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MSeer—An Advanced Technique for Locating Multiple Bugs in Parallel

机译:MSeer-并行定位多个错误的高级技术

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In practice, a program may contain multiple bugs. The simultaneous presence of these bugs may deteriorate the effectiveness of existing fault-localization techniques to locate program bugs. While it is acceptable to use all failed and successful tests to identify suspicious code for programs with exactly one bug, it is not appropriate to use the same approach for programs with multiple bugs because the due-to relationship between failed tests and underlying bugs cannot be easily identified. One solution is to generate fault-focused clusters by grouping failed tests caused by the same bug into the same clusters. We propose MSeer-an advanced fault localization technique for locating multiple bugs in parallel. Our major contributions include the use of (1) a revised Kendall tau distance to measure the distance between two failed tests, (2) an innovative approach to simultaneously estimate the number of clusters and assign initial medoids to these clusters, and (3) an improved K-medoids clustering algorithm to better identify the due-to relationship between failed tests and their corresponding bugs. Case studies on 840 multiple-bug versions of seven programs suggest that MSeer performs better in terms of effectiveness and efficiency than two other techniques for locating multiple bugs in parallel.
机译:实际上,一个程序可能包含多个错误。这些错误的同时存在可能会降低现有的故障定位技术定位程序错误的效率。虽然可以使用所有失败和成功的测试来为仅存在一个错误的程序识别可疑代码,但是对于具有多个错误的程序则不宜使用相同的方法,因为失败的测试与基本错误之间的归因关系无法确定。容易识别。一种解决方案是通过将由同一错误引起的失败测试分组到同一集群中来生成以故障为中心的集群。我们提出了MSeer,这是一种用于并行定位多个错误的高级故障定位技术。我们的主要贡献包括:(1)修改后的Kendall tau距离来测量两次失败测试之间的距离;(2)一种创新的方法,可以同时估算星团的数量并为这些星团分配初始药物,以及(3)改进的K-medoids聚类算法,可以更好地识别失败测试与相应错误之间的归因关系。对七个程序的840个多重错误版本的案例研究表明,MSeer在有效性和效率方面比并行定位多个错误的其他两种技术表现更好。

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