使用LittleDarwin的动态突变体包容分析
Dynamic Mutant Subsumption Analysis using LittleDarwin
摘要：
在软件测试领域的许多学术研究都依赖于变异测试作为他们的比较标准。然而，最近的研究表明，冗余突变体对其结果的准确性有显著影响。这个问题的一个解决方案是使用突变体包容来检测冗余突变体。因此，为了促进该领域的研究，需要能够检测冗余突变体的突变测试工具。在本文中，我们描述了我们如何改进我们的工具，LittleDarwin，以满足这一要求。
英文摘要：
Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement.
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。
