A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts
This work addresses a domain-specific problem for developers and maintainers of smart contracts on the blockchain, offering an incremental improvement in code summarization techniques.
The paper tackles the lack of high-quality code comments in smart contracts by proposing a multi-modal Transformer-based approach that learns from both structure-based traversal sequences and graphs of abstract syntax trees, achieving state-of-the-art performance with substantial improvements across four evaluation metrics on a dataset of over 300K method-comment pairs.
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.