# Coding by Design: GPT-4 empowers Agile Model Driven Development

arXiv: https://arxiv.org/abs/2310.04304 · HTML: https://mlsanity.com/paper/2310.04304/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [Improving ChatGPT Prompt for Code Generation](https://mlsanity.com/paper/2305.08360/) (2023)
- [Analysis of ChatGPT on Source Code](https://mlsanity.com/paper/2306.00597/) (2023)
- [Bi-level Network Design for UAM Vertiport Allocation Using Activity-Based Transport Simulations](https://mlsanity.com/paper/2307.14731/) (2023)

## Built on by (most cited first, 6 of 6)

- [Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3](https://mlsanity.com/paper/2504.16027/) (2025)
- [Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models](https://mlsanity.com/paper/2509.07010/) (2025)
- [LLM as a code generator in Agile Model Driven Development](https://mlsanity.com/paper/2410.18489/) (2024)
- [HyperGraphOS: A Meta Operating System for Science and Engineering](https://mlsanity.com/paper/2412.04923/) (2024)
- [Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams](https://mlsanity.com/paper/2508.03379/) (2025)
- [Toward AI-Augmented Cooperative Engineering Workflows: Requirements and Architecture the European Rover Challenge](https://mlsanity.com/paper/2609.31136/) (2026)

## Abstract (arXiv)

Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language presents challenges for complex software designs. Accordingly, our research offers an Agile Model-Driven Development (MDD) approach that enhances code auto-generation using OpenAI's GPT-4. Our work emphasizes "Agility" as a significant contribution to the current MDD method, particularly when the model undergoes changes or needs deployment in a different programming language. Thus, we present a case-study showcasing a multi-agent simulation system of an Unmanned Vehicle Fleet. In the first and second layer of our approach, we constructed a textual representation of the case-study using Unified Model Language (UML) diagrams. In the next layer, we introduced two sets of constraints that minimize model ambiguity. Object Constraints Language (OCL) is applied to fine-tune the code constructions details, while FIPA ontology is used to shape communication semantics and protocols. Ultimately, leveraging GPT-4, our last layer auto-generates code in both Java and Python. The Java code is deployed within the JADE framework, while the Python code is deployed in PADE framework. Concluding our research, we engaged in a comprehensive evaluation of the generated code. From a behavioural standpoint, the auto-generated code aligned perfectly with the expected UML sequence diagram. Structurally, we compared the complexity of code derived from UML diagrams constrained solely by OCL to that influenced by both OCL and FIPA-ontology. Results indicate that ontology-constrained model produce inherently more intricate code, but it remains manageable and low-risk for further testing and maintenance.