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International SPIN Workshop on Model Checking of Software

SPIN 2015: Model Checking Software pp 20–38Cite as

Refinement Selection

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9232))

Abstract

Counterexample-guided abstraction refinement (CEGAR) is a property-directed approach for the automatic construction of an abstract model for a given system. The approach learns information from infeasible error paths in order to refine the abstract model. We address the problem of selecting which information to learn from a given infeasible error path. In previous work, we presented a method that enables refinement selection by extracting a set of sliced prefixes from a given infeasible error path, each of which represents a different reason for infeasibility of the error path and thus, a possible way to refine the abstract model. In this work, we (1) define and investigate several promising heuristics for selecting an appropriate precision for refinement, and (2) propose a new combination of a value analysis and a predicate analysis that does not only find out which information to learn from an infeasible error path, but automatically decides which analysis should be preferred for a refinement. These contributions allow a more systematic refinement strategy for CEGAR-based analyses. We evaluated the idea on software verification. We provide an implementation of the new concepts in the verification framework

and make it publicly available. In a thorough experimental study, we show that refinement selection often avoids state-space explosion where existing approaches diverge, and that it can be even more powerful if applied on a higher level, where it decides which analysis of a combination should be favored for a refinement.

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Notes

  1. 1.

    In the programming language C, a boolean variable is modeled by an integer variable b for which b==0 represents the value \( false \) and b!=0 represents the value \( true \) (cf. [2] for a discussion of more fine-grained types for C).

  2. 2.

    Results available at http://www.rers-challenge.org/2014Isola/

  3. 3.

    Available under the Apache 2.0 License from http://cpachecker.sosy-lab.org/

  4. 4.

    http://www.sosy-lab.org/~dbeyer/cpa-ref-sel/

  5. 5.

    We do not expect the precision with a bad domain-type score to be actually useful, we report its results merely for comparison.

  6. 6.

    Experiments showed no relevant difference between selecting the shortest or the longest infeasible sliced path in case of a tie in the primary selection heuristic.

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Authors and Affiliations

  1. University of Passau, Passau, Germany

    Dirk Beyer, Stefan Löwe & Philipp Wendler

Authors
  1. Dirk Beyer
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  2. Stefan Löwe
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  3. Philipp Wendler
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Correspondence to Dirk Beyer .

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Editors and Affiliations

  1. Stellenbosch University, Matieland, South Africa

    Bernd Fischer

  2. Stellenbosch University, Matieland, South Africa

    Jaco Geldenhuys

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Beyer, D., Löwe, S., Wendler, P. (2015). Refinement Selection. In: Fischer, B., Geldenhuys, J. (eds) Model Checking Software. SPIN 2015. Lecture Notes in Computer Science(), vol 9232. Springer, Cham. https://doi.org/10.1007/978-3-319-23404-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-23404-5_3

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  • Online ISBN: 978-3-319-23404-5

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