A hybrid CRBA-SVM model for software defect prediction

Support vector machine (SVM) model is becoming an increasingly popular method in software defects prediction. This model has strong non-linear classifying ability. However, SVM model lacks effective method to determine the best parameters. In this paper, a modified bat algorithm, named changing range bat algorithm, is employed to optimise the parameters of SVM model. To test the performance of this new model, several public datasets of software defect prediction are employed and then the results are compared with other five approaches. Experimental results show that the classification ability of hybrid CRBA-SVM model surpasses all other approaches.