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Article

SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development

1
Information Modeling and Communication Systems Team, EDPAGS Laboratory, Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco
2
LaGeS Laboratory, Hassania School of Public Works, Casablanca 20230, Morocco
*
Author to whom correspondence should be addressed.
Information 2025, 16(10), 870; https://doi.org/10.3390/info16100870
Submission received: 30 August 2025 / Revised: 28 September 2025 / Accepted: 3 October 2025 / Published: 7 October 2025
(This article belongs to the Special Issue Using Generative Artificial Intelligence Within Software Engineering)

Abstract

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering.
Keywords: smart contracts; blockchain; software engineering; model-driven engineering (MDE); large language models (LLMs); generative artificial intelligence (Gen-AI); code generation smart contracts; blockchain; software engineering; model-driven engineering (MDE); large language models (LLMs); generative artificial intelligence (Gen-AI); code generation

Share and Cite

MDPI and ACS Style

Ait Hsain, Y.; Laaz, N.; Mbarki, S. SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development. Information 2025, 16, 870. https://doi.org/10.3390/info16100870

AMA Style

Ait Hsain Y, Laaz N, Mbarki S. SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development. Information. 2025; 16(10):870. https://doi.org/10.3390/info16100870

Chicago/Turabian Style

Ait Hsain, Yassine, Naziha Laaz, and Samir Mbarki. 2025. "SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development" Information 16, no. 10: 870. https://doi.org/10.3390/info16100870

APA Style

Ait Hsain, Y., Laaz, N., & Mbarki, S. (2025). SCEditor-Web: Bridging Model-Driven Engineering and Generative AI for Smart Contract Development. Information, 16(10), 870. https://doi.org/10.3390/info16100870

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