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If an AI-generated test passes on one run and fails on another, treat it as a nondeterministic test—not as a prompt to increase the timeout or add retries. Reproduce the failure, capture the conditions that change between runs, and then fix the source of nondeterminism in the test, application, or test environment. Generated tests are code, and a 2024 ICSE study found their flakiness was at least as common as in developer-written tests.
Why do AI-generated tests pass sometimes and fail other times?
A test is flaky when it sometimes fails despite apparently unchanged code and inputs. The word “generated” does not make a test inherently unreliable; the test can still depend on uncontrolled state, timing, ordering, randomness, concurrency, or an overly strict assertion. pytest identifies these as common sources of intermittent failure.
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Generated tests deserve the same scrutiny as any other code. In a 2024 ICSE study, Martin Gruber, Muhammad Firhard Roslan, Owain Parry, Fabian Scharnböck, Phil McMinn, Gordon Fraser, and colleagues sampled 6,356 projects, generated tests with EvoSuite and Pynguin, and ran each test 200 times. The researchers found flakiness was at least as common in generated tests as in developer-written tests. Randomness caused generated-test flakiness more frequently than networking or concurrency. The study also reported 71.7% fewer flaky tests when EvoSuite’s flakiness-suppression mechanisms were applied; that figure describes the study’s results, not a guaranteed reduction for another project.
For a UI test, the apparent failure may be a timing race: the test acts before an element is ready, or checks a condition before background work finishes. A failure can also come from leftover data, a test that depends on another test’s order, parallel workers sharing state, a changing clock or random value, or a dependency or environment that behaves differently between runs.
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Google Testing Blog summarizes an important distinction: “Flaky (nondeterministic) tests, however, are different.” A test that fails intermittently is not simply a test that has exposed a normal, repeatable product defect. The variation itself is evidence: something about the test inputs, execution, state, or environment is not controlled. The goal is to find that changing factor, not to treat a successful rerun as proof that the first failure was meaningless.
How should I reproduce and diagnose the failure?
Do not edit the test after a single failure. First determine whether the failure can be reproduced and which changing condition is associated with it; then reduce the case until the cause is visible. pytest describes flakiness as intermittent or sporadic failure with apparently nondeterministic behavior. Reproduction is therefore a diagnostic step, not just a confidence check.
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Repeat the same test
Run the failing test several times under the same conditions before changing code. Fuchsia’s testing guidance recommends repeat runs and injectable random seeds; it says teams may use 100–1,000 local repetitions for tests prone to flakes. That is a suggested range for local investigation, not a universal requirement for every test. A single passing rerun is weak evidence: a failure that occurs once in many runs can still be a real reliability problem.
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Vary one condition at a time
Try running the test in a different order, with parallel workers disabled or changed, with a controlled random seed, and in a different environment if one is available. Changing one factor at a time makes it easier to distinguish an order dependency from a timing, parallelism, or environment problem. If it fails only after another test, inspect shared data and cleanup. If it fails only with parallel workers, look for collisions or unsafe shared state. If it follows a particular seed, inspect randomized inputs first.
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Preserve artifacts from each failure
Keep the stack trace and logs, along with timing information, the random seed, dependency versions, and browser or device versions when applicable. For UI failures, retain a screenshot or video. Record the exact generated test revision and, when available, the prompt or generation context that produced it. Without these details, a rerun may show that a test failed but not what state it reached or what changed. Capture the same details from a successful run where useful, so you can compare the two executions rather than rely on memory.
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Classify likely sources
Check shared state and cleanup, test order, parallel workers, clock and random inputs, asynchronous operations, network calls, thread races, and assertions that require exact timing or formatting. Google’s testing guidance and pytest’s flakiness documentation describe these as distinct failure classes, so investigate the one supported by the artifacts rather than applying the same fix to every failure. For example, a timeout in a stack trace might point to delayed readiness, while a mismatch that changes with the random seed suggests variable test data.
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Reduce the failing test
Strip the generated test down to the smallest case that still fails. If an end-to-end test obscures whether the problem lies in a component, an integration, or the whole application, separate those layers and test the behavior at a lower level. pytest recommends splitting suites or rewriting at a lower level when doing so removes the flakiness. Keep the behavior under test intact as you reduce the setup: minimizing the case should clarify the cause, not quietly remove the condition that made the test useful.
How do I stop an AI-written Selenium test from using sleeps?
Replace fixed sleeps with an explicit wait for the condition required by the next action. Selenium’s documentation puts it plainly: “Sleeping is not waiting.” A fixed delay guesses how long the application needs; a condition-based wait checks whether the application is actually ready.
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Wait for the state the test needs
Choose a condition that represents the test’s next valid step: an element becomes clickable, expected text appears, a spinner disappears, or a background operation completes. The wait should be tied to that condition rather than to an arbitrary number of seconds. For example, if the test clicks a button as soon as it appears but the application has not made it interactive, wait for clickability—not merely for the element to exist.
