Research Output
Automatically Detecting Fallacies in System Safety Arguments
  Safety cases play a significant role in the development of safety-critical systems. The key components in a safety case are safety arguments, that are designated to demonstrate that the system is acceptably safe. Inappropriate reasoning with safety arguments could undermine a system’s safety claims which in turn contribute to safety-related failures of the system. Currently, safety argument reviews are conducted manually, require expensive expertise and are often labor intensive. It would therefore be desirable if software can be employed to help with the detection of flaws in the arguments. A prerequisite for this approach is the need for a formal representation of safety arguments. This paper proposes a predicate logic based representation of safety arguments and a method to detect argument fallacies. It is anticipated that the work contributes to the field of the safety case development as well as to the area of computational fallacies.

  • Type:

    Conference Paper (unpublished)

  • Date:

    26 October 2015

  • Publication Status:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

  • Funders:

    Edinburgh Napier Funded


Wells, S., Yuan, T., Manandhar, S., & Kelly, T. (2015, October). Automatically Detecting Fallacies in System Safety Arguments. Paper presented at 15th International Workshop on Computational Models of Natural Argument(CMNA15)



safety-critical systems, computational fallacies,

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