Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a high demand of resources. Meta-heuristic search algorithms -- that effectively solve the test-generation problem -- have been proposed as a solution to also efficiently tune parameters. In this work we explore the use of differential evolution as a means for tuning the hyperparameters of the DynaMOSA and MIO many-objective search algorithms as implemented in the Pynguin framework. Our results show that significant improvement of the resulting test suite's coverage is possible with the tuned DynaMOSA algorithm and that differential evolution is more efficient than basic grid search.

翻译：基于搜索的测试生成算法具有无数配置选项。用户很少调整这些选项，通常坚持使用默认值，但这可能无法获得最佳结果。调整算法的超参数是寻找更优超参数值的方法，但通常需要大量资源。元启发式搜索算法——能有效解决测试生成问题——已被提出作为高效调参的解决方案。在本研究中，我们探索使用差分进化算法来调优Pynguin框架中实现的DynaMOSA和MIO多目标搜索算法的超参数。我们的结果表明，通过调优的DynaMOSA算法可以显著提升生成测试套件的覆盖率，且差分进化算法比基础网格搜索更高效。