Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Handbook of software reliability engineering
Handbook of software reliability engineering
On estimating the number of defects remaining in software
Journal of Systems and Software
An introduction to variational methods for graphical models
Learning in graphical models
Software metrics: success, failures and new directions
Journal of Systems and Software - Special issue on invited articles on top systems and software engineering scholars
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
On the neural network approach in software reliability modeling
Journal of Systems and Software
Reliability prediction for component-based software architectures
Journal of Systems and Software - Special issue on: Software architecture - Engineering quality attributes
A Bayesian predictive software reliability model with pseudo-failures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using Bayesian network on network tomography
Computer Communications
Bayesian network based software reliability prediction with an operational profile
Journal of Systems and Software
Software reliability prediction by soft computing techniques
Journal of Systems and Software
Bayesian updating of optimal release time for software systems
Software Quality Control
A survey of online failure prediction methods
ACM Computing Surveys (CSUR)
An approach for early prediction of software reliability
ACM SIGSOFT Software Engineering Notes
Assessing fault occurrence likelihood for service-oriented systems
ICWE'11 Proceedings of the 11th international conference on Web engineering
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
Engineering Applications of Artificial Intelligence
An approach to software reliability prediction based on time series modeling
Journal of Systems and Software
System reliability calculation based on the run-time analysis of ladder program
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Due to the complexity of software products and development processes, software reliability models need to possess the ability of dealing with multiple parameters. Also in order to adapt to the continually refreshed data, they should provide flexibility in model construction in terms of information updating. Existing software reliability models are not flexible in this context. The main reason for this is that there are many static assumptions associated with the models. Bayesian network is a powerful tool for solving this problem, as it exhibits strong ability to adapt in problems involving complex variant factors. In this paper, a software prediction model based on Markov Bayesian networks is developed, and a method to solve the network model is proposed. The use of our model is illustrated with an example.