A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts

TL;DR

This paper introduces MMTrans, a multi-modal Transformer-based approach for automatic code summarization in smart contracts, leveraging AST structures to generate high-quality comments and improve understanding of blockchain programs.

Contribution

The paper presents a novel multi-modal Transformer model that combines AST sequences and graphs for better code comment generation in smart contracts.

Findings

MMTrans outperforms existing baselines on a large dataset.

It effectively captures both global and local semantic information.

Generated comments are of higher quality and more accurate.

Abstract

Code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code. However, high-quality code comments are often unavailable in smart contracts, the increasingly popular programs that run on the blockchain. In this paper, we propose a Multi-Modal Transformer-based (MMTrans) code summarization approach for smart contracts. Specifically, the MMTrans learns the representation of source code from the two heterogeneous modalities of the Abstract Syntax Tree (AST), i.e., Structure-based Traversal (SBT) sequences and graphs. The SBT sequence provides the global semantic information of AST, while the graph convolution focuses on the local details. The MMTrans uses two encoders to extract both global and local semantic information from the two modalities respectively, and then uses a joint decoder to generate code comments. Both the encoders and the decoder employ the multi-head attention structure of the Transformer to enhance the ability to capture the long-range dependencies between code tokens. We build a dataset with over 300K <method, comment> pairs of smart contracts, and evaluate the MMTrans on it. The experimental results demonstrate that the MMTrans outperforms the state-of-the-art baselines in terms of four evaluation metrics by a substantial margin, and can generate higher quality comments.