Software reliability prediction using recurrent neural network with Bayesian regularization

International Journal of Neural Systems
Liang Tian, Afzel Noore

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.

References

Feb 2, 2008·IEEE Transactions on Neural Networks·F F LeungP S Tam

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Citations

Dec 31, 2009·IEEE Transactions on Neural Networks·Derrick T Mirikitani, Nikolay Nikolaev
Jan 4, 2018·International Journal of Neural Systems·Yicun Ouyang, Hujun Yin
Nov 27, 2018·Royal Society Open Science·Keith CarlsonDaniel Rockmore

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