Software Reliability prediction using Ensemble Model

Abstract

Software Reliability is the key factor of software quality estimation and prediction during testing period. We have implemented three models such as Radial Basis Function Neural Network (RBFNN) model, Ensemble model based on two types Feed Forward Neural Networks and one Radial Basis Function Neural Network and Radial basis function Neural Network Ensembles (RNNE) model for Software reliability prediction over five benchmark datasets. We have used Bayesian regularization method on all three models to avoid over-fitting problem and generalization of the neural network. We have been used two types of meaningful performance measures such as Relative Error (RE) and Average Errors (AE) for software reliability prediction. The results of all three proposed models have been compared with some traditional models such as Duane model and Artificial neural networks like Feed Forward Neural Network (FFNN) model. The experimental result shows that the nonparametric growth model called Ensemble model (multiple predictors) shows best minimal error than parametric model. Finally, It has been observed that the multiple predictors like Ensemble model always shows the best performance than single predictor like artificial neural network and some other traditional neural network