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%.

翻译：集群技术作为一种在多过失假设情景中平行调试的有希望的战略,吸引了人们的极大关注。 这种超常方法(即失败指数化或断层隔离)使开发者能够通过将失败的测试案例分成几个脱节小组,同时执行多重调试任务。 当使用对模型失败的分级代表来模拟更好的集群时,有几个因素影响集群的有效性,包括风险评估公式(REF)、缺陷数目(NOF)、缺陷类型(FT)和成功测试案例数(NSP1F),在本文件中,我们介绍了关于这四个因素如何影响集群有效性的第一次全面经验研究。我们对1 060个错误版本的228个模拟缺陷和141个实际缺陷进行了广泛的控制实验,结果显示:(1) GP19在所有可再生能源框架中具有高度竞争力,(2) 集群效力随着NOF的增加而下降,(3) 当一个方案只包含上游缺陷时,提高的集群效力比较容易实现,(4) 当NSP1F的规模降至20%时,集群的有效性仍然存在。