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Automatically Inferring Quantified Loop Invariants by Algorithmic Learning from Simple Templates

  • Soonho Kong
  • Yungbum Jung
  • Cristina David
  • Bow-Yaw Wang
  • Kwangkeun Yi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6461)

Abstract

By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invariants. Our technique can find arbitrary first-order invariants (modulo a fixed set of atomic propositions and an underlying SMT solver) in the form of the given template and exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is able to find quantified invariants for loops from the Linux source, as well as for the benchmark code used in the previous works. Our contribution is a simpler technique than the previous works yet with a reasonable derivation power.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Soonho Kong
    • 1
  • Yungbum Jung
    • 1
  • Cristina David
    • 2
  • Bow-Yaw Wang
    • 3
    • 4
    • 5
  • Kwangkeun Yi
    • 1
  1. 1.Seoul National UniversitySouth Korea
  2. 2.National University of SingaporeSingapore
  3. 3.INRIAFrance
  4. 4.Tsinghua UniversityChina
  5. 5.Academia SinicaChina

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