Software Reliability Prediction Using Wavelet Neural Networks

In this paper, we propose the use of wavelet neural networks (WNN) to predict software reliability. In WNN, we employed two kinds of wavelets - Morlet wavelet and Gaussian wavelet as transfer functions resulting in two variants of WNN. The effectiveness of WNN is demonstrated on a data set taken from literature. Its performance is compared with that of multiple linear regression (MLR), multivariate adaptive regression splines (MARS), backpropagation trained neural network (BPNN), threshold accepting trained neural network (TANN), pi-sigma network (PSN), general regression neural network (GRNN), dynamic evolving neuro-fuzzy inference system (DENFIS) and TreeNet in terms of normalized root mean square error (NRMSE) obtained on test data. Based on the experiments performed, it is observed that the WNN outperformed all the other techniques.