{"title":"一种早期检测显性和隐性横切关注点的聚类技术","authors":"C. Duan, J. Cleland-Huang","doi":"10.1109/EARLYASPECTS.2007.1","DOIUrl":null,"url":null,"abstract":"This paper describes an approach for automating the detection of early aspects. Based on hierarchical clustering and an underlying probabilistic algorithm, the technique generates initial requirements clusters representing relatively homogenous feature sets, use cases and potential cross-cutting concerns. A second clustering phase is then applied in which dominant terms are identified and removed from each of the initial clusters, allowing new clusters to form around less dominant terms. This second phase enables previously inter-tangled aspects to be detected. Three metrics are introduced to differentiate potential cross-cutting concerns from other types of clusters. The approach is illustrated through an example based on the Public Health Watcher case study.","PeriodicalId":153496,"journal":{"name":"Early Aspects at ICSE: Workshops in Aspect-Oriented Requirements Engineering and Architecture Design (EARLYASPECTS'07)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"A Clustering Technique for Early Detection of Dominant and Recessive Cross-Cutting Concerns\",\"authors\":\"C. Duan, J. Cleland-Huang\",\"doi\":\"10.1109/EARLYASPECTS.2007.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an approach for automating the detection of early aspects. Based on hierarchical clustering and an underlying probabilistic algorithm, the technique generates initial requirements clusters representing relatively homogenous feature sets, use cases and potential cross-cutting concerns. A second clustering phase is then applied in which dominant terms are identified and removed from each of the initial clusters, allowing new clusters to form around less dominant terms. This second phase enables previously inter-tangled aspects to be detected. Three metrics are introduced to differentiate potential cross-cutting concerns from other types of clusters. The approach is illustrated through an example based on the Public Health Watcher case study.\",\"PeriodicalId\":153496,\"journal\":{\"name\":\"Early Aspects at ICSE: Workshops in Aspect-Oriented Requirements Engineering and Architecture Design (EARLYASPECTS'07)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Early Aspects at ICSE: Workshops in Aspect-Oriented Requirements Engineering and Architecture Design (EARLYASPECTS'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EARLYASPECTS.2007.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Early Aspects at ICSE: Workshops in Aspect-Oriented Requirements Engineering and Architecture Design (EARLYASPECTS'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EARLYASPECTS.2007.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Clustering Technique for Early Detection of Dominant and Recessive Cross-Cutting Concerns
This paper describes an approach for automating the detection of early aspects. Based on hierarchical clustering and an underlying probabilistic algorithm, the technique generates initial requirements clusters representing relatively homogenous feature sets, use cases and potential cross-cutting concerns. A second clustering phase is then applied in which dominant terms are identified and removed from each of the initial clusters, allowing new clusters to form around less dominant terms. This second phase enables previously inter-tangled aspects to be detected. Three metrics are introduced to differentiate potential cross-cutting concerns from other types of clusters. The approach is illustrated through an example based on the Public Health Watcher case study.