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Synthesizing History and Prophecy Variables for Symbolic Model Checking

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Verification, Model Checking, and Abstract Interpretation (VMCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13881))

Abstract

Introduction of history and prophecy variables can allow a proof to be expressed in a weaker logic or a more localized form. This fact has been used, for example, to allow purely propositional, quantifier-free, invariant generators to produce proofs for parameterized systems requiring universal quantification in the inductive invariant. However, automatic synthesis of history and prophecy variables remains an open problem. We introduce counterexample-guided heuristics for this purpose based on property-driven refutation of counterexamples and Craig interpolation. The approach is evaluated on a set of benchmarks based on array manipulating programs with multiple loops.

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Notes

  1. 1.

    The code and instructions for reproducing our results is available at https://github.com/cvick32/ConditionalHistory. Including our tool code and the benchmark transformation code there are roughly 3800 SLOC in Python.

  2. 2.

    For a benchmark-by-benchmark comparison of all the tools that were evaluated, see. https://github.com/cvick32/ConditionalHistory/tree/main/paper-results.

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Authors and Affiliations

  1. UT Austin, Austin, USA

    Cole Vick & Kenneth L. McMillan

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  1. Cole Vick
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  2. Kenneth L. McMillan
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Editors and Affiliations

  1. Inria, Amazon Web Services, Courbevoie, France

    Cezara Dragoi

  2. Amazon Web Services, Seattle, WA, USA

    Michael Emmi

  3. University of Southern California, Los Angeles, CA, USA

    Jingbo Wang

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Vick, C., McMillan, K.L. (2023). Synthesizing History and Prophecy Variables for Symbolic Model Checking. In: Dragoi, C., Emmi, M., Wang, J. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2023. Lecture Notes in Computer Science, vol 13881. Springer, Cham. https://doi.org/10.1007/978-3-031-24950-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-24950-1_15

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