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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. 

Go is a young programming language invented to build safe and efficient concurrent programs. It provides goroutines as lightweight threads and channels for inter-goroutine communication. Programmers are encouraged to explicitly pass messages through channels to connect goroutines, with the purpose of reducing the chance of making programming mistakes and introducing concurrency bugs. Go is one of the most beloved programming languages and has already been used to build many critical infrastructure software systems in the data-center environment. However, a recent study shows that channel-related concurrency bugs are still common in Go programs, severely hurting the reliability of the programs. This paper presents GFuzz, a dynamic detector that can effectively pinpoint channel-related concurrency bugs by mutating the processing orders of concurrent messages. We build GFuzz in three steps. We first adopt an effective approach to identify concurrent messages and transform a program to process those messages in any given order. We then take a fuzzing approach to generate new processing orders by mutating exercised ones and rely on execution feedback to prioritize orders close to triggering bugs. Finally, we design a runtime sanitizer to capture triggered bugs that are missed by the Go runtime. We evaluate GFuzz on seven popular Go software systems, including Docker, Kubernetes, and gRPC. GFuzz finds 184 previously unknown bugs and reports a negligible number of false positives. Programmers have already confirmed 124 reports as real bugs and fixed 67 of them based on our reporting. A careful inspection of the detected concurrency bugs from gRPC shows the effectiveness of each component of GFuzz and confirms the components' rationality.