Learning Universally Quantified Invariants of Linear Data Structures

  • Pranav Garg
  • Christof Löding
  • P. Madhusudan
  • Daniel Neider
Conference paper

DOI: 10.1007/978-3-642-39799-8_57

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8044)
Cite this paper as:
Garg P., Löding C., Madhusudan P., Neider D. (2013) Learning Universally Quantified Invariants of Linear Data Structures. In: Sharygina N., Veith H. (eds) Computer Aided Verification. CAV 2013. Lecture Notes in Computer Science, vol 8044. Springer, Berlin, Heidelberg

Abstract

We propose a new automaton model, called quantified data automata over words, that can model quantified invariants over linear data structures, and build poly-time active learning algorithms for them, where the learner is allowed to query the teacher with membership and equivalence queries. In order to express invariants in decidable logics, we invent a decidable subclass of QDAs, called elastic QDAs, and prove that every QDA has a unique minimally-over-approximating elastic QDA. We then give an application of these theoretically sound and efficient active learning algorithms in a passive learning framework and show that we can efficiently learn quantified linear data structure invariants from samples obtained from dynamic runs for a large class of programs.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pranav Garg
    • 1
  • Christof Löding
    • 2
  • P. Madhusudan
    • 1
  • Daniel Neider
    • 2
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.RWTH Aachen UniversityGermany

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