Software reliability analysis models
IBM Journal of Research and Development
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
Handbook of software reliability engineering
Handbook of software reliability engineering
On the neural network approach in software reliability modeling
Journal of Systems and Software
Software Reliability Engineering: More Reliable Software Faster and Cheaper
Software Reliability Engineering: More Reliable Software Faster and Cheaper
System Software Reliability (Springer Series in Reliability Engineering)
System Software Reliability (Springer Series in Reliability Engineering)
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Journal of Systems and Software
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
IBM Systems Journal
The implementation of artificial neural networks applying to software reliability modeling
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Assessment of software testing time using soft computing techniques
ACM SIGSOFT Software Engineering Notes
A study on software reliability prediction models using soft computing techniques
International Journal of Information and Communication Technology
International Journal of Intelligent Systems Technologies and Applications
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Many analytical models have been proposed for modeling software reliability growth trends with different predictive capabilities at different phases of testing yet there still is a need to develop a model that can be applied for accurate predictions in a realistic environment. In this paper we describe a software reliability prediction model using feed-forward neural network for better reliability prediction through back-propagation algorithm and discuss the issues of network architecture and data representation methods. We demonstrate a comparative analysis between the proposed approach and three well known software reliability growth prediction models using seven different failure datasets collected from standard software projects to test the validity of the presented method. A numerical example also has been cited to illustrate the results that revealed significant improvement by using Artificial Neural Network (ANN) over conventional statistical models based on NHPP.