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Safety Invariant Verification that Meets Engineers’ Expectations

Lookup NU author(s): Dr Alexei Iliasov, Dr Linas Laibinis, Dr Ilya Lopatkin, Emeritus Professor Alexander RomanovskyORCiD

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer, 2022.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

This industrial experience report discusses the problems we have been facing while using our formal verification technology, called SafeCap, in a substantial number of live signalling projects in the UK mainline rail, and the solutions we are now developing to counter these.Symbolic execution and safety invariant verification are well-understood subjects and yet their application to real life high assurance systems requires going a few steps beyond the conventional practice. In engineering practice it is not sufficient to simply know that a safety property fails:one needs to know why and hence where and what exactly fails; it is also crucial to positively demonstrate no safety failure is missing. In this industrial report we show how to derive a list of all potential errors by transforming safety invariant predicate using information from symbolic state transition system. The identified possible errors are verified by an automated symbolic prover, while a report generator presents findings in an engineer-friendly format to guide subsequent rework steps.


Publication metadata

Author(s): Iliasov A, Laibinis L, Taylor D, Lopatkin I, Romanovsky A

Editor(s): Simon Collart-Dutilleul, Anne E. Haxthausen, Thierry Lecomte

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Modelling, Analysis, Verification, and Certification: 4th International Conference on Reliability, Safety and Security of Railway Systems (RSSRail 2022)

Year of Conference: 2022

Pages: 20-31

Online publication date: 20/05/2022

Acceptance date: 02/02/2022

Date deposited: 27/12/2021

ISSN: 0302-9743

Publisher: Springer

URL: https://doi.org/10.1007/978-3-031-05814-1_2

DOI: 10.1007/978-3-031-05814-1_2

ePrints DOI: 10.57711/k0h0-9b39

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783031058134


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