Deriving invariants by algorithmic learning, decision procedures, and predicate abstraction

  • Authors:
  • Yungbum Jung;Soonho Kong;Bow-Yaw Wang;Kwangkeun Yi

  • Affiliations:
  • School of Computer Science and Engineering, Seoul National University;School of Computer Science and Engineering, Seoul National University;Institute of Information Science, Academia Sinica;School of Computer Science and Engineering, Seoul National University

  • Venue:
  • VMCAI'10 Proceedings of the 11th international conference on Verification, Model Checking, and Abstract Interpretation
  • Year:
  • 2010

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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.