Download PDFOpen PDF in browser

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

Links:
BibTeX entry
@inproceedings{LPAR2023:Model_Checking_Omega_Regular_Hyperproperties,
  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},
  pages     = {23--35},
  year      = {2023},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/d1VW},
  doi       = {10.29007/1xjt}}
Download PDFOpen PDF in browser