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A runtime safety analysis concept for open adaptive systems

Kabir, Sohag; Sorokos, Ioannis; Aslansefat, Koorosh; Papadopoulos, Yiannis; Gheraibia, Youcef; Reich, Jan; Saimler, Merve; Wei, Ran


Sohag Kabir

Ioannis Sorokos

Youcef Gheraibia

Jan Reich

Merve Saimler

Ran Wei


© Springer Nature Switzerland AG 2019. In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architecture reconfiguration. Given the safety-critical nature of the applications involved, an approach for addressing safety in the context of reconfiguration impacting functional and non-functional properties at runtime is needed. In this paper, we introduce a concept for runtime safety analysis and decision input for open adaptive systems. We combine static safety analysis and evidence collected during operation to analyse, reason and provide online recommendations to minimize deviation from a system’s safe states. We illustrate our concept via an abstract vehicle platooning system use case.


Kabir, S., Sorokos, I., Aslansefat, K., Papadopoulos, Y., Gheraibia, Y., Reich, J., …Wei, R. (2019). A runtime safety analysis concept for open adaptive systems. Lecture notes in computer science, 11842, 332-346.

Journal Article Type Conference Paper
Conference Name Model-Based Safety and Assessment (IMBSA 2019)
Conference Location Thessaloniki, Greece
Acceptance Date Nov 14, 2018
Online Publication Date Oct 11, 2019
Publication Date Oct 11, 2019
Deposit Date Feb 17, 2021
Publicly Available Date May 26, 2021
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 11842
Pages 332-346
ISBN 9783030328719
Keywords Platooning; Bayesian networks; Model-based dependability analysis; Runtime assurance
Public URL


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Copyright Statement
©2019 The authors. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder

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