{"title":"分布式支持系统开发历史的聚合","authors":"Hiroki Kawai, H. Uwano, Soichiro Tani","doi":"10.1109/SNPD.2012.63","DOIUrl":null,"url":null,"abstract":"This paper proposes a method to recommend the relevant information of the document which recorded in the development support systems such as BTS and VCS. We improve a system which we implemented in previous work with the method proposed in this paper. Our method get a document from the support systems, extract the word, then calculate the feature vector based on the TF-IDF value of each word. In the experiment, we apply the proposal method to the dataset from an open source software projects, and evaluate the recommendation accuracy between the six clustering algorithm. The result of the experiment shows that the proposed method improves the recommendation accuracy compared with the previous work.","PeriodicalId":387936,"journal":{"name":"2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aggregation of Development History from Distributed Support Systems\",\"authors\":\"Hiroki Kawai, H. Uwano, Soichiro Tani\",\"doi\":\"10.1109/SNPD.2012.63\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method to recommend the relevant information of the document which recorded in the development support systems such as BTS and VCS. We improve a system which we implemented in previous work with the method proposed in this paper. Our method get a document from the support systems, extract the word, then calculate the feature vector based on the TF-IDF value of each word. In the experiment, we apply the proposal method to the dataset from an open source software projects, and evaluate the recommendation accuracy between the six clustering algorithm. The result of the experiment shows that the proposed method improves the recommendation accuracy compared with the previous work.\",\"PeriodicalId\":387936,\"journal\":{\"name\":\"2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SNPD.2012.63\",\"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 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2012.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aggregation of Development History from Distributed Support Systems
This paper proposes a method to recommend the relevant information of the document which recorded in the development support systems such as BTS and VCS. We improve a system which we implemented in previous work with the method proposed in this paper. Our method get a document from the support systems, extract the word, then calculate the feature vector based on the TF-IDF value of each word. In the experiment, we apply the proposal method to the dataset from an open source software projects, and evaluate the recommendation accuracy between the six clustering algorithm. The result of the experiment shows that the proposed method improves the recommendation accuracy compared with the previous work.