| Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts |
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| 作者姓名： | 孔雀屏 王子彦 黄袁 陈湘萍 周晓聪 郑子彬 黄罡 |
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| 作者单位： | School of Computer Science and Engineering;School of Software Engineering;Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion;Peking University Shenzhen Graduate School |
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| 基金项目： | supported by the Key-Area Research and Development Program of Guangdong Province of China under Grant No.2020B010164002;the National Natural Science Foundation of China under Grant No.62032025. |
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| 摘 要： | Ethereum blockchain is a new internetware with tens of millions of smart contracts running on it.Different from general programs,smart contracts are decentralized,tamper-resistant and permanently running.Moreover,to avoid resource abuse,Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by contracts.It is necessary to optimize smart contracts to save money.However,since developers are not familiar with the operating environment of smart contracts(i.e.,Ethereum virtual machine)or do not pay attention to resource consumption during development,there are many optimization opportunities for smart contracts.To fill this gap,this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is optimized.To evaluate the prevalence and economic benefits of gas-inefficient patterns,this paper conducts an empirical study on more than 160,000 real smart contracts.The promising experimental results demonstrate that 52.75%of contracts contain at least one gas-inefficient pattern proposed in this paper.If these patterns are removed from the contract,at least 0.30 can be saved per contract.
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| 关 键 词： | smart contract ANTI-PATTERN detection optimization empirical study |
| 收稿时间： | 2021-06-02 |
Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts |
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| Que-Ping Kong,Zi-Yan Wang,Yuan Huang,Xiang-Ping Chen,Xiao-Cong Zhou,Zi-Bin Zheng,Gang Huang.Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts[J].Journal of Computer Science and Technology,2022,37(1):67-82. |
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| Authors: | Que-Ping Kong Zi-Yan Wang Yuan Huang Xiang-Ping Chen Xiao-Cong Zhou Zi-Bin Zheng Gang Huang |
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| Affiliation: | 1.School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China;2.School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China;3.Guangdong
Key Laboratory for Big Data Analysis and Simulation of Public Opinion,
School of Communication and Design, Sun Yat-sen University, Guangzhou
510006, China;4.Peking University Shenzhen Graduate School, Shenzhen 518000, China |
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| Abstract: | Ethereum blockchain is a new internetware with tens of millions of smart contracts running on it. Different from general programs, smart contracts are decentralized, tamper-resistant and permanently running. Moreover, to avoid resource abuse, Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by contracts. It is necessary to optimize smart contracts to save money. However, since developers are not familiar with the operating environment of smart contracts (i.e., Ethereum virtual machine) or do not pay attention to resource consumption during development, there are many optimization opportunities for smart contracts. To fill this gap, this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is optimized. To evaluate the prevalence and economic benefits of gas-inefficient patterns, this paper conducts an empirical study on more than 160,000 real smart contracts. The promising experimental results demonstrate that 52.75% of contracts contain at least one gas-inefficient pattern proposed in this paper. If these patterns are removed from the contract, at least 0.30 can be saved per contract. |
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| Keywords: | smart contract anti-pattern detection optimization empirical study |
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