A new model for software defect prediction using Particle Swarm Optimization and support vector machine

Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model.