Dai, Xiaotian orcid.org/0000-0002-6669-5234, Zhao, Shuai, Lesage, Benjamin Michael Jean-Rene et al. (1 more author) (2022) Using digital twins in the development of complex dependable real-time embedded systems. In: International Symposium on Leveraging Applications of Formal Methods:. Leveraging Applications of Formal Methods, Verification and Validation. Practice, 22-30 Oct 2022 Lecture Notes in Computer Science (LNCS) . Springer , GRC , pp. 37-53.
Abstract
Modelling execution times in complex real-time embedded systems is vital for understanding and predicting tasks’ temporal behaviour, and to improve the system scheduling performance. Previous research mainly relied on worst-case execution time estimations based on formal static analyses that are often pessimistic. The models that resulted are hard to maintain and even harder to validate. In this work, the novel use of Digital Twins provides opportunities to improve this issue and beyond for dependable real-time systems. We aim to establish and contribute to three questions: (i) how to easily model execution times with an adequate level of abstraction, and how to evaluate the quality of that model; (ii) how to identify errors in the models and how to evaluate the impact of errors; and (iii) how to make decisions as to when and how to improve the models. In this paper, we proposed a Digital Twin-based adaptation framework, and demonstrated its use for modelling and refining execution time profiles. Key decisions concerning the quality of the model and its impact on performance are evaluated. Finally, some challenges and key research questions for the formal method community are proposed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 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: | 16 Nov 2022 16:40 |
Last Modified: | 05 Mar 2025 00:11 |
Published Version: | https://doi.org/10.1007/978-3-031-19762-8 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (LNCS) |
Identification Number: | 10.1007/978-3-031-19762-8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193479 |
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