Guiding LLM-based Smart Contract Generation with Finite State Machine

Luo, Hao; Lin, Yuhao; Yan, Xiao; Hu, Xintong; Wang, Yuxiang; Zeng, Qiming; Wang, Hao; Jiang, Jiawei

doi:10.24963/ijcai.2025/653

Guiding LLM-based Smart Contract Generation with Finite State Machine

Hao Luo, Yuhao Lin, Xiao Yan, Xintong Hu, Yuxiang Wang, Qiming Zeng, Hao Wang, Jiawei Jiang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5869-5877.

https://doi.org/10.24963/ijcai.2025/653

Keywords

Machine Learning: ML: Applications
Machine Learning: ML: Foundation models

Abstract

Smart contract is a kind of self-executing code based on blockchain technology with a wide range of application scenarios, but the traditional generation method relies on manual coding and expert auditing, which has a high threshold and low efficiency. Although Large Language Models (LLMs) show great potential in programming tasks, they still face challenges in smart contract generation w.r.t. effectiveness and security. To solve these problems, we propose FSM-SCG, a smart contract generation framework based on finite state machine (FSM) and LLMs, which significantly improves the quality of the generated code by abstracting user requirements to generate FSM, guiding LLMs to generate smart contracts, and iteratively optimizing the code with the feedback of compilation and security checks. The experimental results show that FSM-SCG significantly improves the quality of smart contract generation. Compared to the best baseline, FSM-SCG improves the compilation success rate of generated smart contract code by at most 48%, and reduces the average vulnerability risk score by approximately 68%.

Hao Luo, Yuhao Lin, Xiao Yan, Xintong Hu, Yuxiang Wang, Qiming Zeng, Hao Wang, and Jiawei Jiang. 2025. Guiding LLM-based Smart Contract Generation with Finite State Machine. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5869–5877. https://doi.org/10.24963/ijcai.2025/653

@inproceedings{ijcai2025p653,
  title     = {Guiding LLM-based Smart Contract Generation with Finite State Machine},
  author    = {Luo, Hao and Lin, Yuhao and Yan, Xiao and Hu, Xintong and Wang, Yuxiang and Zeng, Qiming and Wang, Hao and Jiang, Jiawei},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {5869--5877},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/653},
  url       = {https://doi.org/10.24963/ijcai.2025/653},
}

TY  - CPAPER
TI  - Guiding LLM-based Smart Contract Generation with Finite State Machine
AU  - Luo, Hao
AU  - Lin, Yuhao
AU  - Yan, Xiao
AU  - Hu, Xintong
AU  - Wang, Yuxiang
AU  - Zeng, Qiming
AU  - Wang, Hao
AU  - Jiang, Jiawei
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 5869
EP  - 5877
DO  - 10.24963/ijcai.2025/653
UR  - https://doi.org/10.24963/ijcai.2025/653
ER  -