Prediction of software reliability using neural networks

Software reliability growth models have achieved considerable importance in estimating reliability of software products. The authors explore the use of feed-forward neural networks as a model for software reliability growth prediction. To empirically evaluate the predictive capability of this new approach, data sets from different software projects are used. The neural networks approach exhibits a consistent behavior in prediction and the predictive performance is comparable to that of parametric models.<>