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Reuse of Verification Results

Conditional Model Checking, Precision Reuse, and Verification Witnesses

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Model Checking Software (SPIN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7976))

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Abstract

Verification is a complex algorithmic task, requiring large amounts of computing resources. One approach to reduce the resource consumption is to reuse information from previous verification runs. This paper gives an overview of three techniques for such information reuse. Conditional model checking outputs a condition that describes the state space that was successfully verified, and accepts as input a condition that instructs the model checker which parts of the system should be verified; thus, later verification runs can use the output condition of previous runs in order to not verify again parts of the state space that were already verified. Precision reuse is a technique to use intermediate results from previous verification runs to accelerate further verification runs of the system; information about the level of abstraction in the abstract model can be reused in later verification runs. Typical model checkers provide an error path through the system as witness for having proved that a system violates a property, and a few model checkers provide some kind of proof certificate as a witness for the correctness of the system; these witnesses should be such that the verifiers can read them and —with less computational effort— (re-) verify that the witness is valid.

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Author information

Authors and Affiliations

  1. University of Passau, Germany

    Dirk Beyer & Philipp Wendler

Authors
  1. Dirk Beyer
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  2. Philipp Wendler
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Editors and Affiliations

  1. Department of Computer Engineering, Vienna, University of Technology, Austria

    Ezio Bartocci

  2. Department of Computer Science, Stony Brook University, 11794-4400, Stony Brook, NY, USA

    C. R. Ramakrishnan

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Beyer, D., Wendler, P. (2013). Reuse of Verification Results. In: Bartocci, E., Ramakrishnan, C.R. (eds) Model Checking Software. SPIN 2013. Lecture Notes in Computer Science, vol 7976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39176-7_1

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  • DOI: https://doi.org/10.1007/978-3-642-39176-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39175-0

  • Online ISBN: 978-3-642-39176-7

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