Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
An Examination of Fault Exposure Ratio
IEEE Transactions on Software Engineering - Special issue on software reliability
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Optimal software release scheduling based on artificial neural networks
Annals of Software Engineering
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
Defect-Based Reliability Analysis for Mission-Critical Software
COMPSAC '00 24th International Computer Software and Applications Conference
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
Heuristic Self-Organization Algorithms for Software Reliability Assessment and Their Applications
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Preliminary Data Analysis Methods in Software Estimation
Software Quality Control
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Using industry based data sets in software engineering research
Proceedings of the 2006 international workshop on Summit on software engineering education
Journal of Systems and Software
Quantitative software security risk assessment model
Proceedings of the 2007 ACM workshop on Quality of protection
Journal of Systems and Software
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International 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
Dependability metrics
Bluetooth indoor localization with multiple neural networks
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Prediction of defect distribution based on project characteristics for proactive project management
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Software quality assurance using software reliability growth modelling: state of the art
International Journal of Business Information Systems
Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters
Microelectronics Journal
Selection of software reliability model based on BP neural network
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
An approach to software reliability prediction based on time series modeling
Journal of Systems and Software
Radial basis function network using intuitionistic fuzzy C means for software cost estimation
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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It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external parameters. Using the failure history, the neural-network model automatically develops its own internal model of the failure process and predicts future failures. Because it adjusts model complexity to match the complexity of the failure history, it can be more accurate than some commonly used analytic models. Results with actual testing and debugging data which suggest that neural-network models are better at endpoint predictions than analytic models are presented.