一种早期检测显性和隐性横切关注点的聚类技术

C. Duan, J. Cleland-Huang
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引用次数: 22

摘要

本文描述了一种自动化检测早期缺陷的方法。基于分层聚类和底层概率算法,该技术生成初始需求聚类,表示相对同质的特性集、用例和潜在的横切关注点。然后应用第二个聚类阶段,识别并从每个初始聚类中删除主导术语,允许围绕不太主导的术语形成新的聚类。第二阶段可以检测到先前相互纠缠的方面。引入了三个度量来区分潜在的横切关注点和其他类型的集群。通过基于公共卫生观察员案例研究的一个例子说明了这种方法。
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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.
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Revisiting a Formal Framework for Modeling Aspects in the Design Phase Towards the Architectural Definition of the Health Watcher System with AO-ADL A Traceability Method for Crosscutting Concerns with Transformation Rules Modeling and Evolving Crosscutting Concerns in ADORA On the Contributions of an End-to-End AOSD Testbed
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