On LLM-Assisted Generation of Smart Contracts from Business Processes

• URL: http://arxiv.org/abs/2507.23087v1
• Date: Wed, 30 Jul 2025 20:39:45 GMT
• Title: On LLM-Assisted Generation of Smart Contracts from Business Processes
• Abstract summary: Large language models (LLMs) have changed the reality of how software is produced.<n>We present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions.<n>Our results show that LLM performance falls short of the perfect reliability required for smart contract development.
• Score: 0.08192907805418582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices [54.956760584923295]
  Researchers have made substantial progress using Large Language Models (LLMs) for code generation.<n>However, developers typically write code based on structured designs or specifications rather than raw natural language descriptions.<n>This gap between existing benchmarks and real industry development practices means that current benchmark scores may not accurately reflect how much code generation can help automate development tasks.
  arXiv  Detail & Related papers  (2026-04-24T15:35:54Z)
• From Helpful to Trustworthy: LLM Agents for Pair Programming [0.0]
  LLM-based coding agents are increasingly used to generate code, tests, and documentation.<n>Still, their outputs can be plausible yet misaligned with developer intent and provide limited evidence for review in evolving projects.<n>This research proposes a systematic study of multi-agent LLM pair programming that externalizes intent and uses development tools for iterative validation.
  arXiv  Detail & Related papers  (2026-04-11T17:39:57Z)
• Environment-Aware Code Generation: How far are We? [52.69113158357018]
  It is unclear whether large language models (LLMs) can reliably generate executable code tailored to a user's specific environment.<n>We present the first systematic study of Environment-Aware Code Generation (EACG), where generated code must be functionally correct and directly executable under arbitrary software configurations.<n>Our results show that current LLMs struggle with environment-specific code generation, while our adaptations improve environment compatibility and executability.
  arXiv  Detail & Related papers  (2026-01-18T04:58:15Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML [3.5515013986822073]
  We present a case study conducted in collaboration with the leveling department at A.<n>We investigate the performance of LLMs in generating functional, maintainable code within a closed, highly specialized software environment.<n>The findings reveal that prompting techniques and model size have a significant impact on output quality.
  arXiv  Detail & Related papers  (2025-09-15T19:39:26Z)
• MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
  We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
  arXiv  Detail & Related papers  (2025-07-16T14:31:33Z)
• Large Language Model Unlearning for Source Code [65.42425213605114]
  PROD is a novel unlearning approach that enables LLMs to forget undesired code content while preserving their code generation capabilities.<n>Our evaluation demonstrates that PROD achieves superior balance between forget quality and model utility compared to existing unlearning approaches.
  arXiv  Detail & Related papers  (2025-06-20T16:27:59Z)
• Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications [0.9105696129628794]
  Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields.<n>This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code.
  arXiv  Detail & Related papers  (2025-03-03T07:17:30Z)
• SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors [5.247363735860479]
  Large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks.<n>Given LLMs' ability to understand and process diverse programs, they present a promising direction for building general-purpose surrogate models.<n>We introduce SURGE, a benchmark with $1160$ problems covering $8$ key aspects.<n>Through empirical analysis of $21$ open-source and proprietary LLMs, we examine scaling laws, data efficiency, and predictive accuracy.
  arXiv  Detail & Related papers  (2025-02-16T15:38:19Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• A Survey on Evaluating Large Language Models in Code Generation Tasks [30.256255254277914]
  This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks.<n>With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation.
  arXiv  Detail & Related papers  (2024-08-29T12:56:06Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• Exploring Data-Efficient Adaptation of Large Language Models for Code Generation [64.5583894165813]
  We propose a novel adaptation approach named DEED, which stands for Data-Efficient adaptation with Error-Driven learning for code generation.<n> Experimental results show that, compared to other mainstream fine-tuning approaches, DEED achieves superior performance with few training data.
  arXiv  Detail & Related papers  (2024-02-29T16:09:02Z)
• Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming [12.355284125578342]
  Large Language Models (LLMs) have become a focal point in modern software development. LLMs offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, each system requires the LLM to be honed to its set of workspaces to ensure the best performance.
  arXiv  Detail & Related papers  (2024-02-22T03:51:34Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.