Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
On the neural network approach in software reliability modeling
Journal of Systems and Software
Using Neural Networks in Reliability Prediction
IEEE Software
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Journal of Systems and Software
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Application of feed-forward neural networks for software reliability prediction
ACM SIGSOFT Software Engineering Notes
Traceability of executable codes using neural networks
ISC'10 Proceedings of the 13th international conference on Information security
Design of ensemble neural network using entropy theory
Advances in Engineering Software
Long-term potential performance degradation analysis method based on dynamical probability model
Expert Systems with Applications: An International Journal
Assessment of software testing time using soft computing techniques
ACM SIGSOFT Software Engineering Notes
Expert Systems with Applications: An International Journal
An ensemble of computational intelligence models for software maintenance effort prediction
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
International Journal of Intelligent Systems Technologies and Applications
Hi-index | 12.06 |
Software reliability is an important factor for quantitatively characterizing software quality and estimating the duration of software testing period. Traditional parametric software reliability growth models (SRGMs) such as nonhomogeneous Poisson process (NHPP) models have been successfully utilized in practical software reliability engineering. However, no single such parametric model can obtain accurate prediction for all cases. In addition to the parametric models, non-parametric models like neural network have shown to be effective alternative techniques for software reliability prediction. In this paper, we propose a non-parametric software reliability prediction system based on neural network ensembles. The effects of system architecture on the performance are investigated. The comparative studies between the proposed system with the single neural network based system and three parametric NHPP models are carried out. The experimental results demonstrate that the system predictability can be significantly improved by combing multiple neural networks.