A comprehensive empirical investigation on failure clustering in parallel debugging
A comprehensive empirical investigation on failure clustering in parallel debugging
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DOI：
10.1016/j.jss.2022.111452
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xi Wu
中科院分区：
文献类型：
--
作者：
Yi Song;Xiaoyuan Xie;Quanming Liu;Xihao Zhang;Xi Wu
The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When using statement ranking representation to model failures for better clustering, several factors influence clustering effectiveness, including the risk evaluation formula (REF), the number of faults (NOF), the fault type (FT), and the number of successful test cases paired with one individual failed test case (NSP1F). In this paper, we present the first comprehensive empirical study of how these four factors influence clustering effectiveness. We conduct extensive controlled experiments on 1060 faulty versions of 228 simulated faults and 141 real faults, and the results reveal that: (1) GP19 is highly competitive across all REFs, (2) clustering effectiveness decreases as NOF increases, (3) higher clustering effectiveness is easier to achieve when a program contains only predicate faults, and (4) clustering effectiveness remains when the scale of NSP1F is reduced to 20%. • The first empirical study regarding the clustering in parallel debugging. • A promising SBFL formula for failure representation in parallel debugging is found. • Clustering effectiveness decreases as the number of faults increases. • Better clustering is easier to obtain when a program has only predicate faults. • Clustering effectiveness remains when successful test cases are reduced to 20%.
