Skip to Main content Skip to Navigation
Journal articles

Causality analysis and fault ascription in component-based systems

Gregor Gössler 1 Jean-Bernard Stefani 1
1 SPADES - Sound Programming of Adaptive Dependable Embedded Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This article introduces a general framework for fault ascription, which consists in identifying, within a multi-component system, the components whose faulty behavior has caused the failure of said system. Our framework uses configuration structures as a general semantical model to handle truly concurrent executions, partial and distributed observations in a uniform way. As a first contribution, and in contrast with most of the current literature on counterfactual analysis which relies heavily on a set of toy examples, we first define a set of expected formal properties for counterfactual builders, i.e. operators that build counterfactual executions. We then show that causality analyses that satisfy our requirements meet a set of elementary soundness and completeness properties. Finally we present a concrete causality analysis meeting all our requirements, and we show that it behaves well under refinement. We present several examples illustrating various phenomena such as causal over-determination or observational determinism, and we discuss the relationship of our approach with Halpern and Pearl's actual causality analysis. This article is the published version of https://hal.inria.fr/hal-02161534.
Complete list of metadatas

https://hal.inria.fr/hal-02927216
Contributor : Gregor Gössler <>
Submitted on : Tuesday, September 1, 2020 - 2:55:34 PM
Last modification on : Friday, January 8, 2021 - 11:22:06 AM

Links full text

Identifiers

Collections

Citation

Gregor Gössler, Jean-Bernard Stefani. Causality analysis and fault ascription in component-based systems. Theoretical Computer Science, Elsevier, 2020, 837, pp.158-180. ⟨10.1016/j.tcs.2020.06.010⟩. ⟨hal-02927216⟩

Share

Metrics

Record views

28