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An Empirical Study on the Usage of Fault Localization in Automated Program Repair

机译：自动化程序维修中故障定位使用的实证研究

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摘要

Spectrum-based fault localization (SFL), the technique producing a rank list of statements in descending order of their suspiciousness values, is nowadays widely used in current automated program repair tools. There are two different algorithms for these tools to choose statements selected for modification to produce candidate patches from the list: one is the rank-first algorithm based on suspiciousness rankings of statements, the other is the suspiciousness-first algorithm based on suspiciousness value of statements. However, to our knowledge there is no research work implementing the two algorithms in the same repair tool or comparing their effectiveness. In this paper, we conduct an empirical research based on the automated repair tool Nopol with the benchmark set of Defects4J to compare these two algorithms. Preliminary results suggest that the suspiciousness-first algorithm is not equivalent to the rank-first algorithm and behaves better in parallel repair and patch diversity.
机译：基于频谱的故障定位（SFL），在当前自动化程序维修工具中广泛应用于其可疑价值的降序，从而播放频谱的故障定位（SFL）。这些工具有两种不同的算法选择选择的语句，以便从列表中生成候选修补程序的候选补丁：一个是基于陈述的可疑排名的秩第一算法，另一个是基于陈述的可疑值的可疑 - 第一算法。然而，对于我们的知识，没有研究工作在同一修复工具中实施两种算法或比较其有效性。在本文中，我们基于自动修复工具Nopol的实证研究与缺陷4J的基准组合，以比较这两个算法。初步结果表明，可疑 - 第一算法不等于秩第一算法，并在并行修复和补丁分集中表现更好。

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