Professor Frank Coolen frank.coolen@durham.ac.uk
Professor
We consider optimal testing of a system in order to demonstrate reliability with regard to its use in a process after testing, where the system has to function for different types of tasks, which we assume to be independent. We explicitly assume that testing reveals zero failures. The optimal numbers of tasks to be tested are derived by optimisation of a cost criterion, taking into account the costs of testing and the costs of failures in the process after testing, assuming that such failures are not catastrophic to the system. Cost and time constraints on testing are also included in the analysis. We focus on study of the optimal numbers of tests for different types of tasks, depending on the arrival rate of tasks in the process and the costs involved. We briefly compare the results of this study with optimal test numbers in a similar setting, but with an alternative optimality criterion which is more suitable in case of catastrophic failures, as presented elsewhere. For these two different optimality criteria, the optimal numbers to be tested depend similarly on the costs of testing per type and on the arrival rates of tasks in the process after testing.
Coolen, F., Coolen-Schrijner, P., & Rahrouh, M. (2005). Bayesian reliability demonstration with multiple independent tasks. IMA Journal of Management Mathematics, 17(2), 131-142. https://doi.org/10.1093/imaman/dpi030
Journal Article Type | Article |
---|---|
Publication Date | 2005-04 |
Journal | IMA Journal of Management Mathematics |
Print ISSN | 1471-678X |
Electronic ISSN | 1471-6798 |
Publisher | Oxford University Press |
Peer Reviewed | Peer Reviewed |
Volume | 17 |
Issue | 2 |
Pages | 131-142 |
DOI | https://doi.org/10.1093/imaman/dpi030 |
Keywords | Budget and time constraints on testing, Costs of testing and process failures. |
Public URL | https://durham-repository.worktribe.com/output/1598788 |
Parametric Predictive Bootstrap Method for the Reproducibility of Hypothesis Tests
(2025)
Journal Article
Nonparametric Predictive Inference for Two Future Observations with Right-Censored Data
(2024)
Journal Article
Nonparametric Predictive Inference for Discrete Lifetime Data
(2024)
Journal Article
Reproducibility of estimates based on randomised response methods
(2024)
Journal Article
A Bayesian Imprecise Classification method that weights instances using the error costs
(2024)
Journal Article
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search