3 citations · 4 across the 8 of their papers we have counts for
9 papers
LLM-based Generation of Semantically Diverse and Realistic Domain Model Instances
Andrei Coman, Lola Burgueño, Dominik Bork +1
Large Language Models (LLMs) have been recently proposed for supporting domain modeling tasks mostly related to the completion of partial models by recommending additional model el…
Detecting Semantic Alignments between Textual Specifications and Domain Models
Shwetali Shimangaud, Lola Burgueño, Rijul Saini +1
Context: Having domain models derived from textual specifications has proven to be very useful in the early phases of software engineering. However, creating correct domain models…
A framework for assessing the capabilities of code generation of constraint domain-specific languages with large language models
David Delgado, Lola Burgueño, Robert Clarisó
Large language models (LLMs) can be used to support software development tasks, e.g., through code completion or code generation. However, their effectiveness drops significantly w…
A Benchmarking Framework for Model Datasets
Philipp-Lorenz Glaser, Lola Burgueño, Dominik Bork
Empirical and LLM-based research in model-driven engineering increasingly relies on datasets of software models, for instance, to train or evaluate machine learning techniques for…
Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education
Shalini Chakraborty, Lola Burgueño, Nathalie Moreno +2
Generative Artificial Intelligence (GenAI) is rapidly gaining momentum in software modeling education, embraced by both students and educators. As GenAI assists with interpreting r…
On the Utility of Domain Modeling Assistance with Large Language Models
Meriem Ben Chaaben, Lola Burgueño, Istvan David +1
Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to synta…