OCTAL: Graph Representation Learning for LTL Model Checking

Abstract

Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, suffer from the state space explosion problem due to cross product operations required that make them prohibitively expensive for large-scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a B\"uchi automaton and an LTL formula, respectively. A novel GRL-based framework , is designed to learn the representation of the graph-structured system and specification, which reduces the model checking problem to binary classification. Empirical experiments on two model checking scenarios show that achieves promising accuracy, with up to 11 overall speedup against canonical SOTA model checkers and 31 for satisfiability checking alone.

Tasks

Reproductions