{"title":"使用覆盖率分析改进导致失败的变更识别","authors":"Kai Yu","doi":"10.1109/ICSE.2012.6227229","DOIUrl":null,"url":null,"abstract":"Delta debugging has been proposed for failure-inducing changes identification. Despite promising results, there are two practical factors that thwart the application of delta debugging: large number of tests and misleading false positives. To address the issues, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regressions in GNU software demonstrate both the speed gain and effectiveness improvements.","PeriodicalId":420187,"journal":{"name":"2012 34th International Conference on Software Engineering (ICSE)","volume":"51 7","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Improving failure-inducing changes identification using coverage analysis\",\"authors\":\"Kai Yu\",\"doi\":\"10.1109/ICSE.2012.6227229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Delta debugging has been proposed for failure-inducing changes identification. Despite promising results, there are two practical factors that thwart the application of delta debugging: large number of tests and misleading false positives. To address the issues, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regressions in GNU software demonstrate both the speed gain and effectiveness improvements.\",\"PeriodicalId\":420187,\"journal\":{\"name\":\"2012 34th International Conference on Software Engineering (ICSE)\",\"volume\":\"51 7\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 34th International Conference on Software Engineering (ICSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSE.2012.6227229\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 34th International Conference on Software Engineering (ICSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE.2012.6227229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving failure-inducing changes identification using coverage analysis
Delta debugging has been proposed for failure-inducing changes identification. Despite promising results, there are two practical factors that thwart the application of delta debugging: large number of tests and misleading false positives. To address the issues, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regressions in GNU software demonstrate both the speed gain and effectiveness improvements.