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Abstract
Software module clustering is the problem of automatically organizing software units into modules to improve program structure. There has been a great deal of recent interest in search-based formulations of this problem in which module boundaries are identified by automated search, guided by a fitness function that captures the twin objectives of high cohesion and low coupling in a single-objective fitness function. This paper introduces two novel multi-objective formulations of the software module clustering problem, in which several different objectives (including cohesion and coupling) are represented separately. In order to evaluate the effectiveness of the multi-objective approach, a set of experiments was performed on 17 real-world module clustering problems. The results of this empirical study provide strong evidence to support the claim that the multi-objective approach produces significantly better solutions than the existing single-objective approach.
Original language | English |
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Pages (from-to) | 264-282 |
Number of pages | 19 |
Journal | IEEE Transactions on Software Engineering |
Volume | 37 |
Issue number | 2 |
Early online date | 5 Feb 2010 |
DOIs | |
Publication status | Published - 1 Mar 2011 |
Keywords
- multi-objective optimization
- evolutionary computation
- module clustering
- SBSE
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Dive into the research topics of 'Software Module Clustering as a Multi-Objective Search Problem'. Together they form a unique fingerprint.Projects
- 1 Finished
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SEBASE: Software Engineered By Automated SEarch
Engineering & Physical Science Research Council
29/06/06 → 28/12/11
Project: Research Councils