Applying agglomerative hierarchical clustering algorithms to component identification for legacy systems

Component identification, the process of evolving legacy system into finely organized component-based software systems, is a critical part of software reengineering. Currently, many component identification approaches have been developed based on agglomerative hierarchical clustering algorithms. How...

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Vydáno v:Information and software technology Ročník 53; číslo 6; s. 601 - 614
Hlavní autoři: Cui, Jian Feng, Chae, Heung Seok
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.06.2011
Elsevier Science Ltd
Témata:
Agglomeration
Agglomerative hierarchical clustering algorithm
Algorithms
Cluster analysis
Clustering
Cohesion
Component identification
Computer programs
Criteria
Joining
Legacy systems
Similarity
Similarity measure
Similarity measures
Software
Software engineering
Software reengineering
Studies
Weighting scheme
Weighting scheme
Legacy systems
Similarity measure
Software reengineering
Agglomerative hierarchical clustering algorithm
Component identification
ISSN:0950-5849, 1873-6025
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Shrnutí:Component identification, the process of evolving legacy system into finely organized component-based software systems, is a critical part of software reengineering. Currently, many component identification approaches have been developed based on agglomerative hierarchical clustering algorithms. However, there is a lack of thorough investigation on which algorithm is appropriate for component identification. This paper focuses on analyzing agglomerative hierarchical clustering algorithms in software reengineering, and then identifying their respective strengths and weaknesses in order to apply them effectively for future practical applications. A series of experiments were conducted for 18 clustering strategies combined according to various similarity measures, weighting schemes and linkage methods. Eleven subject systems with different application domains and source code sizes were used in the experiments. The component identification results are evaluated by the proposed size, coupling and cohesion criteria. The experimental results suggested that the employed similarity measures, weighting schemes and linkage methods can have various effects on component identification results with respect to the proposed size, coupling and cohesion criteria, so the hierarchical clustering algorithms produced quite different clustering results. According to the experimental results, it can be concluded that it is difficult to produce perfectly satisfactory results for a given clustering algorithm. Nevertheless, these algorithms demonstrated varied capabilities to identify components with respect to the proposed size, coupling and cohesion criteria.
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ISSN:0950-5849
1873-6025
DOI:10.1016/j.infsof.2011.01.006