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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| Published in | Information and software technology Vol. 53; no. 6; pp. 601 - 614 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.06.2011
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-5849 1873-6025 |
| DOI | 10.1016/j.infsof.2011.01.006 |
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| Abstract | 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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| AbstractList | 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. 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. Context 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. Objective 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. Method 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. Results 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. Conclusions 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. 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. |
| Author | Cui, Jian Feng Chae, Heung Seok |
| Author_xml | – sequence: 1 givenname: Jian Feng surname: Cui fullname: Cui, Jian Feng organization: Department of Computer Science and Technology, Xiamen University of Technology, 600 LiGong Rd., Xiamen 361024, China – sequence: 2 givenname: Heung Seok surname: Chae fullname: Chae, Heung Seok email: hschae@pusan.ac.kr organization: Department of Science and Engineering, Pusan National University, 30 Changjeon-dong, Keumjeong-gu, Busan 609-735, South Korea |
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| Snippet | Component identification, the process of evolving legacy system into finely organized component-based software systems, is a critical part of software... This paper focuses on analyzing agglomerative hierarchical clustering algorithms in software reengineering, and then identifying their respective strengths and... Context Component identification, the process of evolving legacy system into finely organized component-based software systems, is a critical part of software... |
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| SubjectTerms | 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 |
| Title | Applying agglomerative hierarchical clustering algorithms to component identification for legacy systems |
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