{"title":"基于格点特征定位的可扩展算法","authors":"Wei Zhao, Lu Zhang, Dan Hao, Hong Mei, Jiasu Sun","doi":"10.1109/ICSM.2004.1357870","DOIUrl":null,"url":null,"abstract":"Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.","PeriodicalId":348668,"journal":{"name":"20th IEEE International Conference on Software Maintenance, 2004. Proceedings.","volume":"171 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Alternative scalable algorithms for lattice-based feature location\",\"authors\":\"Wei Zhao, Lu Zhang, Dan Hao, Hong Mei, Jiasu Sun\",\"doi\":\"10.1109/ICSM.2004.1357870\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.\",\"PeriodicalId\":348668,\"journal\":{\"name\":\"20th IEEE International Conference on Software Maintenance, 2004. Proceedings.\",\"volume\":\"171 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"20th IEEE International Conference on Software Maintenance, 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSM.2004.1357870\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"20th IEEE International Conference on Software Maintenance, 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSM.2004.1357870","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Alternative scalable algorithms for lattice-based feature location
Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.