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

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 80723 different pairs of<comment,code snippet>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 finetuned 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 finetuned CodeBERT model has superior performance in terms of Recall@k and MRR.These findings highlight the effectiveness of our finetuning approach and its potential to significantly improve the code search accuracy.