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Incorporating software failure in risk analysis––Part 2: Risk modeling process and case study

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  • Thieme, Christoph A.
  • Mosleh, Ali
  • Utne, Ingrid B.
  • Hegde, Jeevith

Abstract

The advent of autonomous cars, drones, and ships, the complexity of these systems is increasing, challenging risk analysis and risk mitigation, since the incorporation of software failures intro traditional risk analysis currently is difficult. Current methods that attempt software risk analysis, consider the interaction with hardware and software only superficially. These methods are often inconsistent regarding the level of analysis and cover often only selected software failures. This paper is a follow-up article of Thieme et al. [1] and presents a process for the analysis of functional software failures, their propagation, and incorporation of the results in traditional risk analysis methods, such as fault trees, and event trees. A functional view on software is taken, that allows for integration of software failure modes into risk analysis of the events and effects, and a common foundation for communication between risk analysts and domain experts. The proposed process can be applied during system development and operation in order to analyses the risk level and identify measures for system improvement. A case study focusing on a decision support system for an autonomous remotely operated vehicle working on a subsea oil and gas production system demonstrates the applicability of the proposed process.

Suggested Citation

  • Thieme, Christoph A. & Mosleh, Ali & Utne, Ingrid B. & Hegde, Jeevith, 2020. "Incorporating software failure in risk analysis––Part 2: Risk modeling process and case study," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s0951832018307178
    DOI: 10.1016/j.ress.2020.106804
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    References listed on IDEAS

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    1. Zhu, Dongfeng & Mosleh, Ali & Smidts, Carol, 2007. "A framework to integrate software behavior into dynamic probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1733-1755.
    2. Chris Garrett & George Apostolakis, 1999. "Context in the Risk Assessment of Digital Systems," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 23-32, February.
    3. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
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    1. de Araujo, Matheus Soares & da Silva, Leandro Dias & Sobrinho, Ã lvaro & Cunha, Paulo & Montecchi, Leonardo, 2022. "Reliability analysis of multi-parameter monitoring systems for Intensive Care Units," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Zhang, Yan & Wang, Yu-Hao & Zhao, Xu & Tong, Rui-Peng, 2023. "Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario," Resources Policy, Elsevier, vol. 85(PB).
    3. Li, Xue & Oh, Poong & Zhou, Yusheng & Yuen, Kum Fai, 2023. "Operational risk identification of maritime surface autonomous ship: A network analysis approach," Transport Policy, Elsevier, vol. 130(C), pages 1-14.
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    5. Thieme, Christoph A. & Mosleh, Ali & Utne, Ingrid B. & Hegde, Jeevith, 2020. "Incorporating software failure in risk analysis – Part 1: Software functional failure mode classification," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

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