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

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)31A_Invited conference paper (refereed items)Yespeer-review

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Detail(s)

Original languageEnglish
Pages1-12
Number of pages12
Publication statusOnline published - 28 Jun 2021

Conference

Title29th IEEE/ACM International Conference on Program Comprehension (ICPC 2021)
LocationVirtual
Period18 - 22 May 2021

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 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.

Research Area(s)

  • Smart Contracts, Code Summarization, Transformer, Graph Convolution, Structure-based Traversal

Citation Format(s)

A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts. / Yang, Zhen; Keung, Jacky; Yu, Xiao; Gu, Xiaodong; Wei, Zhengyuan; Ma, Xiaoxue; Zhang, Miao.

2021. 1-12 29th IEEE/ACM International Conference on Program Comprehension (ICPC 2021), .

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)31A_Invited conference paper (refereed items)Yespeer-review