论文标题
对并行调试中故障聚类的全面实证研究
A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging
论文作者
论文摘要
这种聚类技术吸引了很多关注，作为在多道违反场景中并行调试的一种有希望的策略，这种启发式方法（即失败索引或故障隔离）使开发人员能够通过将失败的测试案例分为几个分离组来同时执行多个调试任务。当使用语句排名表示以进行更好的聚类模型时，几个因素会影响聚类的有效性，包括风险评估公式（REF），故障数（NOF），故障类型（FT）以及成功的测试用例与一个单独的失败测试用例（NSP1F）配对的数量。在本文中，我们介绍了这四个因素如何影响聚类有效性的首次全面实证研究。我们对228个模拟故障和141个实际故障的1060个故障版本进行了广泛的控制实验，结果表明：1）GP19在所有参考资料中竞争高度竞争，2）聚类有效性随着NOF的增加而降低，3）较高的群集效率在程序中更容易在proginals predication nefiptions predications predications predications new n clistive nsss中，并且在cliste的效率上均为cleSSSSSSSSSSSSSSSSSSSSS，则可以构成效率为4）。
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%.
