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.

本研究初步评估了先进的神经网络在自动化代码生成方面的能力，通过转换统一建模语言（UML）类图为 Java 类文件，GPT-4-Vision 模型在处理单一类 UML 图方面表现出较高的成功率，但在处理多类别 UML 图时性能较弱，需要进一步研究来充分发挥该模型的潜力。