Imprecise probabilistic inference for software run reliability growth models

Utkin, L.V.; Coolen, F.P.A.

Imprecise probabilistic inference for software run reliability growth models

Utkin, L.V.; Coolen, F.P.A.

Authors

L.V. Utkin

Professor Frank Coolen frank.coolen@durham.ac.uk
Professor

Abstract

This paper presents the application of an inferential statistical approach which combines imprecise Bayesian methods with likelihood inference, to a standard software run reliability growth model. The main idea of the approach is to divide the set of model parameters into two subsets related to fundamentally different aspects of the overall model, and to combine an imprecise Bayesian method related to one of the subsets of the model parameters with maximum likelihood estimation for the other subset. In accordance with the first subset and statistical data, the imprecise Bayesian model is constructed, which provides lower and upper predictive probability distributions depending on the second subset of parameters. These further parameters are then estimated by a maximum likelihood method. This method is applied to a basic software run reliability growth model and it is shown to perform better than a standard model. Several aspects related to the method are discussed, including its advantages, its wider applicability and the possibility to include relevant expert judgements.

Citation

Utkin, L., & Coolen, F. (2018). Imprecise probabilistic inference for software run reliability growth models. Journal of uncertain systems, 12(4), 292-308

Journal Article Type	Article
Acceptance Date	Nov 5, 2018
Online Publication Date	Nov 30, 2018
Publication Date	Nov 1, 2018
Deposit Date	Nov 12, 2018
Publicly Available Date	Nov 13, 2018
Journal	Journal of uncertain systems.
Print ISSN	1752-8909
Electronic ISSN	1752-8917
Publisher	World Academic Union
Peer Reviewed	Peer Reviewed
Volume	12
Issue	4
Pages	292-308
Publisher URL	http://www.worldacademicunion.com/journal/jus/online.htm

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Copyright Statement
Utkin, L.V. & Coolen, F.P.A. (2018). Imprecise probabilistic inference for software run reliability growth models. Journal of Uncertain Systems 12(4): 292-308. © 2018 World Academic Union. All rights reserved.