基于故障森林的组合测试故障定位研究

Locating failure-inducing combinations based on fault forest

组合测试作为一种对参数组合空间抽样的系统方法,适用于待测系统中存在由特定参数组合所引发的软件失效.依据组合测试结果,定位出最小失效诱因模式(minimal failure-causing schema,MFS)有助于程序员进行故障源检测与修复.然而,组合测试可能存在mask effect,使得测试用例中即使包含MFS也未必一定触发软件失效.因此,在存在mask effect的系统中精确定位最小失效诱因模式尤为困难.为此提出了一种基于故障森林的组合测试故障定位方法.给定一个t-路组合测试集(t≥2)及其附加测试集,该方法首先学习由多个深度为t的基本故障分类树所组成的故障森林,然后从故障森林中提取基本故障组合模式,最后将可疑MFS进行排序,并提交给程序员进行进一步诊断.仿真实验结果表明,该方法能有效定位系统中存在的组合故障模式.特别地,对于存在mask effect的待测系统,故障定位结果健壮.

Combinatorial testing, a method for sampling parameter combination in the parameter space of a system, is suitable for systems in which failure is caused by a specific parameter combination. Based on the results of combination testing, locating the minimal failure causing schema （MFS） can help programmers to localize faults and repair them. However, combination testing might be affected by the mask effect, and even test cases containing MFSs may not necessarily trigger a failure. Therefore, it is extremely difficult to pinpoint MFSs in systems affected by the mask effect. A fault location method based on fault forest is proposed. Given a set of t-way combination test （ t ~ 2） and their augment test set, this method first learns some basic fault trees which generate a fault forest, then extracts the basic suspicious MFS from the forest, and finally orders those suspicious MFSs by their suspiciousness which will help programmers perform further diagnosis. The simulation results show that the presented method can effectively identify MFS. In particular, for the systems affected by mask effect, result is robust.