Prediction of Software Reliability Using Feed Forward Neural Networks

Software reliability assessment has been a vital factor to characterize the quality of any software product quantitatively during testing phase. Over the years many analytical models have been proposed for modeling software reliability growth trends with different predictive capabilities at different phases of testing. Yet we need to develop such single model that can be applied for accurate prediction in all circumstances. Here we explore the applicability of neural network models for better prediction of reliability in a realistic environment and present an assessment method of software reliability growth using connectionist model. We apply feed forward back propagation algorithm and discuss the related issues of network architecture, method of data representation and some unrealistic assumptions incorporated with software reliability models. The model has been applied to different failure data sets collected from several standard software projects. A numerical example has been cited to illustrate the results revealing significant improvement by using artificial neural networks over conventional statistical models based on NHPP.