{"title":"软件多故障聚类集成技术","authors":"Mingxing Zhang, Shihai Wang, Wentao Wu, Weiguo Qiu, Wandong Xie","doi":"10.1109/QRS-C57518.2022.00059","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Software Multi-Fault Clustering Ensemble Technology\",\"authors\":\"Mingxing Zhang, Shihai Wang, Wentao Wu, Weiguo Qiu, Wandong Xie\",\"doi\":\"10.1109/QRS-C57518.2022.00059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":183728,\"journal\":{\"name\":\"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QRS-C57518.2022.00059\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QRS-C57518.2022.00059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.