Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Handbook of software reliability engineering
Handbook of software reliability engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On the neural network approach in software reliability modeling
Journal of Systems and Software
Using Neural Networks in Reliability Prediction
IEEE Software
Analysis of error processes in computer software
Proceedings of the international conference on Reliable software
Analysis of software reliability and performance
Analysis of software reliability and performance
Software Reliability Engineering: More Reliable Software Faster and Cheaper
Software Reliability Engineering: More Reliable Software Faster and Cheaper
An integration of fault detection and correction processes in software reliability analysis
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Journal of Systems and Software
Effect of the Delay Time in Fixing a Fault on Software Error Models
COMPSAC '07 Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 02
Application of feed-forward neural networks for software reliability prediction
ACM SIGSOFT Software Engineering Notes
A survey of computational intelligence approaches for software reliability prediction
ACM SIGSOFT Software Engineering Notes
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In current software reliability modeling research, the main concern is how to develop general prediction models. In this paper, we propose several improvements on the conventional software reliability growth models (SRGMs) to describe actual software development process by eliminating some unrealistic assumptions. Most of these models have focused on the failure detection process and not given equal priority to modeling the fault correction process. But, most latent software errors may remain uncorrected for a long time even after they are detected, which increases their impact. The remaining software faults are often one of the most unreliable reasons for software quality. Therefore, we develop a general framework of the modeling of the failure detection and fault correction processes. Furthermore, we apply neural network with back-propagation to match the histories of software failure data. We will also illustrate how to construct the neural networks from the mathematical viewpoints of software reliability modeling in detail. Finally, numerical examples are shown to illustrate the results of the integration of the detection and correction process in terms of predictive ability and some other standard criteria.