Abstract
Since the launch of Ethereum in 2013, the smart contract has been a momentous part of the blockchain systems due to its character of automatic execution. The generation of smart contracts has also attracted extensive attention from the academic community. However, the preparation and generation of smart contracts are still mainly manual so far, which limits the scalability of the smart contracts. In this paper, we put forward a new method to generate smart contracts automatically based on knowledge extraction and Unified Modeling Language (UML), which can significantly accelerate the generation of smart contracts. We will describe this method in more detail based on the logistics supply chain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Liu, L., et al.: A recursive reinforced blockchain performance evaluation and improvement architecture: maximising diversity to improve scalability. Int. J. Commun. Syst. e5315 (2022)
Zheng, Z., Xie, S., Dai, H.-N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018)
Jurgelaitis, M., Čeponienė, L., Butkienė, R.: Solidity code generation from UML state machines in model-driven smart contract development. IEEE Access 10, 33465–33481 (2022)
Liu, L., Wei-Tek Tsai, Md., Bhuiyan, Z.A., Peng, H., Liu, M.: Blockchain-enabled fraud discovery through abnormal smart contract detection on ethereum. Futur. Gener. Comput. Syst. 128, 158–166 (2022)
Balog, M., Gaunt, A.L., Brockschmidt, M., Nowozin, S., Tarlow, D.: Deepcoder: learning to write programs. arXiv preprint arXiv:1611.01989 (2016)
Veeramani, A., Venkatesan, K., Nalinadevi, K.: Abstract syntax tree based unified modeling language to object oriented code conversion. In: Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, pp. 1–8 (2014)
Choudhury, O., Rudolph, N., Sylla, I., Fairoza, N., Das, A.: Auto-generation of smart contracts from domain-specific ontologies and semantic rules. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 963–970. IEEE (2018)
Tateishi, T., Yoshihama, S., Sato, N., Saito, S.: Automatic smart contract generation using controlled natural language and template. IBM J. Res. Dev. 63(2/3), 6–1 (2019)
Asawa, K., Kukreja, S., Gondkar, R.: An NCDP for developing a blockchain based dynamic supply chain management with auto-generation of smart contract. In: 2021 26th International Conference on Automation and Computing (ICAC), pp. 1–6. IEEE (2021)
Li, X., et al.: Entity-relation extraction as multi-turn question answering. arXiv preprint arXiv:1905.05529 (2019)
Li, Q., et al.: Reinforcement learning-based dialogue guided event extraction to exploit argument relations. IEEE/ACM Trans. Audio Speech Lang. Process. 30, 520–533 (2021)
Peng, H., et al.: Fine-grained event categorization with heterogeneous graph convolutional networks. arXiv preprint arXiv:1906.04580 (2019)
Peng, H., et al.: Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Trans. Knowl. Discov. Data 15(5), 1–33 (2021)
Peng, H., Zhang, R., Li, S., Cao, Y., Pan, S., Philip, Yu.: Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 980–998 (2022)
Peng, H., Zhang, R., Dou, Y., Yang, R., Zhang, J., Yu, P.S.: Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans. Inf. Syst. (TOIS) 40(4), 1–46 (2021)
Peng, H., et al.: Lime: low-cost and incremental learning for dynamic heterogeneous information networks. IEEE Trans. Comput. 71(3), 628–642 (2021)
Ben Abdessalem Karaa, W., Ben Azzouz, Z., Singh, A., Dey, N., Ashour, A.S., Ben Ghazala, H.: Automatic builder of class diagram (ABCD): an application of UML generation from functional requirements. Softw. Pract. Exp. 46(11), 1443–1458 (2016)
Abdelnabi, E.A., Maatuk, A.M., Abdelaziz, T.M., Elakeili, S.M.: Generating UML class diagram using NLP techniques and heuristic rules. In: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 277–282 (2020)
Peng, H., Li, H., Song, Y., Zheng, V., Li, J.: Differentially private federated knowledge graphs embedding. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1416–1425 (2021)
Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. arXiv preprint arXiv:1909.07755 (2019)
Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. arXiv preprint arXiv:2004.13625 (2020)
Niaz, I.A., Tanaka, J., et al.: Mapping UML statecharts to java code. In: IASTED Conference on Software Engineering, pp. 111–116 (2004)
Usman, M., Nadeem, A.: Automatic generation of java code from UML diagrams using UJECTOR. Int. J. Softw. Eng. Appl. 3(2), 21–37 (2009)
Sunitha, E.V., Samuel, P.: Automatic code generation from UML state chart diagrams. IEEE Access 7, 8591–8608 (2019)
Kai, H., Zhu, J., Ding, Y., Bai, X., Huang, J.: Smart contract engineering. Electronics 9(12), 2042 (2020)
Acknowledgements
This work was supported by the S &T Program of Hebei through grant 20310101D.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ran, P. et al. (2023). Automatic Smart Contract Generation with Knowledge Extraction and Unified Modeling Language. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-031-28124-2_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28123-5
Online ISBN: 978-3-031-28124-2
eBook Packages: Computer ScienceComputer Science (R0)