RT Journal Article
T1 Towards using Few-Shot Prompt Learning for Automating Model Completion
A1 BenChaaben, Meriem
A1 Burgueño-Caballero, Lola
A1 Sahraoui, Houari
K1 Ingeniería del software
AB We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.
PB IEEE
YR 2023
FD 2023
LK https://hdl.handle.net/10630/30332
UL https://hdl.handle.net/10630/30332
LA eng
NO M. B. Chaaben, L. Burgueño and H. Sahraoui, "Towards using Few-Shot Prompt Learning for Automating Model Completion," 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), Melbourne, Australia, 2023, pp. 7-12, doi: 10.1109/ICSE-NIER58687.2023.00008
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026