Automating Comment Generation for Smart Contract from Bytecode

• URL: http://arxiv.org/abs/2503.15270v1
• Date: Wed, 19 Mar 2025 14:45:40 GMT
• Title: Automating Comment Generation for Smart Contract from Bytecode
• Authors: Jianhang Xiang, Zhipeng Gao, Lingfeng Bao, Xing Hu, Jiayuan Chen, Xin Xia,
• Abstract summary: In practice, only 13% of smart contracts deployed on the component to the blockchain are associated with source code.<n>We propose SmartBT (Smart contract Bytecode Translator) for automatically translating smart contract bytecode into fine-grained natural language description.
• Score: 11.143538294203026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, smart contracts have played a vital role in automatic financial and business transactions. To help end users without programming background to better understand the logic of smart contracts, previous studies have proposed models for automatically translating smart contract source code into their corresponding code summaries. However, in practice, only 13% of smart contracts deployed on the Ethereum blockchain are associated with source code. The practical usage of these existing tools is significantly restricted. Considering that bytecode is always necessary when deploying smart contracts, in this paper, we first introduce the task of automatically generating smart contract code summaries from bytecode. We propose a novel approach, named SmartBT (Smart contract Bytecode Translator) for automatically translating smart contract bytecode into fine-grained natural language description directly. Two key challenges are posed for this task: structural code logic hidden in bytecode and the huge semantic gap between bytecode and natural language descriptions. To address the first challenge, we transform bytecode into CFG (Control-Flow Graph) to learn code structural and logic details. Regarding the second challenge, we introduce an information retrieval component to fetch similar comments for filling the semantic gap. Then the structural input and semantic input are used to build an attentional sequence-to-sequence neural network model. The copy mechanism is employed to copy rare words directly from similar comments and the coverage mechanism is employed to eliminate repetitive outputs. The automatic evaluation results show that SmartBT outperforms a set of baselines by a large margin, and the human evaluation results show the effectiveness and potential of SmartBT in producing meaningful and accurate comments for smart contract code from bytecode directly.

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