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There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards described in natural languages. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors systematically injected by us to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing. 

The execution of smart contracts on Ethereum, a public blockchain system, incurs a fee called gas fee for its computation and data storage. When programmers develop smart contracts (e.g., in the Solidity programming language), they could unknowingly write code snippets that unnecessarily cause more gas fees. These issues, or what we call gas wastes, can lead to significant monetary losses for users. This paper takes the initiative in helping Ethereum users reduce their gas fees in two key steps. First, we conduct an empirical study on gas wastes in open-source Solidity programs and Ethereum transaction traces. Second, to validate our study findings, we develop a static tool called PeCatch to effectively detect gas wastes in Solidity programs, and manually examine the Solidity compiler’s code to pinpoint implementation errors causing gas wastes. Overall, we make 11 insights and four suggestions, which can foster future tool development and programmer awareness, and fixing our detected bugs can save $0.76 million in gas fees daily.