Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

TL;DR

This paper introduces a retrieval-augmented generation approach combined with a hybrid GNN and attention-based dynamic graph to improve code summarization, achieving state-of-the-art results on a large-scale C code dataset.

Contribution

It proposes a novel retrieval-augmented mechanism and a hybrid GNN with dynamic graph attention to enhance code summarization performance.

Findings

Achieves state-of-the-art BLEU-4, ROUGE-L, and METEOR scores.

Introduces a new large-scale C code benchmark dataset.

Demonstrates significant improvements over existing methods.

Abstract

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR.