Software reliability analysis models
IBM Journal of Research and Development
Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
Optimal Release Times for Software Systems with Scheduled Delivery Time Based on the HGDM
IEEE Transactions on Computers
Determining the Cost of a Stop-Test Decision
IEEE Software
Criteria for software reliability model comparisons
ACM SIGSOFT Software Engineering Notes
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
System Software Reliability (Springer Series in Reliability Engineering)
System Software Reliability (Springer Series in Reliability Engineering)
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Journal of Systems and Software
Software reliability prediction by soft computing techniques
Journal of Systems and Software
IBM Systems Journal
Architecture-based software reliability modeling
Journal of Systems and Software
Application of feed-forward neural networks for software reliability prediction
ACM SIGSOFT Software Engineering Notes
Support vector regression for software reliability growth modeling and prediction
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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Accurate software reliability prediction can not only enable developers to improve the quality of software but also provide useful information to help them for planning valuable resources. In this paper, we examine an analytical perspective of software reliability prediction using soft computing techniques with specific focus on methods, metrics and datasets. Based on the investigated results, usage percentage of datasets of public domain and soft computing techniques has increased significantly in last ten years. However, measurements using metrics are still the most dominant methods for predicting software reliability. In practice, intelligent machine learning techniques have shown remarkable improvements for reliability prediction. Therefore, software practitioners working on software reliability prediction should continue to use public datasets and other machine learning algorithms to build better prediction models. The significant findings of our study, in conjunction with previous research, could be used as guidelines for practitioners to make predictions in more realistic operating-context.