The distinction matters because “present” and “ready for the next action” are not always the same state. Match the wait to the operation that follows it. After submitting a form, for instance, wait for the expected result or completion signal rather than pausing for a guessed interval. If the condition never becomes true, the failure then points to a meaningful missing state instead of an unexplained delay.
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A longer delay can make a race less visible without correcting it. Selenium warns that fixed sleeps are a major source of flaky browser tests, and that an AI agent may respond to intermittent failures by increasing a timeout. If the underlying readiness condition is still wrong, the test remains fragile and simply takes longer to run. A delay may also be too short in a slower environment and needlessly long in a faster one, so it combines unreliable synchronization with wasted runtime.
Use the platform’s synchronization tools
For Android tests, the Android guidance recommends mechanisms such as Espresso idling resources and Compose wait-until APIs instead of arbitrary sleeps. Different devices and environments may need different execution times, which is why a fixed pause can be both too short to prevent a failure and unnecessarily long when the app is already ready. Use the synchronization mechanism that matches the framework and operation rather than copying a generic sleep into every test.
How can I make generated tests deterministic?
Control the values and state that can vary between runs, and make each test establish the conditions it needs. Fuchsia’s guidance defines deterministic tests as tests that produce the same result on every run against the same revision of code. The test should make its important inputs visible and controllable, instead of depending on whichever values the machine, clock, scheduler, or another test happens to provide.
Control time and randomness
For time-dependent behavior, inject a fake clock rather than having the test rely on the machine’s wall clock. For randomized behavior, provide a controllable seed and record it on failure. Avoid generating an uncontrolled random value inside a test when a known input can exercise the same behavior. These controls make an intermittent result reproducible and let you distinguish a genuine application defect from a test whose inputs quietly change.
When randomness is part of the behavior being tested, do not remove it indiscriminately. Make the seed explicit so a failing case can be replayed, and include that seed with the test’s failure artifacts. This is especially relevant because the 2024 ICSE study found randomness was a more frequent source of flakiness in generated tests than networking or concurrency. It is a reason to inspect random inputs early, not to assume every failure has that cause.
Give each test independent state
Initialize and tear down test data within the test or its fixture. Avoid shared mutable globals and assumptions that another test has already run. A test should be able to run alone, in a different order, and—if the suite supports it—alongside other tests without colliding over shared state. Clean setup and teardown are not just housekeeping: they prevent a previous run from changing the inputs to the next one.
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If a test behaves differently depending on suite order, run it by itself and then after the suspected predecessor. Check whether the earlier test leaves data, changes a global value, or fails to restore a resource. If parallel execution triggers the problem, identify whether workers are sharing mutable state or relying on thread-safe behavior. The durable fix is to isolate the state or make the parallel interaction safe, rather than requiring a lucky order.
Choose the right boundary for external services
Mock an external service when the purpose is to test a unit or component independently of that service. Use separate contract or integration tests when the purpose is to verify real dependency behavior. This keeps a unit test from failing merely because an unrelated network dependency is unavailable while preserving a place to test the real integration.
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Make asynchronous work observable
Do not assume a background task has finished because a certain amount of time has elapsed. Wait for an observable completion condition or use the framework’s synchronization mechanism. If there is no reliable signal, that can point to a design or testability problem worth addressing rather than a reason to insert another delay. In a useful test, completion is something the test can observe and assert, not a guess based on elapsed time.
When should I change an assertion?
Change an assertion only when it is stricter than the product behavior the test is meant to protect. A weaker assertion that stops the red build but no longer checks the intended contract is not a fix.
For floating-point values, use a tolerance appropriate to the expected precision rather than requiring exact equality when the behavior does not promise it. For timing-sensitive behavior, assert the relevant outcome rather than an exact duration unless that timing is part of the contract. Likewise, avoid asserting incidental ordering or formatting when the product only promises a broader behavioral result. pytest flags overly strict floating-point and timing assertions as possible sources of flakiness.
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Before softening an assertion, confirm what the application is supposed to do. If two results differ in a way users or downstream code can observe, the test may have uncovered a real defect. If the difference is only an unspecified representation detail, adjust the assertion to check the stable contract instead. The key question is not whether a looser assertion turns the failure green; it is whether the revised check still distinguishes correct behavior from incorrect behavior.
Should I retry, quarantine, rewrite, or delete a flaky test?
A retry can keep a CI pipeline moving while a team investigates, but it does not make the test reliable. Select a mitigation based on whether the test still provides unique value and whether the cause has been identified.
