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2024

How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts

Wang, Beian, Wang, Chong, Liang, Peng et al.

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Since the emergence of GPT-3, Large Language Models (LLMs) have caught the eyes of researchers, practitioners, and educators in the field of software engineering.

However, there has been relatively little investigation regarding the performance of LLMs in assisting with requirements analysis and UML modeling.
This paper explores how LLMs can assist novice analysts in creating three types of typical UML models: use case models, class diagrams, and sequence diagrams.
For this purpose, we designed the modeling tasks of these three UML models for 45 undergraduate students who participated in a requirements modeling course, with the help of LLMs.

How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts · Around

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John Wiley & Sons, Ltd, 2009
A. v. Lamsweerde, Requirements Engineering: From System Goals to UML Models to Software Specifications · 2009
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Pearson Education, 2012
C. Larman, Applying UML and Patterns: An Introduction to Object Oriented Analysis and Design and Interative Development · 2012
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P. Vaithilingam, T. Zhang, and E. L. Glassman, “Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models,” in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI): Extended Abstracts
2022
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A. Ahmad, M. Waseem, P. Liang, M. Fahmideh, M. S. Aktar, and T. Mikkonen, “Towards human-bot collaborative software architecting with chatgpt,” in Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering (EASE)
2023
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D. Zimmermann and A. Koziolek, “Automating gui-based software testing with gpt-3,” in Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
2023
Earlier work this paper cites.
Z. Zheng, K. Ning, J. Chen, Y. Wang, W. Chen, L. Guo, and W. Wang, “Towards an understanding of large language models in software engineering tasks,” arXiv preprint arXiv:2308.11396
Original
2023
Earlier work this paper cites.

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C. Arora, J. Grundy, and M. Abdelrazek, “Advancing requirements engineering through generative ai: Assessing the role of llms,” arXiv preprint arXiv:2310.13976
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Later among the works it cites.
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D. Luitel, S. Hassani, and M. Sabetzadeh, “Improving requirements completeness: Automated assistance through large language models,” arXiv preprint arXiv:2308.03784

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J. Zhang, Y. Chen, N. Niu, and C. Liu, “Evaluation of chatgpt on requirements information retrieval under zero-shot setting,” Available at SSRN 4450322

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https://zenodo.org/doi/10.5281/zenodo.10532600

B. Wang, C. Wang, P. Liang, B. Li, and C. Zeng, “Case Study for the Paper: How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts,” January 2024 · 2024

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M. Waseem, T. Das, A. Ahmad, M. Fehmideh, P. Liang, and T. Mikkonen, “Chatgpt as a software development bot: A project-based study,” in Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

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