{"title":"A criterion for filtering code clone related bugs","authors":"Yasuhiro Hayase, Yii Yong Lee, Katsuro Inoue","doi":"10.1145/1390817.1390828","DOIUrl":null,"url":null,"abstract":"Software reviews are time-consuming task especially for large software systems. To reduce the efforts required, Li et al. developed CP-Miner, a code clone detection tool that detects identifier naming inconsistencies between code clones as bug candidates. However, reviewers using CP-Miner still have to assess many inconsistencies, since the tool also reports many false-positive candidates. To reduce the false-positive candidates, we propose a criterion for filtering the candidates. In our experiments, filtering with the proposed criterion removed 30% of the false-positive candidates and no true-positive candidates. This result shows that the proposed criterion helps the review task by effectively reducing the number of bug candidates.","PeriodicalId":193694,"journal":{"name":"DEFECTS '08","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DEFECTS '08","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1390817.1390828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Software reviews are time-consuming task especially for large software systems. To reduce the efforts required, Li et al. developed CP-Miner, a code clone detection tool that detects identifier naming inconsistencies between code clones as bug candidates. However, reviewers using CP-Miner still have to assess many inconsistencies, since the tool also reports many false-positive candidates. To reduce the false-positive candidates, we propose a criterion for filtering the candidates. In our experiments, filtering with the proposed criterion removed 30% of the false-positive candidates and no true-positive candidates. This result shows that the proposed criterion helps the review task by effectively reducing the number of bug candidates.