A Language-Based Causal Model for Safety

Marcello Bonsangue, Georgiana Caltais*, Hui Feng, Hünkar Can Tunç

*Corresponding author for this work

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

Inspired by the seminal works on causal analysis by Halpern and Pearl, in this paper we introduce a causal model based on counterfactuals, adapted to finite automata models and with safety properties defined by regular expressions. The latter encode undesired execution traces. We devise a framework that computes actual causes, or minimal traces that lead to states enabling hazardous behaviours. Furthermore, our framework exploits counterfactual information and identifies modalites to steer causal executions towards alternative safe ones. This can provide systems engineers with valuable data for actual debugging and fixing erroneous behaviours. Our framework employs standard algorithms from automata theory, thus paving the way to further generalizations from finite automata to richer structures like probabilistic or KAT automata.

Original languageEnglish
Title of host publicationTheoretical Aspects of Software Engineering - 16th International Symposium, TASE 2022, Proceedings
EditorsYamine Aït-Ameur, Florin Craciun
Number of pages18
Place of publicationCham
PublisherSpringer
Publication date2022
Pages290-307
ISBN (Print)978-3-031-10362-9
ISBN (Electronic)978-3-031-10363-6
DOIs
Publication statusPublished - 2022
Event16th International Symposium on Theoretical Aspects of Software Engineering (TASE) - Cluj-Napoca, Romania
Duration: 8 Jul 202210 Jul 2022
Conference number: 16

Conference

Conference16th International Symposium on Theoretical Aspects of Software Engineering (TASE)
Number16
Country/TerritoryRomania
CityCluj-Napoca
Period08/07/202210/07/2022
SeriesLecture Notes in Computer Science
Volume13299
ISSN0302-9743

Keywords

  • Automata
  • Causal models
  • Counterfactuals
  • Regular languages
  • Safety

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