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Similarly to production code, code smells also occur in test code, where theyare called test smells. Test smells have a detrimental effect not only on testcode but also on the production code that is being tested. To date, themajority of the research on test smells has been focusing on programminglanguages such as Java and Scala. However, there are no available automatedtools to support the identification of test smells for Python, despite itsrapid growth in popularity in recent years. In this paper, we strive to extendthe research to Python, build a tool for detecting test smells in thislanguage, and conduct an empirical analysis of test smells in Python projects.  We started by gathering a list of test smells from existing research andselecting test smells that can be considered language-agnostic or have similarfunctionality in Python's standard Unittest framework. In total, we identified17 diverse test smells. Additionally, we searched for Python-specific testsmells by mining frequent code change patterns that can be considered as eitherfixing or introducing test smells. Based on these changes, we proposed our owntest smell called Suboptimal assert. To detect all these test smells, wedeveloped a tool called PyNose in the form of a plugin to PyCharm, a popularPython IDE. Finally, we conducted a large-scale empirical investigation aimedat analyzing the prevalence of test smells in Python code. Our results showthat 98% of the projects and 84% of the test suites in the studied datasetcontain at least one test smell. Our proposed Suboptimal assert smell wasdetected in as much as 70.6% of the projects, making it a valuable addition tothe list.

PyNose: A Test Smell Detector For Python