global state and caches
Module caches, singleton objects, monkey patches, feature flags, and imported configuration often survive beyond the test that changed them. Reset or scope them explicitly. If a test depends on cache contents, seed the cache in its own fixture and assert the expected state before exercising the behavior.
Track background threads and asynchronous work
Tests and fixtures can spawn threads or tasks that continue after the assertion has finished. A later test may then observe an unexpected callback, open socket, or modified object. Keep handles to every background activity, signal it to stop, join or await it during teardown, and fail the test if it cannot stop within a bounded timeout.
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Remove hidden time and order dependence
Assertions against the current wall clock, sleep-based synchronization, and assumptions about dictionary or database ordering can fail only under load or on a slower runner. Inject a controllable clock, wait on an observable readiness condition instead of sleeping, and sort data when order is not part of the contract. Run the affected test with randomized order and retain the seed so another engineer can reproduce the sequence.
Consider parallel execution explicitly
A test that passes alone but fails with multiple workers may share a file, port, account, queue, or global service. Reproduce with the same worker count and shard assignment. Either isolate the resource, serialize the genuinely non-parallel operation, or move the check to a level where parallel isolation is easier.
| Symptom | Likely boundary | Investigation and fix |
|---|---|---|
| Passes alone, fails after another test | Order or leaked global state | Randomize order, preserve the seed, reset process-wide settings, and identify the preceding test that leaves state. |
| Fails only with multiple workers | Shared file, port, database, or account | Namespace resources by worker, use isolated schemas, or serialize the operation that cannot be made independent. |
| Fails near timeout, then passes | Readiness or timing assumption | Replace sleeps with condition-based waits, capture service latency, and use a clock that the test can control. |
| Later tests fail after this test errors | Incomplete teardown | Exercise cleanup on exception and cancellation paths; make teardown idempotent and observable. |
| Different failures on each retry | Concurrent background work or shared mutable state | Capture thread/task activity, stop it in teardown, and remove unsynchronized access to shared objects. |
Choose a response that preserves visibility
Fix or rewrite the test
Once the failure mechanism is understood, make the smallest change that restores deterministic behavior. Add an assertion for the missing precondition, isolate the fixture, replace a real dependency with a controlled boundary, or split one broad test into checks with narrower state. Re-run the original reproducer and the surrounding suite, not only the test that happened to pass on retry.
Use retries as a bounded diagnostic aid
A runner retry or diagnostic plugin can expose each attempt, replay a failure, or randomize order. Check whether it is compatible with your framework version, whether it retains the first report, and whether it distinguishes a test retry from a whole-job retry. A retry reduces disruption while you investigate; it does not repair the test.
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Quarantine with an exit plan
Quarantine should be a temporary control, not a second name for deletion. Selective documents a model in which quarantined tests continue to run and appear in reports while no longer failing the run. Adopt that visibility if your tooling supports it.
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Attach a named owner, the issue or incident record, the date of quarantine, the observed failure pattern, and a condition for restoration to the merge gate. Review the list on a schedule and remove entries when fixed. Pytest warns that non-strict xfail can become a manual quarantine and is dangerous to leave in place permanently; use it only with the same ownership and review discipline.
Design a flake budget your team can defend
A flake budget is a measurable agreement between test owners and the people who rely on the merge gate. The sources do not establish a universal percentage or time limit. Choose values based on your risk tolerance, suite size, release cadence, and ability to investigate, then document the decision.
| Policy dimension | Decision to record | Why it matters |
|---|---|---|
| Unit | Individual test, suite, job, or complete pipeline | A pipeline-level rate can hide one test that fails repeatedly, while a test-level view can miss systemic runner problems. |
| Event | First-attempt failure, pass after retry, quarantined result, or a defined combination | These events have different meanings and must not be combined silently. |
| Denominator and window | Test executions or CI runs measured over a stated period | A rate without its denominator and time window cannot be interpreted or compared. |
| Response | Investigator, retained evidence, escalation path, and merge-gate action when the limit is crossed | A number without a consequence does not change behavior. |
| Quarantine lifecycle | Whether the test keeps running, owner, review date, and restoration condition | Visibility prevents temporary suppression from becoming permanent. |
Write the policy in plain language. For example: define whether a retry pass counts as a flake event; specify the reporting window; require first-attempt artifacts; and state who decides whether to block, quarantine, or fix. GitLab’s published engineering guidance and Selective’s product documentation are operational examples, not authorities for a universal threshold.
