使用专用性和模块化的基于依赖性聚集的软件聚类
Feature-Gathering Dependency-Based Software Clustering Using Dedication and Modularity
摘要：
软件集群是理解软件系统的重要技术之一。然而，迄今为止所提出的技术需要人的交互来细化聚类结果。提出了一种基于依赖关系的软件聚类算法SArF。SARF有两个特点。首先，SArF消除了需要人工交互的无所不在模块移除步骤的需要。其次，SArF的目标是将相关的软件特性或功能收集到一个集群中。为了实现这些目标，我们定义了Dedication得分来推断依赖关系的重要性，并利用模块化最大化来聚类加权有向图。两个案例研究和广泛的比较评估，使用开源和工业系统表明，SARF可以成功地分解系统拟合的权威分解从功能的角度来看，没有任何定制的设置，SARF优于现有的依赖性为基础的软件聚类研究。此外，案例研究表明，存在着可测量的权威性限度，SARF几乎达到了极限。
英文摘要：
Software clustering is one of the important techniques to comprehend software systems. However, presented techniques to date require human interactions to refine clustering results. In this paper, we proposed a novel dependency-based software clustering algorithm, SArF. SArF has two characteristics. First, SArF eliminates the need of the omnipresent-module-removing step which requires human interactions. Second, the objective of SArF is to gather relevant software features or functionalities into a cluster. To achieve them, we defined the Dedication score to infer the importance of dependencies and utilized Modularity Maximization to cluster weighted directed graphs. Two case studies and extensive comparative evaluations using open source and industrial systems show that SArF could successfully decompose the systems fitting to the authoritative decompositions from a feature viewpoint without any tailored setups and that SArF was superior to existing dependency-based software clustering studies. Besides, the case studies show that there exist measurable authoritativeness limits and that SArF nearly reached the limits.
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