##基于图神经网络的程序错误诊断与修复

Abstract

This paper proposes a novel approach to program error diagnosis and repair leveraging Graph Neural Networks (GNNs). Traditional methods for software defect detection and correction often rely on manual inspection or rule-based systems, which can be time-consuming, expensive, and prone to human error. Our approach transforms program code into a graph structure, representing dependencies between code elements. A GNN is then employed to learn these dependencies and identify patterns associated with errors. This learned representation enables automated diagnosis of errors, and crucially, the generation of potential repair code. We demonstrate the feasibility and effectiveness of this method through a conceptual framework and illustrate the potential benefits of automating this critical aspect of software development. The core claim of this work is the utilization of GNNs to analyze program code dependencies for automated error diagnosis and repair. The core mechanism involves representing code as a graph, utilizing GNNs to learn dependencies and error patterns, and generating repair code.