Published Paper Details:

Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment. Software reliability modelling has gained a lot of importance in the recent years. Criticality of software in many of the present day applications has led to a tremendous increase in the amount of work being carried out in this area. The use of intelligent neural network and hybrid techniques in place of the traditional statistical techniques has shown a remarkable improvement in the development of prediction models for software reliability in the recent years. Among the intelligent and the statistical techniques it is not easy to identify the best one since their performance varies with the change in data. In this paper, we propose an artificial neural network-based approach for developing the model for software reliability estimation. We first explain the neural networks from the mathematical viewpoints of software reliability modeling. That is, we will show how to apply neural network to develop a model for the prediction of software reliability. The implementation of proposed model is shown with real software failure data sets. From simulation results, we can see that the proposed model significantly outperforms the traditional software reliability models.