Author(s): L. Tian, A. Noore

Reference:Reliability Engineering and System Safety, vol. 87, no. 1, pp. 45-51, Jan. 2005

Abstract: An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using realtime control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.

Keywords: Neural networks, software reliability growth prediction, genetic algorithm, failure time data