What is Flaky test? A Detailed Guide

Flaky tests in software testing are tests that exhibit inconsistent behavior across different execution runs. Such unpredictability leads to unreliable results, making it challenging to assess test coverage accurately. These tests can produce both false positives and false negatives in reports, potentially causing unnecessary escalations. False negatives occur when a test wrongly identifies a defect in the software, even when the software is functioning correctly, leading to wasted time and effort in investigating non-existing issues.

Addressing flaky tests requires a thorough identification and understanding of their underlying causes. By pinpointing the root reasons for their flakiness, testers and developers can take essential steps towards rectifying these tests, thereby ensuring a more stable and reliable test automation process.

Reasons for flaky Tests:

1.Timing Issues 

Tests that interact with ui elements or API calls may fail intermittently as the reponses can be inconsistent at times or there could be asynchronous operations without appropriate synchronization.

In almost all applications today there are multiple ajax calls included, because of which the application may behave or respond inconsistently. In these cases, flaky tests may happen. 

If any tests are supposed to wait for any specific UI elements and hard coded value is used for the waits , then it can cause flaky tests as the time taken for the elements may vary depending on the network speed and many other parameters. Its always advisable to use dynamic waits for the elements wherever necessary.

2. Test Dependency

Test dependency refers to a situation where the outcomes of one test is influenced by other tests , it can cause various issues like false positive or negative which leads to a flaky test ,where the stability and reliability of the tests are compromised.

When multiple test cases share same data set for their execution , it can lead to dependency issues .If the previous test has modified the data which the next test case is not able to follow , the second one will fail.

Test cases which mostly rely on network services or any 3rd party application integration are more prone to failure. If these external dependencies are not consistently available or experience changes, it can impact the reliability of the test results.

3.Multi threaded scenarios:

It happens in a situation where multiple threads are occurring at the same time or multiple test cases with different data sets are trying to access the same thread concurrently which can result in a flakyness of the tests and can result in unpredictable result .

Resources , data files that are shared among different test cases are the cause of this issue , if multiple threads are trying to access the resources at the same time, it may fail in an inconsistent way.

The critical sections are portions of code where shared resources are accessed and modified. When multiple threads or processes attempt to access the same critical section simultaneously, this kind of situation may occur.

Non atomic operations are when a test has multiple steps which can be intervened by other tests or processes may lead to inconsistent test results .

4.Environment dependency:

The accuracy of the test cases also depends on the environment where the code was written and the code being executed in a realtime basis. A test being executed in a different operating system, different configurations and other external factors  can impact the behavior of the application under test, leading to inconsistent test results and potentially causing flaky tests .

The software environment can vary with different operating systems, browsers, and versions of software dependencies. Each configuration may have its peculiarities or bugs, which could lead to different behavior in the application.

Test automation may run on various hardware configurations, such as different processors, memory sizes, or graphics cards,different version of OS. These hardware differences can affect the application’s performance and behavior, leading to varying test outcomes. Also, in virtualized or containerized environments, differences in host configurations or resource allocations can impact the behavior of the application and tests.



5.Unreliable Test Frameworks:

Unreliable test frameworks may produce varying outcomes for the same test scenarios, even when the application’s behavior remains unchanged. Such inconsistencies make it challenging to rely on test results and undermine the confidence in the testing process.

Some test frameworks may have limited compatibility with different operating systems, browsers, or devices, making it challenging to ensure comprehensive test coverage across various environments.

And Insufficient or unclear documentation of the test framework can hinder effective adoption and usage. Testers may struggle to understand the framework’s features, configuration options, and best practices.

Unreliable frameworks may have poor support, infrequent updates, or an inactive development community. This lack of maintenance can result in outdated dependencies, security vulnerabilities, and compatibility issues.

Test frameworks that suffer from performance issues or do not scale well to handle large test suites can impact productivity and efficiency in the testing process.

If a test framework lacks integrations with other testing tools or continuous integration systems, it may hinder the overall test automation workflow.

6.Non Deterministic assertion:

Non-deterministic assertions in software testing refer to assertions or checks that are not consistently true or false when applied to the same test case across multiple test runs. In other words, the outcome of these assertions may vary based on factors such as timing, external dependencies, or environment conditions. This variability can lead to flaky tests, where the same test may produce different results on different test executions.

One common example of non-deterministic assertions is when tests involve interactions with dynamic web elements or asynchronous operations. These tests might assert the presence or absence of an element on the page, but due to timing issues, the element’s state might change between test runs, causing the assertion to fail unpredictably. Similarly, when tests involve interactions with external APIs or databases, non-deterministic response times or data changes can affect the outcome of assertions.

Best Practices for Identifying and Reducing Flaky Tests

Identifying and reducing flaky tests is essential for maintaining a reliable and efficient test automation process. Here are 15 best practices to help you achieve this goal:

1. Monitor Test Execution History: Over a period of time, extract the test execution history and track the results to find patterns of flakiness. Analyze test failures and inconsistencies to identify the flaky tests.

