Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jss.2004.02.028
Title: Software failure prediction based on a Markov Bayesian network model
Authors: Bai, C.G.
Hu, Q.P.
Xie, M. 
Ng, S.H. 
Keywords: Bayesian network
Markov Bayesian network
Reliability models
Software failure
Software reliability
Issue Date: 1-Feb-2005
Source: Bai, C.G., Hu, Q.P., Xie, M., Ng, S.H. (2005-02-01). Software failure prediction based on a Markov Bayesian network model. Journal of Systems and Software 74 (3) : 275-282. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jss.2004.02.028
Abstract: 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. © 2004 Elsevier Inc. All rights reserved.
Source Title: Journal of Systems and Software
URI: http://scholarbank.nus.edu.sg/handle/10635/63321
ISSN: 01641212
DOI: 10.1016/j.jss.2004.02.028
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