Quantifying Uncertainty in Safety Cases Using Evidential Reasoning

摘要

Dealing with uncertainty is an important and difficult aspect of analyses and assessment of complex systems. A real-time large-scale complex critical system involves many uncertainties, and assessing probabilities to represent these uncertainties is itself a complex task. Currently, the certainty with which safety requirements are satisfied and the consideration of the other confidence factors often remains implicit in the assessment process. Many publications in the past have detailed the structure and content of safety cases and Goal Structured Notation (GSN). This paper does not intend to repeat them. Instead, this paper outlines a novel solution to accommodate uncertainty in the safety cases development and assessment using the Evidential-Reasoning approach - a mathematical technique for reasoning about uncertainty and evidence. The proposed solution is a bottom-up approach that first performs low-level evidence assessments that makes any uncertainty explicit, and then automatically propagates this confidence up to the higher-level claims. The solution would enable safety assessors and managers to accurately summarise their judgement and make doubt or ignorance explicit.