A Framework and DataSet for Bugs in Ethereum Smart Contracts
This work addresses a critical problem for Ethereum smart contract developers by providing a framework and dataset to improve bug detection and tool development, though it is incremental as it builds on existing standards and tools.
The authors tackled the lack of a comprehensive classification framework and dataset for bugs in Ethereum smart contracts by collecting bugs from multiple sources, categorizing them into 9 types, and creating a dataset covering all bug types, which they used to evaluate state-of-the-art tools and found that Mythril, Slither, and Remix are the most effective combination, but 10 bug types remain undetectable.
Ethereum is the largest blockchain platform that supports smart contracts. Users deploy smart contracts by publishing the smart contract's bytecode to the blockchain. Since the data in the blockchain cannot be modified, even if these contracts contain bugs, it is not possible to patch deployed smart contracts with code updates. Moreover, there is currently neither a comprehensive classification framework for Ethereum smart contract bugs, nor detailed criteria for detecting bugs in smart contracts, making it difficult for developers to fully understand the negative effects of bugs and design new approaches to detect bugs. In this paper, to fill the gap, we first collect as many smart contract bugs as possible from multiple sources and divide these bugs into 9 categories by extending the IEEE Standard Classification for Software Anomalies. Then, we design the criteria for detecting each kind of bugs, and construct a dataset of smart contracts covering all kinds of bugs. With our framework and dataset, developers can learn smart contract bugs and develop new tools to detect and locate bugs in smart contracts. Moreover, we evaluate the state-of-the-art tools for smart contract analysis with our dataset and obtain some interesting findings: 1) Mythril, Slither and Remix are the most worthwhile combination of analysis tools. 2) There are still 10 kinds of bugs that cannot be detected by any analysis tool.