# On LLM-Assisted Generation of Smart Contracts from Business Processes

arXiv: https://arxiv.org/abs/2507.23087 · HTML: https://mlsanity.com/paper/2507.23087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://mlsanity.com/paper/2310.08754/) (2023)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [Bias Testing and Mitigation in LLM-based Code Generation](https://mlsanity.com/paper/2309.14345/) (2023)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Large Language Models for Business Process Management: Opportunities and Challenges](https://mlsanity.com/paper/2304.04309/) (2023)
- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [SAP Signavio Academic Models: A Large Process Model Dataset](https://mlsanity.com/paper/2208.12223/) (2022)
- [Guiding LLM-based Smart Contract Generation with Finite State Machine](https://mlsanity.com/paper/2505.08542/) (2025)
- [An Empirical Study of AI-based Smart Contract Creation](https://mlsanity.com/paper/2308.02955/) (2023)
- [Efficacy of Various Large Language Models in Generating Smart Contracts](https://mlsanity.com/paper/2407.11019/) (2024)
- [Sustainability via LLM Right-sizing](https://mlsanity.com/paper/2504.13217/) (2025)
- [Automation in Model-Driven Engineering: A look back, and ahead](https://mlsanity.com/paper/2405.18539/) (2024)
- [Optimized Execution of Business Processes on Blockchain](https://mlsanity.com/paper/1612.03152/) (2016)
- [Blockchain for Business Process Enactment: A Taxonomy and Systematic Literature Review](https://mlsanity.com/paper/2206.03237/) (2022)
- [Process Channels: A New Layer for Process Enactment Based on Blockchain State Channels](https://mlsanity.com/paper/2304.01107/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs](https://mlsanity.com/paper/2608.30250/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have changed the reality of how software is produced. Within the wider software engineering community, among many other purposes, they are explored for code generation use cases from different types of input. In this work, we present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions, an idea that has emerged in recent literature to overcome the limitations of traditional rule-based code generation approaches. However, current LLM-based work evaluates generated code on small samples, relying on manual inspection, or testing whether code compiles but ignoring correct execution. With this work, we introduce an automated evaluation framework and provide empirical data from larger data sets of process models. We test LLMs of different types and sizes in their capabilities of achieving important properties of process execution, including enforcing process flow, resource allocation, and data-based conditions. Our results show that LLM performance falls short of the perfect reliability required for smart contract development. We suggest future work to explore responsible LLM integrations in existing tools for code generation to ensure more reliable output. Our benchmarking framework can serve as a foundation for developing and evaluating such integrations.