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A novel method using LSTM-RNN to generate smart contracts code templates for improved usability

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Abstract

Recently, the development of blockchain technology has given us an opportunity to improve the security and trustworthiness of multimedia. With the applications of blockchain technology, smart contracts have been widely used in many industries. However, the current development of smart contracts faces many challenges. One of the challenges is that the coding process is complicated for developers, leading to unnormalized code and can cause development and maintenance issues. Also, this is an important limitation factor in the development of smart contracts. To solve this problem, this paper proposes a method of generating contract templates based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to simplify the coding process. First, the contracts available online were crawled, before detecting the vulnerabilities of these contracts. Contracts with less vulnerabilities are used as training data. For better generation effects, the Abstract Syntax Tree (AST) and the word2vec are used to extract the lexical unit sequence features to obtain a word vector in order to analyze the semantics of the code. Afterwards, the generated sequence vector features are fed to LSTM-RNN for template generation. The efficiency of four types of vectorization method models were tested and the results showed that the Skip-Gram+ HS used in this paper achieved the highest performance. In addition, a security test was conducted based on the contracts before and after using the contract templates for normalized coding. The results show that the proposed method is not only a beneficial attempt to combine deep learning with blockchain technology, but also provides an effective guidance for improving the security of smart contracts.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62277001, in part by the National Key Technology R&D Program of China under Grant SQ2020YFB10027, ZT2025002 in part by the Scientific Research Program of Beijing Municipal Education Commission under Grant KZ202110011017, in part by the Natural Science Foundation of Beijing Municipality under Grant 9232005, in part by the Beijing Municipal Philosophy and Social Science Foundation under Grant 19GLB036, in part by Major Science and Technology Special Project of Yunnan Province under Grant 202102AD080006, in part by the Open Research Fund of the Beijing Key Laboratory of Big Data Technology for Food Safety (Project No. BTBD-2021KF05), in part by University of Macau (File no. MYRG2019-00006-FST), and in part by the Science and Technology Development Fund of the Macau SAR (0091/2020/A2).

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Authors and Affiliations

  1. PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, 999078, China

    Zhihao Hao & Bob Zhang

  2. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, No. 33, Fucheng Road, Beijing, 100048, China

    Zhihao Hao, Bob Zhang, Dianhui Mao, Min Zuo & Haisheng Li

  3. National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, No. 33, Fucheng Road, Beijing, 100048, China

    Zhihao Hao, Dianhui Mao, Min Zuo & Haisheng Li

  4. China Industrial Control Systems Cyber Emergency Response Team, Beijing, 100040, People’s Republic of China

    Zhihao Hao

  5. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, 999078, China

    Jerome Yen

  6. School of Law, China University of Political Science and Law, No. 25, Xitucheng Road, Beijing, 102249, China

    Zhihua Zhao

  7. State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macau SAR, 999078, China

    Cheng-Zhong Xu

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  1. Zhihao Hao
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Hao, Z., Zhang, B., Mao, D. et al. A novel method using LSTM-RNN to generate smart contracts code templates for improved usability. Multimed Tools Appl 82, 41669–41699 (2023). https://doi.org/10.1007/s11042-023-14592-x

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