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Exploiting resolution proofs to speed up LTL vacuity detection for BMC

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Abstract

When model-checking reports that a property holds on a model, vacuity detection increases user confidence in this result by checking that the property is satisfied in the intended way. While vacuity detection is effective, it is a relatively expensive technique requiring many additional model-checking runs. We address the problem of efficient vacuity detection for Bounded Model Checking (BMC) of linear temporal logic properties, presenting three partial vacuity detection methods based on the efficient analysis of the resolution proof produced by a successful BMC run. In particular, we define a characteristic of resolution proofs— peripherality—and prove that if a variable is a source of vacuity, then there exists a resolution proof in which this variable is peripheral. Our vacuity detection tool, VaqTree, uses these methods to detect vacuous variables, decreasing the total number of model-checking runs required to detect all sources of vacuity.

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Authors and Affiliations

  1. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    Jocelyn Simmonds, Jessica Davies & Marsha Chechik

  2. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Arie Gurfinkel

Authors
  1. Jocelyn Simmonds
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  2. Jessica Davies
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  3. Arie Gurfinkel
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  4. Marsha Chechik
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Correspondence to Jocelyn Simmonds.

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Simmonds, J., Davies, J., Gurfinkel, A. et al. Exploiting resolution proofs to speed up LTL vacuity detection for BMC. Int J Softw Tools Technol Transfer 12, 319–335 (2010). https://doi.org/10.1007/s10009-009-0134-1

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