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| Option | When it is useful | Risk or limit |
|---|---|---|
| Retry | As a temporary way to keep a large test suite moving while investigating a known flaky test. | A passing retry can hide an unresolved failure. Android recommends retries for large tests but still says flaky tests need fixing. |
| Quarantine | When a test must be separated from the blocking suite while its failure is investigated. | pytest describes rerun plugins and xfail quarantine as mitigations and warns that permanent quarantine is dangerous. |
| Rewrite at a lower level | When the test’s behavior matters but a high-level generated implementation hides the cause or introduces avoidable timing and environment dependencies. | Make sure the new test still checks the intended behavior; moving the test down a layer should not silently remove meaningful coverage. |
| Delete | When the behavior is already covered elsewhere and the flaky generated test adds no distinct protection. | Check for unique coverage before removal. Microsoft recommends fixing or removing unreliable tests and monitoring suite health. |
Quarantine and retries are containment, not a permanent reliability strategy. Set an owner and follow-up for a quarantined test, and monitor whether the suite’s failures and runtime improve after a fix. If the test’s behavior is covered elsewhere, removal can be more honest than keeping a permanently unreliable check.
There is a practical cost comparison here. A retry can add repeated work to CI and consume time while still leaving a misleading green result; a fixed sleep adds waiting to every run and may still fail on a slower machine; a condition-based wait spends time only while the needed state is not ready. Rewriting or deleting also has a cost: it takes review and can reduce coverage if done carelessly. Compare those costs against the test’s diagnostic value and unique contract coverage, not just whether the latest pipeline passed.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose between two possible fixes?
Prefer the change that removes the source of variation while preserving the behavior the test is meant to verify. A useful fix should improve reliability without turning a meaningful check into a pass-by-default.
- Determinism: Do identical inputs and the same code revision produce the same result?
- Isolation: Can the test run in any order, independently, and in parallel where supported?
- Synchronization: Does the test wait for a condition, or merely for time to pass?
- Diagnostic value: Will a future failure retain the logs, seed, screenshot, trace, or other evidence needed to explain it?
- Runtime cost: Does the fix reduce needless waiting or add expensive repeated work?
- Contract coverage: Does it test intended product behavior, or only suppress the symptom that caused the original failure?
A fix that passes more often but makes failures harder to diagnose or stops checking the real contract has traded visible failures for hidden risk. Prefer a deterministic test with useful failure artifacts over a longer sleep, an indiscriminate retry, or an assertion weakened without checking what the product promises.
Use the failure evidence to compare candidate fixes. If the seed is implicated, control and retain it. If order or parallelism changes the result, isolate state. If an asynchronous UI step races, wait for its readiness condition. If the test depends on a live service that is irrelevant to the behavior under test, move that dependency to an appropriate integration boundary. These fixes address different failure mechanisms; choosing the one that matches the evidence is more valuable than applying a generic reliability trick.
Frequently Asked Questions
Can AI-generated tests be reviewed like tests written by a developer?
Yes. Review the generated test as executable code: check its setup, cleanup, synchronization, inputs, dependencies, and assertions before relying on its result. The generator’s source does not establish that a test is either correct or incorrect.
Why does my AI-generated test pass sometimes and fail other times?
Usually, some input or execution condition is changing: shared state, test order, parallel work, timing, randomness, an external call, or an overly strict assertion. Repeat the test, vary one condition at a time, and preserve its seed, logs, timing, environment details, and failure artifacts to narrow down the cause.
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How many times should I rerun a test to find a flake?
There is no universal count. Fuchsia’s testing guidance says teams may use 100–1,000 local repetitions for tests prone to flakes. Use repeated runs as an investigation aid, and vary order, parallelism, seed, or environment when needed; one successful rerun does not establish reliability.
Should I keep a flaky test if it catches a real bug occasionally?
Only if it provides coverage that would otherwise be lost, and then treat its intermittent signal as a reliability problem to resolve rather than as acceptable noise. If another test already covers the same behavior, retaining a flaky duplicate may add more confusion than protection.
Can I use a random seed to reproduce a flaky test?
A seed helps when randomized inputs are part of the failure: save the seed from a failing run and rerun with it. It will not reproduce failures caused by unrelated factors such as shared state, timing, or a changing external dependency.
Should I retry a flaky test in CI?
A retry can temporarily keep a large suite moving while you investigate, but it can also hide an unresolved failure. Android recommends retries for large tests while still emphasizing that flaky tests need to be fixed; do not treat a passing retry as a permanent solution.
What should I use instead of sleep in a Selenium test?
Wait for the condition required by the next action, such as an element becoming clickable, expected text appearing, or a spinner disappearing. Selenium’s guidance is that “Sleeping is not waiting”: a fixed pause guesses how long readiness takes, while a condition-based wait checks for the state the test actually needs.
When is it better to delete a flaky generated test?
Delete it when the behavior is already covered elsewhere and the test adds no distinct protection. If it is unique coverage, fix it or rewrite it at a level that can test the intended contract deterministically.
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The Bottom Line
Fix the source of variation rather than masking it. Reproduce the failure, save the evidence, and match the remedy to the cause: control seeds and clocks, isolate state, wait for observable conditions, or adjust an assertion only when the product contract allows it. Use retries and quarantine as temporary containment, and keep a flaky test only when it provides unique coverage worth repairing.
This is primarily a code-and-process problem, not one that calls for a physical product. A software tool for CI flaky-test detection or test observability may help surface patterns, but the core fix remains a deterministic test and a clear diagnosis.
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