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Incremental Invariant Generation Using Logic-Based Automatic Abstract Transformers

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NASA Formal Methods (NFM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7871))

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Abstract

Formal analysis tools for system models often require or benefit from the availability of auxiliary system invariants. Abstract interpretation is currently one of the best approaches for discovering useful invariants, in particular numerical ones. However, its application is limited by two orthogonal issues: (i) developing an abstract interpretation is often non-trivial; each transfer function of the system has to be represented at the abstract level, depending on the abstract domain used; (ii) with precise but costly abstract domains, the information computed by the abstract interpreter can be used only once a post fix point has been reached; this may take a long time for large systems or when widening is delayed to improve precision. We propose a new, completely automatic, method to build abstract interpreters which, in addition, can provide sound invariants of the system under analysis before reaching the end of the post fix point computation. In effect, such interpreters act as on-the-fly invariant generators and can be used by other tools such as logic-based model checkers. We present some experimental results that provide initial evidence of the practical usefulness of our method.

Work supported by AFOSR grant #AF9550-09-1-0517, FNRAE Cavale project and ANR INS Project CAFEIN, with the support of the Aerospace Space cluster.

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Author information

Authors and Affiliations

  1. the French Aerospace Lab, Onera, France

    Pierre-Loïc Garoche

  2. The University of Iowa, USA

    Pierre-Loïc Garoche, Temesghen Kahsai & Cesare Tinelli

Authors
  1. Pierre-Loïc Garoche
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  2. Temesghen Kahsai
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  3. Cesare Tinelli
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Guillaume Brat

  2. Stinger Ghaffarian Technologies Inc., NASA Ames Research Center, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Neha Rungta

  3. NASA Ames Research Center, Carnegie Mellon University, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Arnaud Venet

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Garoche, PL., Kahsai, T., Tinelli, C. (2013). Incremental Invariant Generation Using Logic-Based Automatic Abstract Transformers. In: Brat, G., Rungta, N., Venet, A. (eds) NASA Formal Methods. NFM 2013. Lecture Notes in Computer Science, vol 7871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38088-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-38088-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38087-7

  • Online ISBN: 978-3-642-38088-4

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