软件多故障聚类集成技术

Mingxing Zhang, Shihai Wang, Wentao Wu, Weiguo Qiu, Wandong Xie
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引用次数: 0

摘要

在软件多故障场景中,通过聚类对不同故障源的失败测试用例进行分类,然后利用基于程序谱的故障定位技术对每个聚类中的程序语句进行可疑排序。软件多故障定位技术通过聚类失败测试用例的代码覆盖信息来解耦失败测试用例。目前,针对软件失败测试用例的聚类策略有很多。然而,由于失败测试用例执行信息具有高维、结构复杂、形状多变等特点,单一聚类算法难以准确识别聚类结构,从而无法准确解耦。本文通过选择多种聚类算法和不同参数获得多个聚类成员,根据聚类成员得到相似度矩阵,然后采用分层聚类获得最终聚类结果,实现不同聚类算法之间的整合,使算法能够识别复杂的聚类结构,进而提高故障定位效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Software Multi-Fault Clustering Ensemble Technology
In the software multi-fault scenario, the failed test cases of different fault sources are classified by clustering, and then the program statements in each cluster are ranked with suspicion by the fault location technology based on program spectrum. Software multi-fault location technology decouples failed test cases by clustering their code coverage information. At present, there are many clustering strategies for software failed test cases. However, due to the characteristics of high dimension, complex structure and variable shape of the execution information of failed test cases, it is difficult for a single clustering algorithm to accurately identify the cluster structure, so that it cannot be decoupled accurately. In this paper, a variety of clustering algorithms and different parameters are selected to obtain multiple cluster members, according to the clustering members to get the similarity matrix, and then hierarchical clustering is used to obtain the final clustering results, to achieve the integration between different clustering algorithms, so that the algorithm can identify the complex cluster structure, and then improve the efficiency of fault location.
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