Compare the main control options
| Approach | Questions to ask | Main caution |
|---|---|---|
| Test-runner retry or diagnostic plugin | Are all attempts exposed? Can failures be replayed? Can order be randomized? Is the tool compatible with the framework version? | Rerunning can mitigate disruption while leaving the cause untouched. |
| Quarantine | Does the test continue to execute and report? Are owner and review status visible? Is restoration automatic or manually approved? | Permanent suppression hides failures; non-strict expected-failure settings can have the same effect. |
| CI analytics and reporting | Does history retain occurrence context? Can it distinguish retry passes from repeated failures? Does it integrate with your repository and runner? | Feature definitions differ, so validate the behavior with your own jobs instead of assuming a vendor label means the same thing everywhere. |
Implement the policy step by step
- Define the event schema. Choose stable fields for test ID, attempt number, status, seed, order, worker, commit, environment, and artifact locations.
- Preserve attempt one. Configure the runner or CI job to publish the first report before any retry starts.
- Set a bounded retry rule. State when a retry is allowed, how many attempts are permitted, and whether the retry applies to a test, shard, or entire job.
- Classify outcomes. Emit separate statuses for first-attempt pass, retry pass, repeated failure, and infrastructure failure. Do not collapse them into one green or red value.
- Route ownership. Send a retry pass with its artifacts to the test owner or service team. Include enough context to reproduce the sequence.
- Apply the budget. Evaluate the chosen unit, event, denominator, and window. Trigger the documented response when the team-defined limit is exceeded.
- Review quarantine. Keep quarantined tests running when possible, inspect their history, and restore them to the gate after the owner verifies the fix.
Troubleshoot common CI situations
The retry passes, but the failure cannot be reproduced locally
Do not discard the CI evidence. Compare operating-system image, dependency lockfile, worker count, seed, clock, locale, service versions, and resource limits. Re-run locally with the captured order and seed, or reproduce inside the same container image. A difference between environments is itself a lead.
The retry fails differently
Classify the original and second failures separately. Different errors often indicate shared state, incomplete cleanup, or a race rather than a single bad assertion. Preserve both traces and inspect background activity and resource contention.
A whole job retry hides which test failed first
Move the retry decision closer to the test runner, or make the job publish per-test reports before CI retries the job. If job-level retries are unavoidable, include an attempt number in every artifact path and mark the pipeline as containing a retry pass rather than presenting it as an ordinary pass.
A quarantine list keeps growing
Stop adding entries without ownership. Require an issue, a review date, and a stated restoration condition. Group entries by fixture, service, or runner to find systemic causes; a large cluster may justify fixing the shared dependency instead of editing each test.
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- Can an engineer retrieve the first failure without rerunning the job?
- Does every retry pass remain visible in test history?
- Is the retry limit explicit and bounded?
- Are fixture setup and teardown paths tested under failure and cancellation?
- Are temporary resources, ports, databases, globals, threads, clocks, and ordering isolated?
- Does the report distinguish test failure, infrastructure failure, retry pass, and quarantine?
- Does every quarantined test have an owner, evidence, review date, and restoration condition?
- Does the flake budget state its unit, event, denominator, window, and response?
The practical rule is simple: a green retry closes neither the investigation nor the evidence trail. Keep the first failure, inspect the lifecycle that surrounds the test, and make every exception to the merge gate explicit, measurable, and temporary.
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