Smart contracts are a foundational component of decentralized networks, enabling trustless operations without intermediaries. Yet, their development remains a highly specialized task, requiring domain-specific knowledge that spans cryptography, economic modeling, and secure coding. The study addresses this complexity by exploring whether it is possible to lower the entry barriers to decentralized applications (dApp) development through automated code generation techniques.
The motivation arises from observing a consistent pattern in blockchain adoption: while there is tremendous interest in creating digital assets, the technical barriers to building reliable and secure smart contracts often hinder innovation. Moreover, many of the tools available today either simplify too much – failing to capture the necessary economic behaviors – or are too rigid to accommodate evolving ecosystem standards. Through this study, we investigate whether combining modular code generation with Large Language Models (LLMs) and domain-specific metamodels can offer a scalable, safe, and adaptable alternative.
The blockchain ecosystem has undergone a transformation since the early days of Bitcoin. While the first generation of blockchain networks focused exclusively on value transfer, the second generation, spearheaded by Ethereum, introduced programmable contracts, giving rise to a variety of digital asset classes, including utility tokens, governance tokens, stablecoins, and non-fungible tokens (NFTs). Each of these assets is backed by a smart contract deployed on a decentralized network and bound by immutability once published.
This flexibility introduced a new problem: fragmentation. Different networks operate under unique execution environments, such as Ethereum’s EVM and Solana’s SeaLevel, and require different languages like Solidity or Rust. Standards such as ERC-20, ERC-721, and ERC-1155 aim to unify some of these operations, but they often lack the expressiveness needed to model advanced token economics (tokenomics), such as reflection fees, liquidity taxes, and buyback mechanisms.
Existing tools in the ecosystem offer partial solutions. OpenZeppelin Wizard enables the generation of Solidity contracts based on certain templates but lacks deep tokenomics modeling. RemixIDE provides AI-assisted code generation, but outputs are inconsistent and often incorrect. Solana’s Token-2022 specification facilitates fixed behavior tokens but lacks customizability.
Meanwhile, the emergence of LLMs has revolutionized the way developers approach code generation. Tools like GitHub Copilot demonstrate the feasibility of converting natural language into source code. However, these models are not reliable when dealing with niche domains such as smart contracts, where minor misinterpretations can result in significant security flaws or financial loss. A careful evaluation is needed to assess the role LLMs can play, not only as generators but as assistants in documentation, formatting, and error detection.
This study integrates several technological approaches to build a robust framework for smart contract generation. At the foundation lies a multi-model architecture inspired by Model-Driven Development (MDD). We define five distinct metamodels:
- Context Metamodel (CM): A language-agnostic representation of smart contract logic.
- Standard Metamodel (SM): Captures token standards (e.g., ERC-20, ERC-721).
- Tokenomic Metamodel (TM): Encodes economic behaviors such as tax, reflection, and liquidity management.
- Extension Metamodel (EM): Represents access control, voting mechanisms, and other modular functionalities.
- Language-Specific Metamodel (LSM): Translates context into syntax-aware models for target languages like Solidity.
Each model undergoes a sequence of Model-to-Model (M2M) transformations before being synthesized into source code through a Model-to-Text (M2T) transformation using Scriban templates. This layered architecture offers flexibility, ensuring the system can accommodate new standards or networks with minimal structural changes.
To support and enhance developer interaction, we also introduce a Visual Studio extension capable of real-time syntax highlighting, context-aware documentation, and integration with LLMs for auxiliary tasks. This IDE support system was essential to bridge the gap between abstract modeling and practical implementation.
Finally, LLMs play a complementary role. While their code generation capabilities are currently unreliable for complex smart contracts, we validate their utility in areas like semantic analysis, comment generation, and structural formatting. An abstraction layer enables switching between LLMs depending on task complexity and performance benchmarks.
Study Details
The study is initiated with the evaluation of the feasibility of automating smart contract generation through a modular and extensible architecture, while assessing the realistic contribution of LLMs in high-stakes environments such as blockchain development. Our goal is not simply to produce code but to ensure that what is generated is secure, efficient, and adaptable across decentralized ecosystems.
We begin with an empirical evaluation of existing LLMs against a diverse set of smart contract scenarios ranging from basic ERC-20 implementations to more complex use cases involving tax mechanisms, liquidity management, and voting extensions. We observe a direct correlation between the completeness of technical context provided and the quality of output. However, for contracts with higher complexity, especially those combining multiple tokenomics features, all models produce inconsistent or incorrect results. Notably, GPT-4 Turbo delivers the most reliable outcomes, but still fails in scenarios that require architectural awareness or security best practices, such as proper handling of reentrancy or permission logic.
Recognizing these limitations, we design and implement a code generation engine centered on a multi-phase transformation pipeline. The process begins with the user providing input through a high-level specification, which is interpreted as a Computing Independent Model (CIM). From this, we derive three parallel metamodels: the Standard (SM), the Tokenomic (TM), and the Extension (EM). These are validated using compatibility and dependency registries to ensure semantic coherence. Once validated, the Composer component consolidates these into a unified Context Metamodel (CM), representing a fully abstracted smart contract independent of any specific programming language.
This CM is then enriched through a transformation phase where additional properties, such as tokenomics behaviors or access control logic, are embedded. This is handled by the Augmenter, which operates at the instruction level when needed, modifying or injecting logic directly into specific functions such as transfer. A final transformation into a Language-Specific Metamodel (LSM) is performed by the Synthesizer, which maps abstract constructs to syntax-level components specific to Solidity or, in future phases, Rust.
The code generation itself is carried out through M2T transformations using Scriban templates. Each syntactic element of the LSM is paired with a corresponding template fragment, allowing modular construction of smart contracts. Instead of adopting a monolithic template per contract type, we implement a compositional approach. This modularity is particularly critical when multiple features converge within the same function or contract architecture. It enables isolated reasoning about logic, which improves maintainability and testing.
Alongside the code generation engine, we implement a Visual Studio extension to enhance developer experience. This extension integrates the M2T pipeline, allowing real-time preview and editing of generated contracts. It highlights syntax and semantic elements for both Solidity and Scriban within the same file, a feature made possible by a two-pass classification algorithm. It also interfaces with the LLM-agnostic service we developed to handle tasks such as code formatting, documentation generation, and static analysis. These operations, while secondary to code generation, play a vital role in usability and correctness, especially for developers less familiar with smart contract best practices.
One of the unexpected outcomes of the study is the realization that a rigid, inheritance-based approach – initially used to replicate OpenZeppelin’s model – is too inflexible for anything beyond basic contract types. The adoption of metamodeling not only resolves this but also opens a pathway to multi-chain and multi-language support. Because all critical logic is captured at an abstract level, transforming the output to another blockchain environment (e.g., from Solidity to Rust for Solana) becomes a matter of adjusting the Synthesizer and LSM for the new target.
By reducing the cognitive load on developers and eliminating repetitive boilerplate, it shortens development cycles. More importantly, it brings repeatability and security into a space where both are notoriously difficult to achieve. It enables non-specialist teams to prototype token economies without exposure to critical architectural risks. For enterprises, it allows rapid iteration of DeFi products while maintaining compliance with internal code quality standards.
The resulting framework is extensible, secure by design, and capable of supporting future advances in blockchain platforms, token standards, and economic mechanisms. The next phases will focus on expanding language support, integrating real-time code validation, and evolving the LLM service to better align with domain-specific generation tasks.
