Deep Graph-Language Fusion for Structure-Aware Code Generation

TL;DR

This paper introduces CGFuse, a framework that integrates graph-based code representations into pre-trained language models at the token level, improving code generation by explicitly capturing code structure.

Contribution

It systematically explores deep, token-level fusion of graph features within PLMs, a novel approach that enhances structural awareness in code generation.

Findings

Up to 16% BLEU improvement in code generation.

Up to 11% CodeBLEU improvement.

Effective integration of graph features at token level.

Abstract

Pre-trained Language Models (PLMs) have the potential to transform software development tasks. However, despite significant advances, current PLMs struggle to capture the structured and relational attributes of code, such as control flow and data dependencies. This limitation is rooted in an architectural mismatch: whereas code structure is best represented by graphs, transformer-based LLMs process input as sequential token patterns and therefore lack explicit structural awareness. While recent research has explored integrating graph-based code representations using techniques like graph feature extraction, retrieval-augmented generation, and prompt engineering, existing approaches suffer from information loss during dense feature extraction or prompt encoding; notably, the potential of deep, token-level fusion of graph features within model internals has not been systematically explored. In this paper, we initiate such an exploration by introducing CGFuse, a novel framework that enables token-level integration of graph-derived representations by infusing learned graph features directly into the intermediate layers of pre-trained language models. CGFuse combines a graph neural network (GNN) with a language model to explicitly preserve and exploit fine-grained structural information from code graphs, including abstract syntax trees and data-flow graphs. We systematically evaluate CGFuse across multiple LLMs, demonstrating up to 10-16% BLEU and 6-11% CodeBLEU improvements in code generation performance. These results highlight the potential of deep graph-PLM integration to advance the field toward more robust, capable AI-driven software development.