Software Reliability Prediction Using Wavelet Neural Networks

Abstract

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.

A survey of software reliability growth models using non-parametric methods

“…Raj Kiran and Ravi [15] propose a different approach -Wavelet Neural Network (WNN) to predict software reliability. Wavelets can transform image data better than Fourier Transform.…”

An approach to software reliability prediction based on time series modeling

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“…As a result, approaches based on neural networks and other machine learning methods [16,17,18,19,20,21,22] have been introduced. However, the main disadvantage of these approaches is that they require a large training data set as input/output examples to get a correct learning, which is computationally intensive and time consuming process.…”

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

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