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Model Checking Omega-Regular Hyperproperties with AutoHyperQ

13 pagesPublished: June 3, 2023

Abstract

Hyperproperties are commonly used to define information-flow policies and other re- quirements that reason about the relationship between multiple traces in a system. We consider HyperQPTL – a temporal logic for hyperproperties that combines explicit quan- tification over traces with propositional quantification as, e.g., found in quantified proposi- tional temporal logic (QPTL). HyperQPTL therefore truly captures ω-regular relations on multiple traces within a system. As such, HyperQPTL can, e.g., express promptness prop- erties, which state that there exists a common bound on the number of steps up to which an event must have happened. While HyperQPTL has been studied and used in various prior works, thus far, no model-checking tool for it exists. This paper presents AutoHyperQ, a fully-automatic automata-based model checker for HyperQPTL that can cope with arbitrary combinations of trace and propositional quantification. We evaluate AutoHyperQ on a range of benchmarks and, e.g., use it to analyze promptness requirements in a diverse collection of reactive systems. Moreover, we demonstrate that the core of AutoHyperQ can be reused as an effective tool to translate QPTL formulas into ω-automata.

Keyphrases: automata based, hyperproperties, hyperqptl, model checking, promptness, qptl, verification

In: Ruzica Piskac and Andrei Voronkov (editors). Proceedings of 24th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 94, pages 23-35.

BibTeX entry
@inproceedings{LPAR2023:Model_Checking_Omega_Regular,
  author    = {Raven Beutner and Bernd Finkbeiner},
  title     = {Model Checking Omega-Regular Hyperproperties with AutoHyperQ},
  booktitle = {Proceedings of 24th International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Ruzica Piskac and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {94},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/d1VW},
  doi       = {10.29007/1xjt},
  pages     = {23-35},
  year      = {2023}}
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