2. Tag Flaky Tests: Tag separate flaky tests in your test suite for separate reporting and handling. This helps differentiate flaky test failures from genuine defects. You would get a reliable report for the consistent test cases.

3. Run Tests in Isolation: Make sure each test case can work on its own and doesn’t need the data or conditions from other tests. Keep tests separate to prevent problems with shared information. Try to divide and organize tests as much as you can.

4. Use Stable Test Data: Maintain clean and consistent test data to minimize the impact of data-related flakiness. Reset data to a known state before test execution. It is preferable to use real time data or mock the production data in automation environments for better results. Keep on updating or refreshing your automation data set for better results. 

5. Implement Retry Mechanisms: If the test is not critical and sometimes produces inconsistent results, you can try adding a retry mechanism. This will automatically rerun the test, improving the likelihood of getting a reliable outcome.

6. Automate Cleanup Tasks: Ensure that tests are designed to undo any modifications made during test execution, leaving a clean environment for the next test run. Additionally, consider implementing automatic data clean-up or cache removal after each test execution to maintain a consistent testing environment.

7. Regularly Update Test Dependencies: To ensure a stable and dependable test automation process, it is essential to regularly update your test automation tools and libraries. Also, keep a vigilant watch on dependencies and third-party libraries used in your tests. Updating them as required will prevent flakiness and promote smoother test execution.

8. Synchronize Test Actions: Use dynamic waits and synchronization techniques to ensure tests interact with elements and resources only when they are ready and available.

9. Mock External Dependencies: Mocking or stubbing can be employed for external services or APIs in order to create controlled and predictable test environments. This approach prevents the need to modify the actual API and leads to more effective test execution.

10. Leverage Parallel Testing: Run tests in parallel to speed up execution and detect flakiness faster. However, design tests to handle concurrent execution without interference.

11. Investigate Flaky Tests Promptly: To tackle flakiness, performing debugging, enhancing logging, and tracing test execution can help identify potential timing or environment issues, ultimately reducing flakiness.

12. Continuous Integration and Reporting: By integrating test automation with a CI/CD pipeline, tests can be automatically run, and timely feedback on flaky tests can be received. Frequent test runs aid in quickly identifying flaky tests.

13. Document Test Environments: It is crucial to maintain clear and detailed documentation of test environments, configurations, and dependencies to ensure consistency. Documentation plays a vital role in establishing a robust framework.

14. Continuous Test Maintenance: Regularly review and update tests as the application evolves to maintain relevancy and stability.

15. Collaboration between Teams: Foster effective communication and collaboration between testers, developers, and stakeholders to collectively address flaky tests and improve overall test reliability.

Leveraging Tools and Techniques to Mitigate Flaky Tests

You can effectively reduce flaky tests by using different tools and techniques that improve the stability and reliability of your test automation.

Here’s one pointer for each tool:

1. Testsigma (Test Automation Platform): Testsigma is a no-code test automation platform that lets you automate your tests in simple English, intelligently handles dynamic web applications and provides self-healing mechanisms to reduce flakiness.

2. Selenium: Selenium is a popular automation framework that supports various programming languages and browsers for cross-browser testing. Leveraging implicit and explicit waits in Selenium helps ensure tests wait for elements to be ready before interacting with them, reducing timing-related flakiness.

3. TestNG (Test Next Generation): TestNG is a testing framework that supports test dependency management, allowing you to manage test execution order and reduce dependencies between tests.

4. JUnit: JUnit is a widely-used unit testing framework for Java, which helps in writing and executing unit tests. Using JUnit’s built-in assertion methods ensures stable and deterministic validation of test outcomes.

5. Cucumber (BDD Framework): Cucumber enables writing tests in a human-readable format (Gherkin) and encourages collaboration between stakeholders. Clear and descriptive scenarios in Cucumber tests make it easier to identify flaky test cases and their behavior.

Addressing the Root Cause of Flaky Tests

Addressing the root cause of flaky tests is vital for a robust test automation process. Thoroughly analyzing test failures helps identify underlying issues causing inconsistency. Mitigating flaky tests involves handling timing problems, synchronizing test actions, and using stable test data. Employing proper waits, dynamic locators, and mock services also stabilizes tests. Regularly updating dependencies, maintaining documentation, and collaborating with team members resolve flakiness. Continuous monitoring, prompt investigation, and integrating with CI/CD pipelines aid in early detection and resolution, ensuring a reliable and efficient test suite.

Conclusion:

In conclusion, flaky tests present a challenge in test automation with their inconsistent behavior. They can lead to wasted efforts and reduced confidence in the testing process. However, by identifying root causes, using stable data, and leveraging appropriate tools, we can mitigate flakiness and establish a more reliable test suite. Proactive measures and collaboration between testers and developers are crucial for achieving consistent and high-quality test automation.