{"title":"基于聚类加权的Bug报告重复检测改进判别模型","authors":"Meng-Jie Lin, Cheng-Zen Yang","doi":"10.1109/COMPSAC.2014.18","DOIUrl":null,"url":null,"abstract":"Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection scheme using cluster weighting to enhance the detection performance of a previous SVM-based method. We have conducted empirical studies with three open source software projects, Apache, ArgoUML, and SVN. Compared with the original SVM-based method, the proposed SVM-TC scheme can achieve 2.83-16.32% improvements of the top-5 recall rates in three projects.","PeriodicalId":106871,"journal":{"name":"2014 IEEE 38th Annual Computer Software and Applications Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An Improved Discriminative Model for Duplication Detection on Bug Reports with Cluster Weighting\",\"authors\":\"Meng-Jie Lin, Cheng-Zen Yang\",\"doi\":\"10.1109/COMPSAC.2014.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection scheme using cluster weighting to enhance the detection performance of a previous SVM-based method. We have conducted empirical studies with three open source software projects, Apache, ArgoUML, and SVN. Compared with the original SVM-based method, the proposed SVM-TC scheme can achieve 2.83-16.32% improvements of the top-5 recall rates in three projects.\",\"PeriodicalId\":106871,\"journal\":{\"name\":\"2014 IEEE 38th Annual Computer Software and Applications Conference\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 38th Annual Computer Software and Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC.2014.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 38th Annual Computer Software and Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC.2014.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Discriminative Model for Duplication Detection on Bug Reports with Cluster Weighting
Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection scheme using cluster weighting to enhance the detection performance of a previous SVM-based method. We have conducted empirical studies with three open source software projects, Apache, ArgoUML, and SVN. Compared with the original SVM-based method, the proposed SVM-TC scheme can achieve 2.83-16.32% improvements of the top-5 recall rates in three projects.
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