{"title":"使用多变量时间序列和关联规则检测逻辑变化耦合:一个实证研究","authors":"G. Canfora, M. Ceccarelli, L. Cerulo, M. D. Penta","doi":"10.1109/ICSM.2010.5609732","DOIUrl":null,"url":null,"abstract":"In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets.","PeriodicalId":101801,"journal":{"name":"2010 IEEE International Conference on Software Maintenance","volume":"82 19","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"Using multivariate time series and association rules to detect logical change coupling: An empirical study\",\"authors\":\"G. Canfora, M. Ceccarelli, L. Cerulo, M. D. Penta\",\"doi\":\"10.1109/ICSM.2010.5609732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets.\",\"PeriodicalId\":101801,\"journal\":{\"name\":\"2010 IEEE International Conference on Software Maintenance\",\"volume\":\"82 19\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Software Maintenance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSM.2010.5609732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Software Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSM.2010.5609732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using multivariate time series and association rules to detect logical change coupling: An empirical study
In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets.