A Graph Neural Network-based Code Recommendation Method for Smart Contract Development

Xiuwen Tang, Jiazhen Gan, Zigui Jiang
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Abstract

Smart contracts can be considered as a service in the blockchain system and have been applied in many fields, covering financial products, online games, real estate, transportation and logistics. However, smart contract technology is still in its infancy. Development task is facing many difficulties and challenges, thus providing a set of new or improved development aids for the smart contract ecosystem is an urgent problem that needs to be solved. This paper proposes a smart contract code recommendation method based on graph neural network, which aims to facilitate the development of smart contracts and help developers realize smart contracts faster and more securely. Experimental results show that this method is better than the existing model of smart contract code recommendation in terms of accuracy.
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基于图神经网络的智能合约开发代码推荐方法
智能合约可以看作是区块链系统中的一项服务,已经在很多领域得到了应用,包括金融产品、网络游戏、房地产、交通物流等。然而,智能合约技术仍处于起步阶段。开发任务面临许多困难和挑战,因此为智能合约生态系统提供一套新的或改进的开发辅助工具是迫切需要解决的问题。本文提出了一种基于图神经网络的智能合约代码推荐方法,旨在促进智能合约的开发,帮助开发者更快、更安全地实现智能合约。实验结果表明,该方法在准确率方面优于现有的智能合约代码推荐模型。
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