Model-guided synthesis of inductive lemmas for FOL with least fixpoints

Author:

Murali Adithya1ORCID,Peña Lucas1ORCID,Blanchard Eion1ORCID,Löding Christof2ORCID,Madhusudan P.1ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, USA

2. RWTH Aachen University, Germany

Abstract

Recursively defined linked data structures embedded in a pointer-based heap and their properties are naturally expressed in pure first-order logic with least fixpoint definitions (FO+lfp) with background theories. Such logics, unlike pure first-order logic, do not admit even complete procedures. In this paper, we undertake a novel approach for synthesizing inductive hypotheses to prove validity in this logic. The idea is to utilize several kinds of finite first-order models as counterexamples that capture the non-provability and invalidity of formulas to guide the search for inductive hypotheses. We implement our procedures and evaluate them extensively over theorems involving heap data structures that require inductive proofs and demonstrate the effectiveness of our methodology.

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,Software

Reference61 articles.

1. Search-based program synthesis

2. The SLAM project

3. Kshitij Bansal Sarah M. Loos Markus N. Rabe Christian Szegedy and Stewart Wilcox. 2019. HOList: An Environment for Machine Learning of Higher-Order Theorem Proving. https://doi.org/10.48550/ARXIV.1904.03241 10.48550/ARXIV.1904.03241

4. Kshitij Bansal Sarah M. Loos Markus N. Rabe Christian Szegedy and Stewart Wilcox. 2019. HOList: An Environment for Machine Learning of Higher-Order Theorem Proving. https://doi.org/10.48550/ARXIV.1904.03241

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