利用因果效应对高阶突变体进行有效抽样
Effectively Sampling Higher Order Mutants Using Causal Effect
摘要：
高阶变异（HOM）已经被提出来避免等价的突变体，提高变异测试的可扩展性，但产生有用的HOM本身仍然是一个昂贵的搜索问题。我们提出了一种新的方法来产生强包容高阶突变体（SSHOM）使用最近推出的因果程序依赖分析（CPDA）。CPDA本身是基于程序突变的，并且提供了对一个程序元素的值的改变将引起另一个程序元素的值的改变的频率的定量估计。我们的SSHOM生成方法使用基于CPDA分析的启发式方法选择程序元素对，对所选对执行一阶变异，并通过组合两个FOM生成HOM。
英文摘要：
Higher Order Mutation (HOM) has been proposed to avoid equivalent mutants and improve the scalability of mutation testing, but generating useful HOMs remain an expensive search problem on its own. We propose a new approach to generate Strongly Subsuming Higher Order Mutants (SSHOM) using a recently introduced Causal Program Dependence Analysis (CPDA). CPDA itself is based on program mutation, and provides quantitative estimation of how often a change of the value of a program element will cause a value change of another program element. Our SSHOM generation approach chooses pairs of program elements using heuristics based on CPDA analysis, performs First Order Mutation to the chosen pairs, and generates an HOM by combining two FOMs.
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。
