The Easiest Way to Identify Flaky Tests in Jest

Flaky tests are the bane of every developer’s existence. They pass one minute and fail the next, making it challenging to trust your test suite and causing frustration and wasted time. Fortunately, there’s a tool called flaky-test-detector that employs the KISS (Keep It Simple, Stupid) approach to help you identify and manage flaky tests in your Jest test suite.

Introducing flaky-test-detector

flaky-test-detector is a command-line tool that helps you identify flaky tests in your test suite by running your tests multiple times and analysing the results. It supports various test runners, including Jest, and can be easily integrated into your project. For this example, we’ll be using yarn to install and run flaky-test-detector.

Setting up flaky-test-detector with Jest:

To use flaky-test-detector with Jest, you'll need to follow these steps:

1. Generate Jest test results in JUnit format:

First, you’ll need to generate Jest test results in the JUnit format (as this is a limitation with the tool). This can be done by using the jest-junit package:

yarn add --dev jest-junit

Then, configure Jest to generate JUnit reports by adding the following to your jest.config file:

{
  "reporters": [ "default", "jest-junit" ]
}

2. Install and configure flaky-test-detector

Next, install flaky-test-detector and the JUnit parser:

yarn add --dev @smartesting/flaky-test-detector

Then, configure flaky-test-detector to use the JUnit parser and run your Jest tests multiple times (e.g., 5 times):

yarn flaky-test-detector --run-tests "yarn test" --repeat=5 --test-output-file=junit.xml --test-output-format=junit

If you want, you can turn this command-line execution into a script in your package.json, like this:

{  
  "detectFlaky:unitTests": "flaky-test-detector --run-tests \"yarn test:browser\" --repeat=5 --test-output-file=junit.xml --test-output-format=junit"
}

This command will run your Jest tests 5 times and generate a JUnit report named junit.xml. The JUnit parser built into flaky-test-detector will then parse the Jest/JUnit results and detect any flaky tests based on inconsistent test results across the 5 runs.

Interpreting the Results

After running flaky-test-detector, it will output a summary of any flaky tests it encountered in the terminal. If you’re wondering how the tool works, the main error reporting happens in the BasicReporter class. When flaky tests are detected, the flakyTestsFound method is called, which logs the number of flaky tests found and their names to the logger.error method.

For example:

Found 2 flaky tests
 - myFlakyTest1
 - myFlakyTest2

Additionally, after detecting flaky tests, detectFlakyTests throws an error: throw new Error('Flaky tests found'). This ensures that the process fails, providing a clear and actionable output when flaky tests are detected.

With this information, you can investigate the root causes of the flaky tests and take appropriate action. Here are some potential mitigation strategies and examples:

1. Refactoring the Test: If the flakiness is caused by issues within the test itself, such as race conditions or improper setup/teardown, refactoring the test code may be necessary. For example, if a test depends on the order of asynchronous operations, you could introduce synchronisation mechanisms or use libraries like async/await to ensure proper control flow.

2. Fixing Underlying Issues: If the flakiness stems from issues in the production code, fixing the underlying bugs or race conditions should be the priority. This might involve adjusting concurrency control mechanisms, fixing data access race conditions, or addressing any other root causes that lead to non-deterministic behaviour.

3. Improving Test Isolation: Flaky tests can sometimes be caused by shared state or dependencies between tests. To mitigate this, you can leverage test doubles (mocks, stubs, or fakes) to isolate the tests from external dependencies or shared resources. This ensures that each test runs in a controlled and predictable environment, reducing the risk of flakiness.

4. Improving Test Data Management: If the flakiness is related to test data, consider using external data sources (e.g., JSON files or databases) or data factories to generate test data consistently. This approach separates test data from test logic, making it easier to maintain and reducing the risk of flakiness caused by hard-coded test data.

5. Parallelisation and Test Ordering: In some cases, flakiness can be caused by parallel test execution or test ordering issues. To address this, you can experiment with different parallelisation strategies or consider running tests in a specific order or in serial order by using --runInBand to mitigate potential race conditions or conflicts.

By leveraging these mitigation strategies and addressing the root causes of flakiness, you can gradually improve the reliability and stability of your test suite, ensuring that your tests provide consistent and trustworthy feedback throughout the development lifecycle.

Conclusion

Flaky tests can be a frustrating and time-consuming issue, but with the help of flaky-test-detector, you can easily identify and manage them in your Jest test suite. By integrating this tool into your development workflow, you can ensure that your tests are reliable and consistent, saving you time and increasing your confidence in your codebase.