The emergence of advanced neural networks has opened up new ways in automated code generation from conceptual models, promising to enhance software development processes. This paper presents a preliminary evaluation of GPT-4-Vision, a state-of-the-art deep learning model, and its capabilities in transforming Unified Modeling Language (UML) class diagrams into fully operating Java class files. In our study, we used exported images of 18 class diagrams comprising 10 single-class and 8 multi-class diagrams. We used 3 different prompts for each input, and we manually evaluated the results. We created a scoring system in which we scored the occurrence of elements found in the diagram within the source code. On average, the model was able to generate source code for 88% of the elements shown in the diagrams. Our results indicate that GPT-4-Vision exhibits proficiency in handling single-class UML diagrams, successfully transforming them into syntactically correct class files. However, for multi-class UML diagrams, the model's performance is weaker compared to single-class diagrams. In summary, further investigations are necessary to exploit the model's potential completely.

翻译：先进神经网络的出现为从概念模型自动生成代码开辟了新途径，有望提升软件开发流程。本文初步评估了GPT-4-Vision这一最先进深度学习模型将统一建模语言（UML）类图转换为可运行的Java类文件的能力。在我们的研究中，使用了18个类图的导出图像，其中包括10个单类图和8个多类图。我们对每个输入采用3种不同提示，并人工评估了结果。我们创建了一个评分系统，对源代码中出现的图中元素进行评分。平均而言，该模型能够为图中88%的元素生成源代码。结果表明，GPT-4-Vision在处理单类UML图方面表现出色，能成功将其转换为语法正确的类文件。然而，对于多类UML图，模型性能较单类图有所下降。总之，需要进一步研究以充分挖掘该模型的潜力。