Improving Gas Efficiency in Smart Contracts: Data-Driven Insights and LLM-Assisted Remediation

Abstract

Smart contracts, primarily written in Solidity, are Turing-complete programs on platforms like Ethereum, requiring gas fees for deployment and execution. Gas quantifies computational costs, and inefficient contracts result in unnecessary expenses for developers and users. Gas optimization at the source code level has been studied in various related works; however, existing methods for summarizing gas-inefficient patterns primarily rely on author-defined rules or heuristic approaches, and their evaluations lack a labeled dataset.In this paper, we conduct a comprehensive empirical study on the issue of gas optimization in smart contracts. We begin by gathering audit reports from Code4rena, a well-known smart contract audit platform. These reports include both expert evaluations, conducted by professionals known as Wardens, and automated analyses generated by the platform’s static analysis tool, 4naly3er. After filtering out false-positive gas optimization instances from the automated reports, we identify 2,095 instances of gas-inefficient patterns across 54 projects. We categorize these inefficiencies into 24 types using thematic analysis and find that static analysis tools often produce false positives and negatives. To address this, we propose a hybrid method combining static analysis and large language models (LLMs) to detect and repair gas inefficiencies. The static analysis tool identifies potential optimization opportunities, while the LLM refines these findings and suggests effective repairs. Our evaluation shows that our approach achieves a precision rate of 82.28% and a recall rate of 88.46%, and can save 919 units of gas per function on average during execution.