🤖 AI Summary
To address the high barrier to smart contract development and limited accessibility for non-technical stakeholders, this paper proposes an automated smart contract generation method based on a Multi-Level Finite State Machine (ML-FSM). The approach maps natural-language or structured traditional contracts into hierarchical, formal, and verifiable FSM models, enabling precise semantic modeling and modular decomposition. It integrates formal verification and security analysis techniques to rigorously validate functional properties and detect vulnerabilities in generated contracts. Experimental evaluation demonstrates that the framework substantially reduces development complexity while enhancing traceability, reusability, and security of smart contracts. It has successfully produced reliable, auditable contracts across diverse domains—including finance and supply chain management—thereby establishing a novel low-code paradigm for blockchain application development.
📝 Abstract
In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.