Software module clustering problem (SMCP) is an important problem of software engineering field. The large-scale SMCPs are very difficult to solve by using the traditional deterministic optimization methods within a reasonable amount of time. The stochastic metaheuristic search optimization methods have been found to be an effective alternative to address the SMCPs in reasonable computation cost. Recently, particle swarm optimization (PSO) algorithm a metaheuristic search optimization method has gained wide attention toward research community and has been demonstrated as an effective and convenient algorithm to solve the various science and engineering problems. To the best of our knowledge, the applicability and usefulness of the PSO algorithm have not been studied by any researcher till date to address the SMCPs. In this paper, we present a module clustering approach for restructuring the software system using the PSO algorithm. To evaluate the proposed software module clustering approach, six real-world software systems are restructured and the obtained clustering solutions are compared with clustering solutions obtained with existing state-of-the-art software module clustering algorithms (i.e., genetic algorithm, hill climbing, and simulated annealing) in terms of modularization quality (MQ), coupling, and cohesion. The statistical analysis of the MQ, coupling, and cohesion results of the clustering solution provides sufficient evidence that the proposed approach is able to generate more effective clustering solution compared to the existing state-of-the-art software module clustering algorithms.

Chapter Contents:

• Abstract
• 20.1 Introduction
• 20.2 Related work
• 20.3 Clustering algorithms for SMCPs
• 20.3.1 GA-based software module clustering
• 20.3.2 HC-based software module clustering
• 20.3.3 SA-based software module clustering
• 20.3.4 Particle swarm optimization
• 20.4 Proposed approach
• 20.4.1 Generation of MDG
• 20.4.1.1 MDG encoding
• 20.4.1.2 GDPSO-based software module clustering
• 20.4.2 Particle representation and initialization
• 20.4.3 Particle fitness function
• 20.4.4 Particle status updating rules
• 20.5 Experimental study
• 20.5.1 Collecting results
• 20.5.1.1 Results evaluation metrics
• 20.5.1.2 Parameter setting of algorithms
• 20.5.1.3 Results and analysis
• 20.6 Conclusions and future works
• References

Inspec keywords: pattern clustering; software engineering; particle swarm optimisation; search problems; statistical analysis

Other keywords: stochastic metaheuristic search optimization methods; software module clustering approach; engineering problems; software engineering field; real-world software systems; PSO algorithm; software module clustering problem; metaheuristic search optimization method; large-scale SMCPs; particle swarm optimization; modularization quality; science problem; statistical analysis

Subjects: Data handling techniques; Software engineering techniques; Combinatorial mathematics; Optimisation techniques