Foster, Simon David orcid.org/0000-0002-9889-9514, Nemouchi, Yakoub, O'Halloran, Colin et al. (2 more authors) (2020) Formal Model-Based Assurance Cases in Isabelle/SACM:An Autonomous Underwater Vehicle Case Study. In: FormaliSE '20:Proceedings of the 8th International Conference on Formal Methods in Software Engineering. ACM
Abstract
Isabelle/SACM is a tool for automated construction of model-based assurance cases with integrated formal methods, based on the Isabelle proof assistant. Assurance cases show how a system is safe to operate, through a human comprehensible argument demonstrating that the requirements are satisfied, using evidence of various provenances. They are usually required for certification of critical systems, often with evidence that originates from formal methods. Automating assurance cases increases rigour, and helps with maintenance and evolution. In this paper we apply Isabelle/SACM to a fragment of the assurance case for an autonomous underwater vehicle demonstrator. We encode the metric unit system (SI) in Isabelle, to allow modelling requirements and state spaces using physical units. We develop a behavioural model in the graphical RoboChart state machine language, embed the artifacts into Isabelle/SACM, and use it to demonstrate satisfaction of the requirements.
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: | 06 May 2020 10:10 |
Last Modified: | 24 Oct 2024 00:28 |
Published Version: | https://doi.org/10.1145/3372020.3391559 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3372020.3391559 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160376 |