What Are Flaky Tests? How to Identify, Prevent, and Fix Them

Test automation has become essential to modern software development. It makes sure that code changes do not introduce regressions. But one persistent issue in test automation is the occurrence of the flaky tests. Flaky tests produce inconsistent results without any corresponding change in the application code or environment. They increase the testing time, slow down CI pipelines, and in some cases hide real errors. 

This guide will provide a comprehensive overview of flaky tests, explaining what they are, the common causes, and how to identify and measure test flakiness. 

An automated test case becomes flaky when it does not produce consistent results given the same codebase and environment

What Are Flaky Tests?

An automated test case becomes flaky when it does not produce consistent results given the same codebase and environment. For example, you might run the same end-to-end UI test twice on the same branch, without changing the application code or the test script, and see it pass the first time and fail the second. These failures are non-deterministic and often unrelated to the actual bugs. 

It’s possible for any test to be flaky. End-to-end tests, particularly those that involve a browser or interact with external systems, are the most susceptible. They have more moving parts, depend on more systems working together, and are more likely to be affected by transient factors such as network delays and slow rendering. 

The Costs of Flaky Tests

The most immediate consequences of flakiness is a loss of trust in the test results. Developers who see irregular failures often doubt whether a red build truly signals a bug. This overtime goes against the integrity of automated testing.

 It also increases the time spent triaging and rerunning tests to separate real bugs from false positives, which adds friction to the development process. 

This waste of time has knock-on effects, as teams may delay deployments or releases while investigating failures. In some cases, flaky tests can mask actual issues, allowing bugs to slip into production because they are dismissed as another case of instability.

What Causes Flaky Tests?

There can be a number of different reasons behind test flakiness, but most can be traced to a few consistent patterns, which include:

Time and Synchronization Issues

If your test relies on UI elements or asynchronous processes, it might fail if it does not wait appropriately. For example, a test that attempts to click a button before it becomes visible will fail sporadically depending on page load speed or network latency. This often happens when tests use fixed times instead of conditional waits that verify readiness. 

Flaky tests may pass locally but fail in CI due to such environmental variations

Environment Variability 

Differences in test environment configurations also introduce flakiness. If there are variations in CPU load, memory availability, network conditions, or even browser versions, it can cause tests to behave differently. 

Flaky tests may pass locally but fail in CI due to such environmental variations. Also, if you have a team that works in different locations with different time zones, or if your CI/CD pipeline is in a different time zone, a test that validates dates can become flaky—especially during the beginning of the month or in leap years.

Resource Contention and Test Order Dependency

Parallel test execution and test order dependency can also contribute to the flakiness if tests are not isolated properly. This is also the case if they’re dependent on side effects or shared state left by previous tests, or if they’re run in a different order. This leads to race conditions or conflict over shared resources like databases, file systems, or network ports. 

External Dependencies 

Tests that rely on third-party services such as APIs, cloud services, or other systems outside your control may fail due to outages, rate limiting, or network issues. Unreliable external dependencies increase the risk of tests becoming flaky. 

How to Identify Flaky Tests

Detecting flaky tests requires careful analysis and a systematic approach. Here are the key strategies to identify flakiness in your test suite:

1. Repeated Test Execution

One of the simplest methods to identify flaky tests is to run suspected tests multiple times under identical conditions. Tests that fail sporadically are likely flaky. This approach can be automated by running tests in loops or configuring CI jobs to rerun failed tests a set number of times.

2. Analyzing Test History and Patterns

CI systems often keep logs and records of test results over time. Analyzing this data for inconsistent failures or unstable patterns can help pinpoint flaky tests. Monitoring trends in test failures across branches and commits provides insight into problematic areas. 

3. Isolating Tests

Running tests individually or in small batches can reveal order dependencies and interference between tests. Isolated tests should ideally produce consistent results, thus helping in identifying sources of flakiness. 

4. Measuring Flakiness Metrics

Quantitative metrics can be valuable in assessing flakiness. Common metrics for measuring flakiness include failure rate and stability score. Tracking these metrics over time can guide prioritization of remediation efforts.

