Software reliability prediction model based on PSO and SVM
Li-Na Qin · 2011
Software reliability prediction classifies software modules as fault-prone modules and less fault-prone modules at the early age of software development. As to a difficult problem of choosing parameters for Support Vector Machine (SVM), this paper introduces Particle Swarm Optimization (PSO) to automatically optimize the parameters of SVM, and constructs a software reliability prediction model based on PSO and SVM. Finally, the paper introduces Principal Component Analysis (PCA) method to reduce the dimension of experimental data, and inputs these reduced data into software reliability prediction model to implement a simulation. The results show that the proposed prediction model surpasses the traditional SVM in prediction performance.