Calinescu, Radu Constantin orcid.org/0000-0002-2678-9260, Paterson, Colin orcid.org/0000-0002-6678-3752 and Johnson, Kenneth Harold Anthony (2019) Efficient Parametric Model Checking Using Domain Knowledge. IEEE Transactions on Software Engineering. 8698796. ISSN 0098-5589
Abstract
We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. ePMC speeds up the analysis of parametric Markov chains modelling the behaviour of software by exploiting domain-specific modelling patterns for the software components (e.g., patterns modelling the invocation of functionally-equivalent services used to jointly implement the same operation within service-based systems, or the deployment of the components of multi-tier software systems across multiple servers). To this end, ePMC precomputes closed-form expressions for key QoS properties of such patterns, and uses these expressions in the analysis of whole-system models. To evaluate ePMC, we show that its application to service-based systems and multi-tier software architectures reduces the analysis time by several orders of magnitude compared to current parametric model checking methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Copyright 2019 IEEE - All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
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: | 24 Apr 2019 09:40 |
Last Modified: | 23 Jan 2025 00:19 |
Published Version: | https://doi.org/10.1109/TSE.2019.2912958 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1109/TSE.2019.2912958 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145222 |