How to Prevent Flaky Tests

There is no perfect guide that can help you avoid flaky tests. But incorporating certain best practices into your testing process can significantly reduce flakiness.

Use automation frameworks' builtin mechanisms for waiting on UI elements or asynchronous operations

Implement Explicit Waits and Synchronization

Use automation frameworks' builtin mechanisms for waiting on UI elements or asynchronous operations. Playwright, for example, offers auto-wait features that wait for elements to be ready before interaction. Avoid arbitrary sleeps or fixed timeouts that can either be too short or unnecessarily long. 

Ensure Test Environment Stability

A stable environment is crucial for preventing flaky tests. Standardizing test infrastructure, whether through containerization, VMs, or cloud-hosted CI runners reduces variability between runs. Also, standardizing browser versions and using network virtualization or mocks to control network conditions can help prevent flaky tests.

Design Independent Tests and Automate Environment Cleanup

Ideally, tests should not depend on external state or the results of previous tests. Each test should perform its own setup and cleanup to maintain isolation. This improves reliability and facilitates parallel execution. 

To ensure consistent starting conditions, incorporate teardown steps to reset databases, clear caches, or restart services after each test.

Minimize External Dependencies and Use Controlled Test Data

Where possible, replace calls to external APIs or services with mocks or stubs. This avoids failures caused by third-party instability and reduces test execution time. Also, avoid generating random data on the fly unless necessary. Instead, use seeded random generators or predefined datasets. 

How to Fix Flaky Tests

Once you have identified a flaky test, the goal should not be to simply patch it but rather to rebuild confidence in its results. That requires examining how and why it fails, strengthening its design, and ensuring it can be trusted in regular runs. For that, you must do the following:

1. Diagnose Failure Causes

Gather detailed logs and screenshots during test failures. Analyze these artifacts to understand timing issues, UI states, or environment factors contributing to flakiness.

2. Refactor for Reliability 

Improve test code by adding proper waits, reducing complexity, and removing fragile selectors or assertions. Replace hard-coded waits with dynamic conditions. Break down overly complex tests into smaller and more stable units.

3. Prioritize Based on Impact

Focus first on tests that fail frequently or block critical workflows. Fix these before addressing less impactful flakiness.

4. Use Reruns Judiciously

Reruns can temporarily mask flakiness by rerunning failed tests. They should not be considered a long-term solution. Overuse of reruns may hide underlying problems and reduce test suite credibility.

Continuously monitor flaky test metrics and alert teams to regressions

5. Establish Monitoring and Alerting

Continuously monitor flaky test metrics and alert teams to regressions. Implement dashboards and reporting tools to visualize test stability trends.

Tools and Techniques to Help Manage Flaky Tests

Several tools and frameworks can help in identifying and reducing flaky tests:

Autify Nexus

Autify Nexus is an AI powered low-code test automation platform built on Playwright. It offers advanced test authoring capabilities that combine low-code ease with flexibility. It includes builtin synchronization features that automatically wait for UI readiness, reducing time related flakiness. It also provides detailed analytics and integration with CI/CD pipeline, allowing continuous monitoring of test stability.

Playwright

As the foundation of Autify Nexus, Playwright itself provides auto-wait behavior, network interception, and debugging capabilities that help create tests resilient to common flakiness triggers.

CI Integration with Jenkins or Github Actions

CI tools like Jenkins and GitHub Actions can be used for early detection of flaky tests. These tools can track reruns, generate stability metrics, and flag patterns over time. You can also supplement these tools with dedicated dashboards that highlight frequently failing tests.

Conclusion

Flaky tests are a drain on productivity and threat to integrity of automated testing. They stem from a combination of timing issues, environmental inconsistencies, and poor test isolation, and they can undermine confidence in your CI/CD process if left unchecked.

If you take a structured approach to identifying and addressing them by combining tools like Autify Nexus with disciplined design practices, you can significantly improve the stability of your test suites and prevent flaky tests.