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Context in the Risk Assessment of Digital Systems

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  • Chris Garrett
  • George Apostolakis

Abstract

As the use of digital computers for instrumentation and control of safety‐critical systems has increased, there has been a growing debate over the issue of whether probabilistic risk assessment techniques can be applied to these systems. This debate has centered on the issue of whether software failures can be modeled probabilistically. This paper describes a “context‐based” approach to software risk assessment that explicitly recognizes the fact that the behavior of software is not probabilistic. The source of the perceived uncertainty in its behavior results from both the input to the software as well as the application and environment in which the software is operating. Failures occur as the result of encountering some context for which the software was not properly designed, as opposed to the software simply failing “randomly.” The paper elaborates on the concept of “error‐forcing context” as it applies to software. It also illustrates a methodology which utilizes event trees, fault trees, and the Dynamic Flowgraph Methodology (DFM) to identify “error‐forcing contexts” for software in the form of fault tree prime implicants.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:riskan:v:19:y:1999:i:1:p:23-32
    DOI: 10.1111/j.1539-6924.1999.tb00383.x
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    Cited by:

    1. Sedlacek, Peter & Zaitseva, Elena & Levashenko, Vitaly & Kvassay, Miroslav, 2021. "Critical state of non-coherent multi-state system," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Bishop, Peter & Bloomfield, Robin & Littlewood, Bev & Popov, Peter & Povyakalo, Andrey & Strigini, Lorenzo, 2014. "A conservative bound for the probability of failure of a 1-out-of-2 protection system with one hardware-only and one software-based protection train," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 61-68.
    3. James H. Lambert & Rachel K. Jennings & Nilesh N. Joshi, 2006. "Integration of risk identification with business process models," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 187-198, September.
    4. Di Maio, Francesco & Rai, Ajit & Zio, Enrico, 2016. "A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 9-18.
    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).
    6. Francesco Di Maio & Samuele Baronchelli & Enrico Zio, 2015. "A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 142-156, January.
    7. Pietro Turati & Nicola Pedroni & Enrico Zio, 2017. "An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 147-159, January.
    8. Zaitseva, Elena & Levashenko, Vitaly & Sedlacek, Peter & Kvassay, Miroslav & Rabcan, Jan, 2021. "Logical differential calculus for calculation of Birnbaum importance of non-coherent system," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. 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).

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