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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Learning Universally Quantified Invariants of Linear Data Structures
verfasst von : Pranav Garg, Christof Löding, P. Madhusudan, Daniel Neider
Erschienen in: Computer Aided Verification
Verlag: Springer Berlin Heidelberg
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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.