An Empirical Study of Automated Unit Test Generation for Python
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Various mature automated test generation tools exist for statically typed programming languages such as Java. Automatically generating tests for dynamically typed programming languages such as Python, however, is substantially more difficult due to the dynamic nature of these languages as well as the lack of type information. Our Pynguin framework provides automated unit test generation for Python. In this paper, we extend our previous work on Pynguin to support more aspects of the Python language, and by studying a larger variety of well-established state of the art test-generation algorithms, namely DynaMOSA, MIO, and MOSA. Our experiments confirm that evolutionary algorithms can outperform random test generation also in the context of Python, and similar to the Java world, MOSA and DynaMOSA yield the highest coverage results. However, our results demonstrate that there are still fundamental issues, such as inferring type information for code without this information, for test generation for Python to be solved.
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