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.

使用大型语言模型（LLMs）如 ChatGPT 从自然语言中生成代码似乎是开创性的。然而，随着更广泛的使用，显然这种方法有自己的局限性。本研究提出了一种敏捷模型驱动开发（MDD）方法，使用 OpenAI 的 GPT-4 来增强代码自动生成。我们的工作强调 “敏捷性” 是对当前 MDD 方法的重要贡献，特别是当模型发生变化或需要部署到不同的编程语言时。因此，我们展示了一个案例研究，展示了无人驾驶车队的多代理仿真系统。在我们的方法的第一层和第二层，我们使用统一建模语言（UML）图示构建了案例研究的文本表示。在下一层中，我们引入了两组约束，以最小化模型的歧义性。对象约束语言（OCL）被应用于微调代码构建细节，而 FIPA 本体论用于塑造通信语义和协议。最后，利用 GPT-4，我们的最后一层自动生成 Java 和 Python 两种代码。Java 代码在 JADE 框架中部署，而 Python 代码在 PADE 框架中部署。在研究的结论部分，我们进行了对生成代码的全面评估。从行为角度来看，自动生成的代码与预期的 UML 顺序图完全一致。结构上，我们比较了仅受 OCL 约束的从 UML 图中导出的代码与既受 OCL 又受 FIPA 本体论影响的代码的复杂性。结果表明，本体论约束的模型产生了固有更复杂的代码，但仍然可管理并且对进一步的测试和维护风险较低。