Learning Universally Quantified Invariants of Linear Data Structures
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.
Cite
@article{arxiv.1302.2273,
title = {Learning Universally Quantified Invariants of Linear Data Structures},
author = {Pranav Garg and Christof Loding and P. Madhusudan and Daniel Neider},
journal= {arXiv preprint arXiv:1302.2273},
year = {2013}
}