Smart Contract Code Repair Recommendation based on Reinforcement Learning and Multi-metric Optimization.
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| Title: | Smart Contract Code Repair Recommendation based on Reinforcement Learning and Multi-metric Optimization. |
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| Authors: | Guo, Hanyang, Chen, Yingye, Chen, Xiangping, Huang, Yuan, Zheng, Zibin |
| Source: | ACM Transactions on Software Engineering & Methodology; May2024, Vol. 33 Issue 4, p1-31, 31p |
| Subject Terms: | HEURISTIC algorithms, SEARCH algorithms, CONTRACTS, PROGRAMMING languages, HEURISTIC, REINFORCEMENT learning, BLOCKCHAINS |
| Abstract: | A smart contract is a kind of code deployed on the blockchain that executes automatically once an event triggers a clause in the contract. Since smart contracts involve businesses such as asset transfer, they are more vulnerable to attacks, so it is crucial to ensure the security of smart contracts. Because a smart contract cannot be tampered with once deployed on the blockchain, for smart contract developers, it is necessary to fix vulnerabilities before deployment. Compared with many vulnerability detection tools for smart contracts, the amount of automatic fix approaches for smart contracts is relatively limited. These approaches mainly use defined pattern-based methods or heuristic search algorithms for vulnerability repairs. In this article, we propose RLRep, a reinforcement learning-based approach to provide smart contract repair recommendations for smart contract developers automatically. This approach adopts an agent to provide repair action suggestions based on the vulnerable smart contract without any supervision, which can solve the problem of missing labeled data in machine learning-based repair methods. We evaluate our approach on a dataset containing 853 smart contract programs (programming language: Solidity) with different kinds of vulnerabilities. We split them into training and test sets. The result shows that our approach can provide 54.97% correct repair recommendations for smart contracts. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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