%0 Journal Article
%A Ling Beilei
%A Sun Jiaze
%T Density PSO-based software module clustering algorithm
%D 2018
%R 10.19682/j.cnki.1005-8885.2018.1015
%J Journal of China Universities of Posts and Telecommunications
%P 38-47
%V 25
%N 4
%X Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution, density PSO-based (DPSO) software module clustering algorithm is proposed. Firstly, the software system is converted into complex network diagram, and then the particle swarm optimization (PSO) algorithm is improved. The shortest path method is used to initialize the swarm and the probability selection approach is used to update the particle positions. Furthermore, density-based modularization quality (DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of<br>
the DPSO algorithm. Hill climbing (HC) algorithm, genetic algorithm (GA), PSO and DPSO algorithm are compared in the modularization quality (MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than other traditional three algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2018.1015