An effectiveness measure for software clustering algorithms

Zhihua Wen, Vassilios Tzerpos
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引用次数: 155

Abstract

Selecting an appropriate software clustering algorithm that can help the process of understanding a large software system is a challenging issue. The effectiveness of a particular algorithm may be influenced by a number of different factors, such as the types of decompositions produced, or the way clusters are named. In this paper, we introduce an effectiveness measure for software clustering algorithms based on Mojo distance, and describe an algorithm that calculates its value. We also present experiments that demonstrate its improved performance over previous measures, and show how it can be used to assess the effectiveness of software clustering algorithms.
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软件聚类算法的有效性度量
选择合适的软件聚类算法来帮助理解大型软件系统的过程是一个具有挑战性的问题。特定算法的有效性可能受到许多不同因素的影响,例如产生的分解类型或集群的命名方式。本文介绍了一种基于Mojo距离的软件聚类算法的有效性度量,并描述了一种计算其值的算法。我们还提出了实验,证明了它比以前的测量方法的性能有所提高,并展示了如何使用它来评估软件聚类算法的有效性。
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