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How to Skip Specific Parametrized Pytests Based on Previous Failures

Learn how to manage test dependencies in `pytest` to skip tests intelligently based on previous failures, ensuring that only the necessary tests are ignored. --- This video is based on the question https://stackoverflow.com/q/71424302/ asked by the user '00__00__00' ( https://stackoverflow.com/u/1506850/ ) and on the answer https://stackoverflow.com/a/71429379/ provided by the user 'MrBean Bremen' ( https://stackoverflow.com/u/12480730/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Skipping specific parametrized pytests based on failure for specific parameters Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Skip Specific Parametrized Pytests Based on Previous Failures When working with automated testing in Python using pytest, you might find yourself needing to run multiple parametrized tests efficiently. But what if you want to conditionally skip certain tests based on failures from previous tests with the same parameters? This challenge arises commonly in projects where dependencies between tests are critical. The good news is that you can manage these dependencies effectively using the pytest-dependency plugin. Let's explore how to achieve this in a systematic fashion. Understanding the Problem Imagine you have multiple tests that use a set of parameters, and you want to run these tests against a dataset. In some situations, a failure in one test may make it unnecessary to run other tests with the same parameters. For example, if test1 fails with a particular parameter, you want test2 to skip that specific run instead of skipping everything related to test1. By default, if you employ the pytest-depends functionality, a failure in test1 will lead to test2 skipping all of its parameters. This isn't the behavior we want, as it can lead to skipped tests that could have passed. Solution Breakdown Step 1: Basic Setup To solve this, we need to set up each test with dependency markers that specify which parameterized test they are dependent on. Here’s how you can do it using decorators: [[See Video to Reveal this Text or Code Snippet]] In this case, if test1 fails for x=2, test2 will skip the execution for x=2 only. Step 2: Handling Dynamic Test Data If you prefer to handle test data dynamically or reduce clutter in your tests, you can define a helper function that generates parameterized test cases with dependencies attached. Here’s how: [[See Video to Reveal this Text or Code Snippet]] This setup will ensure that if test1[2] fails, then test2[2], and other dependent tests will be skipped accordingly. Step 3: Adapting for Multiple Arguments If you have tests that require more than one parameter, the same principles apply with slight adjustments. Here's an example: [[See Video to Reveal this Text or Code Snippet]] Implementation with Generic Data If you want to handle multi-parameterised tests dynamically, you can use a helper function similarly: [[See Video to Reveal this Text or Code Snippet]] Conclusion By properly utilizing the pytest-dependency plugin, you can structure your tests to manage dependencies intuitively. This allows you to create a robust testing methodology where only the necessary tests are skipped based on previous outcomes. This improvement not only saves time but also enhances the clarity of your testing strategy. Now, when crafting your test cases, remember to think about their dependencies—it's a small adaptation that can lead to significant gains in efficiency! Happy testing!