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The previous smart contract code comment (SCC) generation approaches can bedivided into two categories: fine-tuning paradigm-based approaches andinformation retrieval-based approaches. However, for the fine-tuningparadigm-based approaches, the performance may be limited by the quality of thegathered dataset for the downstream task and they may have knowledge-forgettingissues. While for the information retrieval-based approaches, it is difficultfor them to generate high-quality comments if similar code does not exist inthe historical repository. Therefore we want to utilize the domain knowledgerelated to SCC generation in large language models (LLMs) to alleviate thedisadvantages of these two types of approaches. In this study, we propose anapproach SCCLLM based on LLMs and in-context learning. Specifically, in thedemonstration selection phase, SCCLLM retrieves the top-k code snippets fromthe historical corpus by considering syntax, semantics, and lexicalinformation. In the in-context learning phase, SCCLLM utilizes the retrievedcode snippets as demonstrations, which can help to utilize the relatedknowledge for this task. We select a large corpus from a smart contractcommunity Etherscan.io as our experimental subject. Extensive experimentalresults show the effectiveness of SCCLLM when compared with baselines inautomatic evaluation and human evaluation.

Automatic Smart Contract Comment Generation via Large Language Models and In-Context Learning