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Cooperative clustering for software modularization
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AI
DOI：10.1016/j.jss.2013.03.080.png)
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Abstract
En 中文
Clustering is a useful technique to group data entities. Many different algorithms have been proposed for software clustering. To combine the strengths of various algorithms, researchers have suggested the use of Consensus Based Techniques (CBTs), where more than one actors (e.g. algorithms) work together to achieve a common goal. Although the use of CBTs has been explored in various disciplines, no work has been done for modularizing software. In this paper, the main research question we investigate is whether the Cooperative Clustering Technique (CCT), a type of CBT, can improve software modularization results. The main contributions of this paper are as follows. First, we propose our CCT in which more than one similarity measure cooperates. during the hierarchical clustering process. To this end, we present an analysis of well-known measures. Second, we present a cooperative clustering approach for two types of well-known agglomerative hierarchical software clustering algorithms, for binary as well as non-binary features. Third, to evaluate our proposed CCT, we conduct modularization experiments on five software systems. Our analysis identifies certain cases that reveal weaknesses of the individual similarity measures. The experimental results support our hypothesis that these weaknesses may be overcome by using more than one measure, as our CCT produces better modularization results for test systems in which these cases occur. We conclude that CCTs are capable of showing significant improvement over individual clustering algorithms for software modularization. (c) 2013 Elsevier Inc. All rights reserved.
Keywords:
Cooperative clustering
Software clustering
Jaccard-NM
Unbiased Ellenberg-NM
Feature vector cases
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