Listar por autor "Burgueño-Caballero, Lola"
Mostrando ítems 41-48 de 48
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Testing M2T/T2M Transformations
Wimmer, Manuel; Burgueño-Caballero, Lola
(2013-10-09)
Testing model-to-model (M2M) transformations is becoming a prominent topic in the current Model-driven Engineering landscape. Current approaches for transformation testing, however, assume having explicit model representations ... -
Testing models and model transformations using classifying terms.
Hilken, Frank; Gogolla, Martin; Burgueño-Caballero, Lola; Vallecillo-Moreno, Antonio Jesús (2016-11-09)
This paper proposes the use of equivalence partitioning techniques for testing models and model transformations. In particular, we introduce the concept of classifying terms, which are general OCL terms on a class model ... -
Towards an Extensible Architecture and Tool Support for Model-based Verification.
Delgado Camacho, David Eduardo; Burgueño-Caballero, Lola; Cámara-Moreno, Javier; Troya-Castilla, Javier (2023)
Model-based software engineering (MBSE) brings models to the center of software and system design. Models are powerful abstractions used to support all phases of the software development life cycle of complex software. As ... -
Towards Approximate Model Transformations
As the size and complexity of models grow, there is a need to count on novel mechanisms and tools for transforming them. This is required, e.g., when model transformations need to provide target models without having access ... -
Towards Distributed Model Transformations with LinTra
Performance and scalability of model transformations are becoming prominent topics in Model-Driven Engineering. In previous works we introduced LinTra, a platform for executing model transformations in parallel. LinTra ... -
Towards standarized benchmarks of LLMs in software modeling tasks: a conceptual framework
Cámara-Moreno, Javier; Burgueño-Caballero, Lola; Troya-Castilla, Javier (Springer, 2024-09-03)
The integration of Large Language Models (LLMs) in software modeling tasks presents both opportunities and challenges. This Expert Voice addresses a significant gap in the evaluation of these models, advocating for the ... -
Towards using Few-Shot Prompt Learning for Automating Model Completion
BenChaaben, Meriem; Burgueño-Caballero, Lola; Sahraoui, Houari (IEEE, 2023)
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 ... -
Using Physical Quantities in Robot Software Models
Burgueño-Caballero, Lola; Mayerhofer, Tanja; Wimmer, Manuel; Vallecillo-Moreno, Antonio Jesús (2018-05-31)
One of the challenges of modeling any software application that deals with real-world physical systems resides in the correct representation of numerical values and their units. This paper shows how both measurement ...