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PSO with Reverse Edge for Multi-Objective Software Module Clustering

Volume 14, Number 10, October 2018, pp. 2423-2431
DOI: 10.23940/ijpe.18.10.p18.24232431

Jiaze Suna,b, Yang Xua, and Shuyan Wanga,b

aSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China
bShaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

(Submitted on July 21, 2018; Revised on August 22, 2018; Accepted on September 15, 2018)


The multi-objective software module clustering problem (MOSMCP) divides the complex software system into subsystems to obtain a perfect structure, which is based on the relations between modules to meet the conflicting software refactor objectives as much as possible. The modularization quality (MQ) and reverse edges number between clusters are considered as evaluation objectives, and a novel particle swarm optimization (PSO) with reverse edge, called REPSO, is proposed. First, the module dependency graph (MDG) in software system under clustering is constructed, and then the multi-objective particle swarm optimization (MOPSO) is improved to cluster the MDG. The exploring approach is used to update the particle locations. Four typical open source projects for module clustering are selected to verify the effectiveness of the REPSO. The laboratorial results prove that the REPSO algorithm is very effective and stable, and the diversity of the optimal solution is good. The REPSO algorithm provides an efficient engineering method for MOSMCP, which enhances the software structure and maintainability.


References: 14

                1. Amarjeet and J. K. Chhabra, “Many-Objective Artificial Bee Colony Algorithm for Large-Scale Software Module Clustering Problem,” Soft Computing, Vol. 2017, No. 3, pp. 1-21, 2017
                2. D. Doval, S. Mancoridis, and B. S. Mitchell, “Automatic Clustering of Software Systems Using A Genetic Algorithm”. in Proceedings of IEEE Conference on Software Technology and Engineering Practice (STEP99), pp. 93-102, Pittsburgh, USA, September 1999
                3. J. H. Huang and J. Liu, “A Similarity-based Modularization Quality Measure for Software Module Clustering Problems,” Information Sciences, Vol. 342, No. C, pp. 96-110, 2016
                4. J. H. Huang and X. Yao, “A Multi-Agent Evolutionary Algorithm for Software Module Clustering Problems,” Soft Computing, Vol. 21, No. 13, pp. 3415-3428, 2017
                5. I. Hussain, A. Khanum, A. Q. Abbasi, and M. Y. Javed, “A Novel Approach for Software Architecture Recovery Using Particle Swarm Optimization,” International Arab Journal of Information Technology, Vol. 12, No. 1, pp. 32-41, 2015
                6. A. C. Kumari and K. Srinivas, “Software Module Clustering Using a Fast-Multi-Objective Hyper-Heuristic Evolutionary Algorithm,” International Journal of Applied Information Systems, Vol. 05, No. 6, pp. 12-18, 2010
                7. A. C. Kumari, K. Srinivas, and M. P. Gupta, “Software Module Clustering Using a Hyper-Heuristic based Multi-Objective Genetic Algorithm,” in Proceedings of the 2013 3rd IEEE International Advance Computing Conference, pp. 813-818, Ghaziabad, India, February 2013
                8. N. Li, T. Zou, D. B. Sun, and Y. Q. Qin, “Multi-Objective Optimization Utilizing Particle Swarm,” Computer Engineering and Applications, Vol. 3, No. 4, pp. 193-262, 2005
                9. S. Mancoridis, B. S. Mitchell, Y. Chen, and E. R. Gansner, “Bunch: A Clustering Tool for the Recovery and Maintenance of Software System Structures,” in Proceedings of IEEE International Conference on Software Maintenance, pp. 50-59, Oxford, UK, August 1999
                10. B. Monika and P. Singh, “Modularizing Software Systems Using PSO Optimized Hierarchical Clustering,” in Proceedings of 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), pp. 659-664, New Delhi, India ,March 2016
                11. K. Praditwong, M. Harman, and X. Yao, “Software Module Clustering as A Multi-Objective Search Problem,” IEEE Transactions on Software Engineering, Vol. 37, No. 2, pp. 264-282, 2015
                12. K. Praditwong and X. Yao, “A New Multi-Objective Evolutionary Optimization Algorithm: The Two-Archive Algorithm,” in Proceedings of the 2006 International Conference on Computational Intelligence and Security, Vol. 1, No. 4, pp. 286-291, 2015
                13. J. Z. Sun and B. L. Ling, “Software Module Clustering Algorithm Using Probability Selection,” Wuhan University Journal of Natural Sciences, Vol. 23, No. 2, pp. 93-102, 2018
                14. Y. Wang and J. C. Zeng, “A Survey of A Multi-objective Particle Swarm Optimization Algorithm,” CAAI Transactions on Intelligent Systems, Vol. 5, No. 5, pp. 377-384, 2010


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