Context

The reuse of software design has been an important issue of software reuse. UML class diagrams are widely applied in software design and has become DE factor standard. As a result, the reuse of UML class diagrams has received more attention. With the increasing number of class diagrams stored in reuse repository, their retrieval becomes a time-consuming job. The clustering can narrow down retrieval range and improve the retrieval efficiency. But few efforts have been done in clustering UML class diagrams. This paper tries to propose a clustering approach for UML class diagrams.

Objective

This paper proposes a two-level clustering of UML class diagrams, namely, semantic clustering and structural clustering. The UML class diagrams stored in reuse repository are clustered into a few domains based on semantics in the first level and a few categories based on structure in the second level.

Method

We propose a clustering algorithm named CUFS, in which the idea of partitioning and hierarchical clustering is combined and feature similarity is proposed for the similarity measure between two clusters in order to merge clusters. A better feature representation of a cluster, namely, feature class diagram, is proposed in this paper. In order to form each sub-cluster, the semantic and structural similarities between UML class diagrams are defined, respectively.

Results

A series of experimental results show that, the proposed feature similarity measure not only speeds up the clustering process, but also expresses the closeness degree between clusters for merging clusters. The proposed algorithm shows a good clustering quality and efficiency under the condition of different size and distribution of UML class diagrams.

Conclusion

It is concluded that the proposed two-level clustering method considers both semantics and structure contained in a class diagram, which can flexibly adapt to different clustering requirements. Also, the proposed clustering algorithm performs better than other related algorithms, regardless of in semantic, structural and hybrid clustering.

中文翻译：

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基于语义和结构的UML类图的两级聚类

语境

软件设计的重用一直是软件重用的重要问题。UML类图已广泛应用于软件设计中，并已成为DE要素标准。结果，UML类图的重用得到了更多的关注。随着存储在重用存储库中的类图数量的增加，对其进行检索成为一项耗时的工作。聚类可以缩小检索范围，提高检索效率。但是，在聚类UML类图方面所做的工作很少。本文试图为UML类图提出一种聚类方法。

目的

本文提出了UML类图的两级聚类，即语义聚类和结构聚类。存储在重用存储库中的UML类图在第一层基于语义被分为几个域，在第二层基于结构被分为几个类别。

方法

我们提出了一种称为CUFS的聚类算法，其中结合了分区和分层聚类的思想，并针对两个聚类之间的相似性度量提出了特征相似性以合并聚类。本文提出了一种更好的聚类特征表示方法，即特征类图。为了形成每个子集群，分别定义了UML类图之间的语义和结构相似性。

结果

一系列实验结果表明，提出的特征相似度度量不仅可以加速聚类过程，而且可以表达聚类之间的紧密度。该算法在UML类图的大小和分布不同的情况下表现出良好的聚类质量和效率。

结论

结论是，提出的两级聚类方法同时考虑了类图中包含的语义和结构，可以灵活地适应不同的聚类需求。而且，无论在语义，结构还是混合聚类方面，所提出的聚类算法的性能均优于其他相关算法。