Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
Neural Computation
On the neural network approach in software reliability modeling
Journal of Systems and Software
Using Neural Networks in Reliability Prediction
IEEE Software
Using the genetic algorithm to build optimal neural networks for fault-prone module detection
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Evolutionary Neural Networks: A Robust Approach to Software Reliability Problems
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Journal of Systems and Software
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Journal of Systems and Software
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Bayesian updating of optimal release time for software systems
Software Quality Control
Software Reliability Prediction Using Group Method of Data Handling
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Application of feed-forward neural networks for software reliability prediction
ACM SIGSOFT Software Engineering Notes
Assessment of software testing time using soft computing techniques
ACM SIGSOFT Software Engineering Notes
Hybrid intelligent systems for predicting software reliability
Applied Soft Computing
Application of Machine Learning Techniques to Predict Software Reliability
International Journal of Applied Evolutionary Computation
International Journal of Computer Applications in Technology
A survey of computational intelligence approaches for software reliability prediction
ACM SIGSOFT Software Engineering Notes
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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.