Prioritizing mutants to guide mutation testing
Prioritizing mutants to guide mutation testing
复制标题
确定突变体的优先顺序以指导突变测试
DOI：
10.1145/3510003.3510187
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Just, René
中科院分区：
文献类型：
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作者：
Kaufman, Samuel J.;Featherman, Ryan;Alvin, Justin;Kurtz, Bob;Ammann, Paul;Just, René
突变测试提供了具体的测试目标（突变体）和严格的测试有效性标准，但由于大量的突变体，其中许多既没有用也没有可操作性，所以它是昂贵的。以前的工作集中在选择有代表性的和足够的突变子集，测量是否是突变足够的子集的测试集是同样足够的整个集合。然而，没有已知的突变测试的工业应用使用甚至计算突变充分性，而是专注于迭代地呈现非常少的突变体作为开发人员编写测试的具体测试目标。本文（1）阐明了突变分析与突变测试之间的重要区别，其中测量突变充分性是感兴趣的，（2）引入了一种新的突变体有用性度量方法，称为测试完整性推进概率（TCAP）;（3）介绍了一种方法，通过逐步选择突变体的基础上预测的TCAP的突变体的优先级;（4）提出了模拟显示，基于TCAP的突变体的优先级比优先级与以前的国家的最先进的测试的完整性更快。
Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but it is expensive due to vast numbers of mutants, many of which are neither useful nor actionable. Prior work has focused on selecting representative and sufficient mutant subsets, measuring whether a test set that is mutation-adequate for the subset is equally adequate for the entire set. However, no known industrial application of mutation testing uses or even computes mutation adequacy, instead focusing on iteratively presenting very few mutants as concrete test goals for developers to write tests.This paper (1) articulates important differences between mutation analysis, where measuring mutation adequacy is of interest, and mutation testing, where mutants are of interest insofar as they serve as concrete test goals to elict effective tests; (2) introduces a new measure of mutant usefulness, called test completeness advancement probability (TCAP); (3) introduces an approach to prioritizing mutants by incrementally selecting mutants based on their predicted TCAP; and (4) presents simulations showing that TCAP-based prioritization of mutants advances test completeness more rapidly than prioritization with the previous state-of-the-art.