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

Abstract

The computational intelligence approach using Neural Network (NN) has been known to be very useful in predicting software reliability. Software reliability plays a key role in software quality. In order to improve accuracy and consistency of software reliability prediction, we propose the applicability of Feed Forward Back-Propagation Network (FFBPN) as a model to predict software reliability. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. Unlike most connectionist models, our model attempt to compute average error (AE), the root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE) simultaneously. A comparative study among the proposed feed-forward neural network with some traditional parametric software reliability growth model’s performance is carried out. The results indicated in this work suggest that FFBPN model exhibit an accurate and consistent behavior in reliability prediction.