Model-based design of resilient systems using quantitative risk assessment

• Published in: Innovations in systems and software engineering Vol. 20; no. 1; pp. 3 - 16
• Main Authors: Mediouni, Braham Lotfi, Dragomir, Iulia, Nouri, Ayoub, Bensalem, Saddek
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2024; Springer Nature B.V; Springer Verlag
• Subjects: Artificial Intelligence; Computer Applications; Computer Science; Fault detection; Faults; Formal method; Risk assessment; Robotics; S.I. : VECoS 2021; Safety critical; Software Engineering; Statistical models; Subsystems; framework; Model-based design; Risk assessment; Real-time; FDIR; Statistical model-checking; Robotics case study
• ISSN: 1614-5046; 1614-5054
• DOI: 10.1007/s11334-023-00527-0

Fault detection, isolation and recovery subsystems are accepted to make safety-critical systems resilient against faults and failures. Yet, these subsystems should be devised only for those faults that violate the system’s requirements, while providing a correct approach such that requirements are met again. Consequently, the obtained system is minimal, although complete, and robust both with respect to safety and performance requirements. In this paper, we propose a systematic and automated approach based on formal methods that includes (1) the evaluation of the relevance of faults based on quantitative risk assessment, and (2) the validation of system robustness by statistical model checking. We apply this approach on an excerpt of a real-life autonomous robotics case study, and we report on the implementation and results obtained with the S BIP framework.