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Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction

  • Conference paper
Verification, Model Checking, and Abstract Interpretation (VMCAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5944))

Abstract

By combining algorithmic learning, decision procedures, and predicate abstraction, we present an automated technique for finding loop invariants in propositional formulae. Given invariant approximations derived from pre- and post-conditions, our new technique exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is able to generate invariants for some Linux device drivers and SPEC2000 benchmarks in our experiments.

This work was supported by (A) the Engineering Research Center of Excellence Program of Korea Ministry of Education, Science and Technology(MEST) / Korea Science and Engineering Foundation(KOSEF) Grant Number R11-2008-007-01002-0, (B) the Brain Korea 21 Project, School of Electrical Engineering and Computer Science, Seoul National University, (C) SK Telecom, and (D) National Science Council of Taiwan Grant Numbers 95-2221-E-001-024-MY3 and 97-2221-E-001-006-MY3.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University,  

    Yungbum Jung, Soonho Kong & Kwangkeun Yi

  2. Institute of Information Science, Academia Sinica,  

    Bow-Yaw Wang

Authors
  1. Yungbum Jung
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  2. Soonho Kong
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  3. Bow-Yaw Wang
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  4. Kwangkeun Yi
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Editor information

Editors and Affiliations

  1. Facultad de Informatica (UPM), IMDEA Software, Campus Montegancedo, 28660 Boadilla del Monte, Madrid, Spain

    Gilles Barthe

  2. Facultad de Informatica (UPM), IMDEA Software and Technical University of Madrid, Campus Montegancedo, 28660 Boadilla del Monte, Madrid, Spain

    Manuel Hermenegildo

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Jung, Y., Kong, S., Wang, BY., Yi, K. (2010). Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction. In: Barthe, G., Hermenegildo, M. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2010. Lecture Notes in Computer Science, vol 5944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11319-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-11319-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11318-5

  • Online ISBN: 978-3-642-11319-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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