Multiple layer clustering of large software systems

Bill Andreopoulos, Aijun An, Vassilios Tzerpos, Xiaogang Wang
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引用次数: 25

Abstract

Software clustering algorithms presented in the literature rarely incorporate in the clustering process dynamic information, such as the number of function invocations during runtime. Moreover, the structure of a software system is often multi-layered, while existing clustering algorithms often create flat system decompositions. This paper presents a software clustering algorithm called MULICsoft that incorporates in the clustering process both static and dynamic information. MULICsoft produces layered clusters with the core elements of each cluster assigned to the top layer. We present experimental results of applying MULICsoft to a large open-source system. Comparison with existing software clustering algorithms indicates that MULICsoft is able to produce decompositions that are close to those created by system experts.
大型软件系统的多层聚类
文献中提出的软件聚类算法很少在聚类过程中包含动态信息,如运行时函数调用的次数。此外,软件系统的结构往往是多层的,而现有的聚类算法往往造成扁平的系统分解。本文提出了一种将静态和动态信息结合在聚类过程中的软件聚类算法MULICsoft。MULICsoft生成分层集群,每个集群的核心元素分配到顶层。我们给出了将MULICsoft应用于大型开源系统的实验结果。与现有软件聚类算法的比较表明,MULICsoft能够产生接近系统专家创建的分解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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