Changes from the Trenches: Should We Automate Them?

Golubev, Yaroslav; Li, Jiawei; Bushev, Viacheslav; Bryksin, Timofey; Ahmed, Iftekhar

doi:10.48550/arxiv.2105.10157

Cited by 1 publication

(3 citation statements)

References 29 publications

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“…In addition to the test smells identified above, our goal was to include Python-specific test smells. To discover Python-specific test smells, we used a tool called PYTHON-CHANGEMINER [18] to search for frequent change patterns in the histories of test suites. We explain the steps of this process in detail in this section.…”

Section: B Identifying Python-specific Test Smellsmentioning

confidence: 99%

“…2) Change pattern mining: To identify Python-specific test smells, we started by mining the histories of the collected projects and finding patterns in the changes made to test files that might be considered as either fixing or introducing a test smell. We extracted all changes made to Python test files from the identified 450 projects and processed these files using PYTHONCHANGEMINER [18].…”

Section: ) Project Selectionmentioning

confidence: 99%

“…These test smells were all adopted from other papers dedicated to different programming languages, but it is natural to assume that Python has its own specific test smells. To discover them, we used a tool called PYTHONCHANGEMINER [18] to search for frequent change patterns in test suites. We manually evaluated 159 patterns that occur in at least three different projects and identified 32 possible changes that are related to assert functions in Unittest and are aimed at making the tests more specific and simplify the understanding of the testing logic.…”

Section: Introductionmentioning

confidence: 99%

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PyNose: A Test Smell Detector For Python

Wang

Golubev²,

Smirnov³

et al. 2021

Preprint

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Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python projects.We started by gathering a list of test smells from existing research and selecting test smells that can be considered languageagnostic or have similar functionality in Python's standard Unittest framework. In total, we identified 17 diverse test smells. Additionally, we searched for Python-specific test smells by mining frequent code change patterns that can be considered as either fixing or introducing test smells. Based on these changes, we proposed our own test smell called Suboptimal Assert. To detect all these test smells, we developed a tool called PYNOSE in the form of a plugin to PyCharm, a popular Python IDE. Finally, we conducted a large-scale empirical investigation aimed at analyzing the prevalence of test smells in Python code. Our results show that 98% of the projects and 84% of the test suites in the studied dataset contain at least one test smell. Our proposed Suboptimal Assert smell was detected in as much as 70.6% of the projects, making it a valuable addition to the list.Index Terms-Test smells, code smells, Python, empirical studies, code change patterns, mining software repositories 1 To avoid the ambiguity that exists in testing terminology between languages and frameworks, in this paper, we will always refer to individual tests or test methods as test cases and to classes that group them as test suites.

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Section: B Identifying Python-specific Test Smellsmentioning

confidence: 99%

Section: ) Project Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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