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Automatic symbolic compositional verification by learning assumptions

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Abstract

Compositional reasoning aims to improve scalability of verification tools by reducing the original verification task into subproblems. The simplification is typically based on assume-guarantee reasoning principles, and requires user guidance to identify appropriate assumptions for components. In this paper, we propose a fully automated approach to compositional reasoning that consists of automated decomposition using a hypergraph partitioning algorithm for balanced clustering of variables, and discovering assumptions using the L * algorithm for active learning of regular languages. We present a symbolic implementation of the learning algorithm, and incorporate it in the model checker NuSmv. In some cases, our experiments demonstrate significant savings in the computational requirements of symbolic model checking.

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Authors and Affiliations

  1. Pennsylvania State University, University Park, PA, USA

    Wonhong Nam

  2. University of Illinois, Urbana-Champaign, IL, USA

    P. Madhusudan

  3. University of Pennsylvania, Philadelphia, PA, USA

    Rajeev Alur

Authors
  1. Wonhong Nam
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  2. P. Madhusudan
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  3. Rajeev Alur
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Correspondence to Wonhong Nam.

Additional information

This research was partially supported by ARO grant DAAD19-01-1-0473, and NSF grants ITR/SY 0121431 and CCR0306382.

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Nam, W., Madhusudan, P. & Alur, R. Automatic symbolic compositional verification by learning assumptions. Form Methods Syst Des 32, 207–234 (2008). https://doi.org/10.1007/s10703-008-0055-8

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