Learning universally quantified invariants of linear data structures

Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationComputer Aided Verification - 25th International Conference, CAV 2013, Proceedings
Pages813-829
Number of pages17
DOIs
StatePublished - Aug 12 2013
Event25th International Conference on Computer Aided Verification, CAV 2013 - Saint Petersburg, Russian Federation
Duration: Jul 13 2013Jul 19 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8044 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on Computer Aided Verification, CAV 2013
CountryRussian Federation
CitySaint Petersburg
Period7/13/137/19/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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