A Model for Safety Case Confidence Assessment

TL;DR

This paper introduces a new model for quantitatively estimating confidence in safety cases, combining Belief Theory and Bayesian Networks to improve safety argument assessment.

Contribution

It proposes a novel model that integrates Belief Theory and Bayesian Belief Networks for quantitative confidence estimation in safety cases.

Findings

The model effectively propagates confidence levels in safety argument networks.

It offers a structured approach to quantify safety confidence.

The approach enhances understanding of safety argument robustness.

Abstract

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence estimation based on Belief Theory for its definition, and on Bayesian Belief Networks for its propagation in safety case networks.