APPLYING NEURAL NETWORKS TO SOFTWARE RELIABILITY ASSESSMENT

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application - only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.