An Artificial Neural Network Model based on Binary Particle Swarm Optimization for enhancing the efficiency of Software Defect Prediction

Abstract

With the rise in the growth of the software industry, it is essential to identify software defects in earlier stages to save costs and improve the efficiency of the software development lifecycle process. We have devised a hybrid software defect prediction (SDP) model that integrates Binary Particle Swarm Optimization (Binary PSO), Synthetic Minority Oversampling Technique (SMOTE), and Artificial Neural Network (ANN). BPSO is applied as a wrapper feature selection process utilizing AUC as a fitness function, SMOTE handles the dataset imbalance, and ANN is used as a classification algorithm for predicting software defects. We analyze the proposed BPSO-SMOTE-ANN model's predictive capability using the AUC and G-mean performance metrics. The proposed hybrid model is found helpful in predicting software defects. The statistical results suggest the enhanced performance of the proposed hybrid model concerning AUC and G-mean values. Also, the hybrid model was found to be competitive with other machine learning(ML) algorithms in determining software defects.