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Smart contract generation model based on code annotation and AST-LSTM tuning

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Abstract

With the wide application of smart contracts in many fields, the number, types, and complexity of smart contracts are showing a rapidly increasing trend. However, the development of smart contracts has its own unique programming language and security requirements, which are difficult for conventional software personnel to adapt quickly, and how to realize the efficient development of smart contracts according to the application requirements is an important issue that needs to be solved for its further development. The author proposes a smart contract generation method based on abstract syntax tree (AST) long short-term memory (LSTM) characterization and code annotation tuning large language model, which adopts the AST-LSTM model combining abstract syntax tree and tree-long short-term memory(Tree-LSTM) to vectorize the code as well as Sentence-BERT to vectorize the annotations and carry out a weighted analysis, and constructs a smart contract clustering analysis model to achieve accurate clustering of functionally similar smart contracts. Then, the AST-LSTM+Transformer model is used to detect defects in the clustered code and correlate the related annotation information to construct a diverse prompt feature prompt statement dataset. Finally, the Llama2-7B model is used as the basis for demand-specific smart contract generation with the help of Lora and P-Tuning v2 fine-tuning techniques. In this paper, we conducted comparative experiments with existing methods with the help of BLEU, an auxiliary tool for the quality assessment of bilingual translation, and Mythril, VaaS, and other code security detection tools. The results of the experiment show that the average value of BLEU of the code generated by this paper’s method is improved by about 25%, and the code security is improved by about 9%, which will greatly promote the rapid development and exploitation of smart contracts with high-security requirements.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the National Nature Science Foundation of China under Grant 71972102 and the Universities Natural Science Research Project of Jiangsu Province under Grant 20KJA520002.

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Authors and Affiliations

  1. School of Computer Science, Nanjing Audit University, 86 Yushan West Road, Jiangpu Street, Nanjing, 211815, Jiangsu, China

    Chen Yong, Hu Defeng, Xu Chao & Chen Nannan

  2. Wuhan Shubo Technology, Wuhan Shubo Technology Co., Ltd., Hongshan District, Fenghuo Innovation Valley, Wuhan, 430070, Hubei, China

    Liu Jianbo

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  1. Chen Yong
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  2. Hu Defeng
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Contributions

Defeng Hu, Yong Chen, and Chao Xu contributed to writing of the first draft and executive subject experiment; Yong Chen and Chao Xu were involved in conception of the project; Nannan Chen and Defeng Hu contributed to data acquisition and analysis; and Jianbo Liu was involved in experimental environment and equipment. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Chen Yong, Hu Defeng or Xu Chao.

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Yong, C., Defeng, H., Chao, X. et al. Smart contract generation model based on code annotation and AST-LSTM tuning. J Supercomput 81, 731 (2025). https://doi.org/10.1007/s11227-025-07186-x

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