Combining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy to use, and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes. In this research, we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction. Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.