In software development and automated testing, consistency really matters. One of the most frustrating barriers that developers and QA engineers encounter is a little something we call flaky tests: tests that pass or fail at random times with no changes to the code.
These googly eyed tests tend to do the most damage and can produce unreliable results which erodes trust in the testing function and can even cause release cycles to slow down especially in the architectural context of CI/CD pipelines.
In this article, we will explore what flaky tests are, why they’ve become more troublesome in modern technology workflows, as well as the causes, challenges, interesting real world use cases, and how they can be classified relative to other types of test failures. We will also discuss strategies or best practices for spotting and beating flaky tests to keep your test suite healthy and reliable.
What is a Flaky Test?
A flaky test is an automated test that yields inconsistent results, occasionally passing and occasionally failing despite no code changes to the application. When the test is flaky it becomes unreliable and therefore is something that can’t be trusted to give needed feedback during continuous integration (CI) and continuous deployment processes.
Flaky tests can cause confusion for developers, either suggesting bugs that don’t exist or incorrectly passing to hide actual bugs. With enough time, teams can begin ignoring failures altogether which can erode the feedback loop of automated testing.
Why Are Flaky Tests a Problem?
Today’s DevOps and Agile workflows are reliant on test automation to ensure code changes are deployed quickly and safely. As tests become flaky multiple issues arise:
1. Delayed Releases
CI/CD pipelines will often block the final deployment if a test is marked as failed. However if the test is flaky whether or not the test is also an actual failure, the test can cause the release to be delayed. The team can often wind up spending several hours in one sprint trying to deploy the code.
2. Eroded Trust in Test Results
The team can become desensitized to test failures, as they may convince themselves that a flaky test is not an actual defect. This poses a risk of shipping faulty code to production.
3. Extra Debugging Time
Because flaky tests are not infallible and reproducible, it can take developers hours and days to understand the test failure, only to realize that the failure was unrelated to the code change.
4. Resource Drain
The time it takes to run flaky tests multiple times to decide whether a failure was real or flaky also take compute resources and developer attention.
Common Flaky Test Causes
It is important to identify the causes of flaky tests in order to address them. Some common causes of flaky tests are:
Asynchronous Processes
Generally, flaky tests are caused by timing issues and are the result of a test interacting with an API, a background job, or delayed UI updates. A test may pass never checking the state until too early or checks it too late and fails.
Environment Related
If your test environment is not as similar as possible to production (or varies from test to test) inconsistent behavior/results could occur. An example could be latency in the network, memory availability, OS Dependencies, etc.
Dependency on Other Tests
Tests that share resources (such as a database or even global state) can also cause problems, where one test in particular can influence another test’s failure, usually happening only when the tests are run together, or only in particular order.
Poor Test Design
Having hardcoded waits, unhandled exceptions, and poorly scoped selectors are also contributors to having flaky tests because these make tests brittle and sensitive to minute adjustments.
How to Detect and Identify Flaky Tests
Catching flaky tests requires good observability and analytics in your testing infrastructure. Here are some strategies:
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Run tests multiple times to surface inconsistencies.
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Use test reporting tools (like BrowserStack Test Observability, CircleCI Insights, or GitHub Actions Matrix) to identify patterns across test runs.
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Look for non deterministic failures—tests that pass locally but fail in CI, or fail intermittently with no code changes.
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Tag or quarantine suspected flaky tests so they don’t block deployments while being investigated.
Flaky Test Use Cases: Real Life Scenarios
1. UI Testing in React
React applications generally have asynchronous updates to the DOM, so a test that checks for a modal that is assumed to have been drawn in the DOM immediately after clicking the button will flop if the modal is rendered just a few milliseconds later in a CI.
2. Mobile App Testing
The mobile test environment can differ slightly between devices or both simulators or emulators. A flaky test can arise from a race condition between the app rendering and simulating user input.
3. Integration Testing External APIs
If your application is dependent on a third party service, the rate limit, and network latency could greatly affect API response time causing intermittent test failures.
How Keploy Eliminates Flaky Tests
Keploy is an open source testing platform that assists teams in reducing flaky tests by creating deterministic test cases and mocks based on actual traffic. By capturing, and then replaying, real API interactions, Keploy helps you keep your tests stable and independent from environmental or third party instability.
Here are some of the ways Keploy addresses flakiness:
Automatically generates test cases and mocks from live traffic, therefore realism and repeatability.
Completely remove dependencies on external systems during testing by stubbing intelligently, therefore reducing network based flakiness.
Offers consistent integration and unit tests across environments and contributes to CI pipelines working effectively.
Keploy is great for backend teams who want to improve test reliability while scaling their microservices, without adding more manual testing effort.
Alternatives and Comparisons: Flaky vs. Non Flaky Failures
Let’s compare flaky test failures with other types of test failures to better understand how to respond to them:
| Failure Type | Cause | Frequency | Resolution Strategy |
|---|---|---|---|
| Flaky Test Failure | Non-deterministic/test issues | Intermittent | Stabilize test, improve reliability |
| Reproducible Failure | Real bug or broken functionality | Consistent | Fix the underlying code or logic |
| Infrastructure Error | CI tool/network/config failure | Rare | Improve CI setup or resilience |
Unlike genuine bugs, flaky tests often signal test fragility or system instability rather than a flaw in application logic.
Best Practices to Minimize Flaky Tests
Eliminating flaky tests isn’t always easy, but there are best practices that can help:
Use Explicit Waits Instead of Hardcoded Delays
Avoid sleep() or static timeouts. Use explicit waits based on conditions like “element is visible” or “API response received.”
Run Tests in Isolation
Ensure each test runs independently without relying on shared global state or ordering.
Reset State Between Tests
Clear cache, database, or environment variables before each test run to prevent contamination.
Mock External Dependencies
Use mocks or stubs for unstable external services to avoid variability in responses.
Use Test Analytics Tools
Platforms like BrowserStack or open source solutions like Keploy can help identify and reduce flaky tests by improving test visibility and automating reliable test generation.
Conclusion
Flaky tests are more than just a minor annoyance—they’re a significant bottleneck in achieving reliable, fast paced software delivery. By understanding their causes, identifying them early, and applying robust engineering practices, development teams can greatly reduce their impact.
Open source tools like Keploy help streamline this process by creating stable, deterministic test cases and mocks, reducing external dependencies and human error. In modern testing environments where CI/CD pipelines are the norm, reducing test flakiness should be a top priority. Not only does it ensure trust in your automation suite, but it also boosts confidence in every release, leading to better software and happier users.
Related Reading
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Getting Started with Microservices Testing
FAQs About Flaky Tests
What is a flaky test?
A flaky test is a test that behaves inconsistently, passing or failing without changes to the code as a result of timing issues, environmental instability, or poor design.
Why do flaky tests happen?
There are many causes of flaky tests but they mostly tie back to asynchronous operations, shared dependencies, unstable environment, or poor implementation of tests.
How do I know if a test is flaky?
If you see a test fail sometimes and cannot reproduce reliably with the same conditions, it is probably flaky.
Should I delete flaky tests?
Not necessarily. Quarantine, investigate and fix are the best actions. If a flaky test is deleted, regression could go unnoticed.
Can flaky tests be automated?
Yes. There are tools such as Keploy, that can automate the recognition of a flaky test and regenerate a reliable test case, so that the real file never has flaky tests in the first place.
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