title: Software reliability prediction using recurrent neural network with Bayesian regularization.
abstract: A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. 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 both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.
authors.affiliation: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6109, USA. tian@csee.wvu.edu
authors.forename: Liang
authors.initials: L
authors.lastname: Tian
authors.forename: Afzel
authors.initials: A
authors.lastname: Noore
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012984:Software / Q000592:standards*