Confidence assessment in safety argument structure - Quantitative vs. qualitative approaches - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Accéder directement au contenu
Article Dans Une Revue International Journal of Approximate Reasoning Année : 2024

Confidence assessment in safety argument structure - Quantitative vs. qualitative approaches

Confidence assessment in safety argument structure - Quantitative vs. qualitative approaches

Résumé

Some safety standards (e.g., ISO 26262 in automotive industry) propose the use of argument structures to justify that the high-level safety properties of a system have been ensured. The goal structuring notation (GSN) is a graphical tool used to represent these argument structures. However, this approach does not address the uncertainties that may affect the validity of the arguments. Thus, some authors proposed to complement GSN patterns with a quantitative confidence assessment procedure. In this paper, we first present a refined procedure that expresses the relation between premises (pieces of evidence) and the conclusion (top-goal to be demonstrated) using logical expressions. Then using Dempster-Shafer theory, we quantify uncertainty on each expression to build an explicit mathematical formula for propagating uncertainty to the conclusion. Inputs for the propagation model are collected from experts and transformed into numerical values using an improved elicitation model. Afterwards, we introduce a purely qualitative alternative to the quantitative procedure based on the theory of qualitative capacities. Finally, we adapt the propagation and elicitation models to this framework.
Fichier principal
Vignette du fichier
IJAR_paper___Yassir (2).pdf (986.58 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04342922 , version 1 (13-12-2023)

Identifiants

Citer

Yassir Idmessaoud, Didier Dubois, Jérémie Guiochet. Confidence assessment in safety argument structure - Quantitative vs. qualitative approaches. International Journal of Approximate Reasoning, 2024, 165, pp.109100. ⟨10.1016/j.ijar.2023.109100⟩. ⟨hal-04342922⟩
137 Consultations
40 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More