2024/04/22 by Gábor Antal, Antal, Gábor, Richárd Vozár +3 · 3 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Model-Driven Software Engineering Techniques #Programming Languages (cs.PL) #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2404.14370
openalex publication_date 2024/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.