A Unified Framework to Learn Program Semantics with Graph Neural Networks

Abstract

Program semantics learning is a vital problem in various AI for SE applications e.g., clone detection, code summarization. Learning to represent programs with Graph Neural Networks (GNNs) has achieved state-of-the-art performance in many applications e.g., vulnerability identification, type inference. However, currently, there is a lack of a unified framework with GNNs for distinct applications. Furthermore, most existing GNN-based approaches ignore global relations with nodes, limiting the model to learn rich semantics. In this paper, we propose a unified framework to construct two types of graphs to capture rich code semantics for various SE applications.