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A framework to integrate software behavior into dynamic probabilistic risk assessment

Author

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  • Zhu, Dongfeng
  • Mosleh, Ali
  • Smidts, Carol

Abstract

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:12:p:1733-1755
    DOI: 10.1016/j.ress.2006.09.024
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    Citations

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    Cited by:

    1. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Nejad, Hamed S. & Parhizkar, Tarannom & Mosleh, Ali, 2022. "Automatic generation of event sequence diagrams for guiding simulation based dynamic probabilistic risk assessment (SIMPRA) of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. 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).
    4. Y Wei & M Rodriguez & C S Smidts, 2010. "Probabilistic risk assessment framework for software propagation analysis of failures," Journal of Risk and Reliability, , vol. 224(2), pages 113-135, June.
    5. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    6. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. 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).
    8. Brissaud, Florent & Smidts, Carol & Barros, Anne & Bérenguer, Christophe, 2011. "Dynamic reliability of digital-based transmitters," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 793-813.

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