74 citations · 74 across the 6 of their papers we have counts for
11 papers
Aggregating empirical evidence from data strategy studies: a case on model quantization
Santiago del Rey, Paulo Sérgio Medeiros dos Santos, Guilherme Horta Travassos +2
Background: As empirical software engineering evolves, more studies adopt data strategiesapproaches that investigate digital artifacts such as models, source code, or system log…
Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
Santiago del Rey, Adrià Medina, Xavier Franch +1
Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models exc…
A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering
Vincenzo de Martino, Joel Castaño, Fabio Palomba +2
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories.…
How do Machine Learning Models Change?
Joel Castaño, Rafael Cabañas, Antonio Salmerón +2
The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Fa…
Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study
Vincenzo De Martino, Silverio Martínez-Fernández, Fabio Palomba
As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensiv…
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models
Alexandra González Álvarez, Joel Castaño, Xavier Franch +1
Background: Given the fast-paced nature of today's technology, which has surpassed human performance in tasks like image classification, visual reasoning, and English understanding…