多样性是否改善了移动应用程序的测试套件生成？
Does Diversity Improve the Test Suite Generation for Mobile Applications?
摘要：
在基于搜索的软件工程中，我们经常使用带有默认配置的流行算法，这通常会导致次优结果，或者我们在试错的基础上执行实验来识别配置，这可能会为特定问题带来更好的结果。为了获得更好的结果，同时避免试错实验，适应度景观分析有助于理解搜索问题，并做出明智的决策。在本文中，我们研究的搜索问题的测试套件生成的移动应用程序（应用程序）使用SAPIENZ的启发式是默认的NSGA-II。我们分析了基因型多样性方面的SAPIEN Z适应度景观，并使用所获得的见解来适应SAPIEN Z的启发式。这些调整导致SAPIENZ^div旨在在搜索期间保留测试套件的多样性。为了评估SAPIEN ^div，我们在76个开源应用程序上与SAPIEN进行了头对头比较。
英文摘要：
In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trial-and-error basis, which may lead to better results for a specific problem. To obtain better results while avoiding trial-and-error experiments, a fitness landscape analysis is helpful in understanding the search problem, and making an informed decision about the heuristics. In this paper, we investigate the search problem of test suite generation for mobile applications (apps) using SAPIENZ whose heuristic is a default NSGA-II. We analyze the fitness landscape of SAPIENZ with respect to genotypic diversity and use the gained insights to adapt the heuristic of SAPIENZ. These adaptations result in SAPIENZ^div that aims for preserving the diversity of test suites during the search. To evaluate SAPIENZ^div, we perform a head-to-head comparison with SAPIENZ on 76 open-source apps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
