Fine-Tuning Pre-Trained CodeBERT for Code Search in Smart Contract

Abstract

Smart contracts, which automatically execute on decentralized platforms like Ethereum, require high security and low gas consumption. As a result, developers have a strong demand for semantic code search tools that utilize natural language queries to efficiently search for existing code snippets. However, existing code search models face a semantic gap between code and queries, which requires a large amount of training data. In this paper, we propose a fine-tuning approach to bridge the semantic gap in code search and improve the search accuracy. We collect 80 723 different pairs of from Etherscan.io and use these pairs to fine-tune, validate, and test the pre-trained CodeBERT model. Using the fine-tuned model, we develop a code search engine specifically for smart contracts. We evaluate the Recall@k and Mean Reciprocal Rank (MRR) of the fine-tuned CodeBERT model using different proportions of the fine-tuned data. It is encouraging that even a small amount of fine-tuned data can produce satisfactory results. In addition, we perform a comparative analysis between the fine-tuned CodeBERT model and the two state-of-the-art models. The experimental results show that the fine-tuned CodeBERT model has superior performance in terms of Recall@k and MRR. These findings highlight the effectiveness of our fine-tuning approach and its potential to significantly improve the code search accuracy.