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
Software reliability models for computer implementations—an empirical study
Software—Practice & Experience
Neural networks for software reliability engineering
Handbook of software reliability engineering
Software defect and operational profile modeling
Software defect and operational profile modeling
Neural networks and intellect: using model-based concepts
Neural networks and intellect: using model-based concepts
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks: Theoretical Foundations and Analysis
Neural Networks: Theoretical Foundations and Analysis
On the neural network approach in software reliability modeling
Journal of Systems and Software
Optimal software release scheduling based on artificial neural networks
Annals of Software Engineering
Using Neural Networks in Reliability Prediction
IEEE Software
A Unified Scheme of Some Nonhomogenous Poisson Process Models for Software Reliability Estimation
IEEE Transactions on Software Engineering
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Software Reliability Growth Modeling: Models and Applications
IEEE Transactions on Software Engineering
IEEE Transactions on Neural Networks
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
Towards a middleware for configuring large-scale storage infrastructures
Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science
Software Reliability Prediction Using Group Method of Data Handling
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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
Application of feed-forward neural networks for software reliability prediction
ACM SIGSOFT Software Engineering Notes
Software quality assurance using software reliability growth modelling: state of the art
International Journal of Business Information Systems
An automated framework for software test oracle
Information and Software Technology
Artificial neural networks as multi-networks automated test oracle
Automated Software Engineering
Hybrid intelligent systems for predicting software reliability
Applied Soft Computing
Application of Machine Learning Techniques to Predict Software Reliability
International Journal of Applied Evolutionary Computation
A study on software reliability prediction models using soft computing techniques
International Journal of Information and Communication Technology
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Software reliability is the probability of failure-free software operation for a specified period of time in a specified environment. During the last three decades, many software reliability growth models (SRGMs) have been proposed and analyzed for measuring software reliability growth. SRGMs are mathematical models that represent software failures as a random process and can be used to evaluate development status during testing. However, most of SRGMs depend on some assumptions or distributions. In this paper, we propose an artificial neural-network-based approach for software reliability estimation and modeling. We first explain the neural networks from the mathematical viewpoints of software reliability modeling. We will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model (DWCM). The applicability of proposed model is demonstrated through real software failure data sets. The results obtained from the experiments show that the proposed model has a fairly accurate prediction capability.