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Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach

机译：使用自适应预防性树状图切割方法的高效软件聚类技术

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摘要

Context: Software clustering is a key technique that is used in reverse engineering to recover a high-level abstraction of the software in the case of limited resources. Very limited research has explicitly discussed the problem of finding the optimum set of clusters in the design and how to penalize for the formation of singleton clusters during clustering. Objective: This paper attempts to enhance the existing agglomerative clustering algorithms by introducing a complementary mechanism. To solve the architecture recovery problem, the proposed approach focuses on minimizing redundant effort and penalizing for the formation of singleton clusters during clustering while maintaining the integrity of the results. Method: An automated solution for cutting a dendrogram that is based on least-squares regression is presented in order to find the best cut level. A dendrogram is a tree diagram that shows the taxonomic relationships of clusters of software entities. Moreover, a factor to penalize clusters that will form singletons is introduced in this paper. Simulations were performed on two open-source projects. The proposed approach was compared against the exhaustive and highest gap dendrogram cutting methods, as well as two well-known cluster validity indices, namely, Dunn's index and the Davies-Bouldin index. Results: When comparing our clustering results against the original package diagram, our approach achieved an average accuracy rate of 90.07% from two simulations after the utility classes were removed. The utility classes in the source code affect the accuracy of the software clustering, owing to its omnipresent behavior. The proposed approach also successfully penalized the formation of singleton clusters during clustering. Conclusion: The evaluation indicates that the proposed approach can enhance the quality of the clustering results by guiding software maintainers through the cutting point selection process. The proposed approach can be used as a complementary mechanism to improve the effectiveness of existing clustering algorithms.
机译：上下文：软件集群是一项关键技术，在资源有限的情况下，可用于逆向工程中以恢复软件的高级抽象。非常有限的研究明确讨论了在设计中寻找最佳聚类集的问题，以及如何在聚类过程中惩罚单例聚类的形成。目的：本文试图通过引入一种补充机制来增强现有的聚集聚类算法。为了解决架构恢复问题，所提出的方法着重于最小化冗余工作并在聚类期间对单例聚类的形成进行惩罚，同时保持结果的完整性。方法：提出了一种基于最小二乘回归的自动切割树状图的解决方案，以找到最佳切割水平。树状图是显示软件实体群集的分类学关系的树形图。此外，本文介绍了惩罚将形成单例的群集的因素。在两个开源项目上进行了仿真。将该方法与穷举和最大间隙树状图切割方法以及两个众所周知的聚类有效性指标，即Dunn指数和Davies-Bouldin指数进行了比较。结果：当将我们的聚类结果与原始包装图进行比较时，在除去效用类别后的两次模拟中，我们的方法实现了90.07％的平均准确率。由于其无所不在的行为，源代码中的实用程序类会影响软件群集的准确性。提出的方法还成功地惩罚了聚类期间单例聚类的形成。结论：评估表明，该方法可以通过指导软件维护人员完成切入点选择过程来提高聚类结果的质量。所提出的方法可以用作补充机制，以提高现有聚类算法的有效性。

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  • 来源
    《Information and software technology》 |2013年第11期|1994-2012|共19页
  • 作者

    Chun Yong Chong; Sai Peck Lee; Teck Chaw Ling;

  • 作者单位

    Department of Software Engineering, Faculty of Computer Science and IT, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia;

    Department of Software Engineering, Faculty of Computer Science and IT, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia;

    Department of Computer System and Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Software maintenance; Design recovery; Software clustering; Remodularization;

    机译：软件维护;设计恢复;软件集群;重新调制;

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