A Hybrid Approach to Causality Analysis

Shaohui Wang 1 Yoann Geoffroy 2 Gregor Gössler 2 Oleg Sokolsky 1 Insup Lee 1
1 CIS - Department of Computer and Information Science [Pennsylvania]
2 SPADES - Sound Programming of Adaptive Dependable Embedded Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In component-based safety-critical systems, when a system safety property is violated, it is necessary to analyze which components are the cause. Given a system execution trace that exhibits component faults leading to a property violation, our causality analysis formalizes a notion of counterfactual reasoning ("what would the system behavior be if a component had been correct?") and algorithmically derives such alternative system behaviors, without re-executing the system itself. In this paper, we show that we can improve precision of the analysis if 1) we can emulate execution of components instead of relying on their contracts, and 2) take into consideration input/output dependencies between components to avoid blaming components for faults induced by other components. We demonstrate the utility of the extended analysis with a case study for a closed-loop patient-controlled analgesia system.
Type de document :
Communication dans un congrès
RV 2015 - 6th International Conference on Runtime Verification, Sep 2015, Vienna, Austria. 9333, 2015, LNCS. 〈10.1007/978-3-319-23820-3_16〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01211607
Contributeur : Gregor Gössler <>
Soumis le : lundi 5 octobre 2015 - 14:20:41
Dernière modification le : jeudi 11 octobre 2018 - 08:48:04

Lien texte intégral

openaccess

Identifiants

Collections

INRIA | LIG_SRCPR | UGA | LIG-MFML-SPADES | LIG

Citation

Shaohui Wang, Yoann Geoffroy, Gregor Gössler, Oleg Sokolsky, Insup Lee. A Hybrid Approach to Causality Analysis. RV 2015 - 6th International Conference on Runtime Verification, Sep 2015, Vienna, Austria. 9333, 2015, LNCS. 〈10.1007/978-3-319-23820-3_16〉. 〈hal-01211607〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

226

Contact


archive-ouverte@inria.fr