On-line prediction of software reliability using an evolutionary connectionist model

Abstract

An on-line adaptive software reliability prediction model using evolutionary connectionist approach based on multiple-delayed-input single-output architecture is proposed. Based on the currently available software failure time data, 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. Bayesian regularization is applied to our network training scheme to improve the generalization capability. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-step-predictability compared to existing neural network model for failure time prediction.