Alasmari, Naif, Calinescu, Radu orcid.org/0000-0002-2678-9260, Paterson, Colin orcid.org/0000-0002-6678-3752 et al. (1 more author) (2022) Quantitative Verification with Adaptive Uncertainty Reduction. Journal of Systems and Software. 111275. ISSN 0164-1212
Abstract
Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification depends on how accurately the parameters of these models can be estimated using data from component unit testing, monitoring, system logs, etc. When insufficient data are available, the models are affected by epistemic parametric uncertainty, the verification results are inaccurate, and any engineering decisions based on them may be invalid. To address these problems, we introduce VERACITY, a tool-supported iterative approach for the efficient and accurate verification of nonfunctional requirements under epistemic parameter uncertainty. VERACITY integrates confidence-interval quantitative verification with a new adaptive uncertainty reduction heuristic that collects additional data about the parameters of the verified model by unit-testing specific system components over a series of verification iterations. VERACITY supports the quantitative verification of discrete-time Markov chains, deciding which components are to be tested in each iteration based on factors that include the sensitivity of the model to variations in the parameters of different components, and the overheads (e.g., time or cost) of unit-testing each of these components. We show the effectiveness and efficiency of VERACITY by using it for the verification of the nonfunctional requirements of a tele-assistance service-based system and an online shopping web application.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | quantitative verification,probabilistic model checking,confidence intervals,uncertainty reduction,nonfunctional requirements,unit testing |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 Feb 2022 09:40 |
Last Modified: | 07 Feb 2025 00:33 |
Published Version: | https://doi.org/10.1016/j.jss.2022.111275 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.jss.2022.111275 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183788 |
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