{"title":"为软件变更指南挖掘交互历史","authors":"Takashi Kobayashi, Nozomu Kato, K. Agusa","doi":"10.1109/RSSE.2012.6233415","DOIUrl":null,"url":null,"abstract":"This paper presents a prediction model for change propagation based on the developers 'interaction history. Since artifacts have internal and external dependencies, a change will cause some changes on related artifacts. In order to guide change operations in software development, our proposed method generates a change guide graph by mining developers' interaction histories which consist of write and read accesses to artifacts. Using a change guide graph, we can guide change using the context of previous changes. To evaluate proposed change guide method, we perform a case study with an open-source software. We show that the context information is effective for file level and method level change predictions.","PeriodicalId":193223,"journal":{"name":"2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Interaction histories mining for software change guide\",\"authors\":\"Takashi Kobayashi, Nozomu Kato, K. Agusa\",\"doi\":\"10.1109/RSSE.2012.6233415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a prediction model for change propagation based on the developers 'interaction history. Since artifacts have internal and external dependencies, a change will cause some changes on related artifacts. In order to guide change operations in software development, our proposed method generates a change guide graph by mining developers' interaction histories which consist of write and read accesses to artifacts. Using a change guide graph, we can guide change using the context of previous changes. To evaluate proposed change guide method, we perform a case study with an open-source software. We show that the context information is effective for file level and method level change predictions.\",\"PeriodicalId\":193223,\"journal\":{\"name\":\"2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE)\",\"volume\":\"243 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RSSE.2012.6233415\",\"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 Third International Workshop on Recommendation Systems for Software Engineering (RSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RSSE.2012.6233415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interaction histories mining for software change guide
This paper presents a prediction model for change propagation based on the developers 'interaction history. Since artifacts have internal and external dependencies, a change will cause some changes on related artifacts. In order to guide change operations in software development, our proposed method generates a change guide graph by mining developers' interaction histories which consist of write and read accesses to artifacts. Using a change guide graph, we can guide change using the context of previous changes. To evaluate proposed change guide method, we perform a case study with an open-source software. We show that the context information is effective for file level and method level change predictions.