Athanasios Retouniotis
Model-connected safety cases
Retouniotis, Athanasios; Papadopoulos, Yiannis; Sorokos, Ioannis; Parker, David; Matragkas, Nicholas; Sharvia, Septavera
Authors
Professor Yiannis Papadopoulos Y.I.Papadopoulos@hull.ac.uk
Professor
Ioannis Sorokos
Dr David Parker D.J.Parker@hull.ac.uk
Lecturer
Nicholas Matragkas
Dr Septavera Sharvia S.Sharvia@hull.ac.uk
Lecturer
Abstract
© 2017, Springer International Publishing AG. We propose the concept of a model-connected safety case that could simplify certification of complex systems. System design models support the synthesis of both the structure of the safety case and the evidence that supports this structure. The resultant safety case argues that all hazards are adequately addressed through meeting the system safety requirements. This overarching claim is demonstrated via satisfaction of the integrity requirements that are assigned to subsystems and components of the system through a sound process of model-based allocation that respects the system design and follows industry standards. The safety evidence that substantiates claims is supported by evidence which is also auto-constructed from the system model. As the system model evolves during design, the corresponding model-connected safety case can be auto-updated. The approach is underpinned by a data model that connects safety argumentation and safety analysis artefacts, and is facilitated by a software tool.
Citation
Retouniotis, A., Papadopoulos, Y., Sorokos, I., Parker, D., Matragkas, N., & Sharvia, S. (2017). Model-connected safety cases. Lecture notes in computer science, 10437 LNCS, 50-63. https://doi.org/10.1007/978-3-319-64119-5_4
Journal Article Type | Conference Paper |
---|---|
Acceptance Date | Aug 2, 2016 |
Online Publication Date | Aug 2, 2017 |
Publication Date | Aug 1, 2017 |
Deposit Date | Feb 6, 2018 |
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 | 10437 LNCS |
Pages | 50-63 |
ISBN | 9783319641188 |
DOI | https://doi.org/10.1007/978-3-319-64119-5_4 |
Public URL | https://hull-repository.worktribe.com/output/584451 |
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