activity
20202026
most citedEnergy Efficiency of Training Neural Network Architectures: An Empirical Study

11 citations · 29 across the 13 of their papers we have counts for

collaborators

14 papers

cs.SE2026

Green Architectural Tactics in ML-enabled Systems: An LLM-based Repository Mining Study

Vincenzo De Martino, Silverio Martínez-Fernández, Fabio Palomba

Context: The increasing adoption of machine learning (ML) and artificial intelligence (AI) technologies raises growing concerns about their environmental sustainability. Developing…

cs.SE2026

A Tool for Automatically Cataloguing and Selecting Pre-Trained Models and Datasets for Software Engineering

Alexandra González, Oscar Cerezo, Xavier Franch +1

The rapid growth of machine learning assets has made it increasingly difficult for software engineers to identify models and datasets that match their specific needs. Browsing larg…

cs.SE2026

SEMODS: A Validated Dataset of Open-Source Software Engineering Models

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face…

cs.SE2025

A Methodological Framework for LLM-Based Mining of Software Repositories

Vincenzo De Martino, Joel Castaño, Fabio Palomba +2

Large Language Models (LLMs) are increasingly used in software engineering research, offering new opportunities for automating repository mining tasks. However, despite their growi…

cs.SE2025★ 9 cited

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices

Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby +22

The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…

cs.SE2025

Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings

Alexandra González, Xavier Franch, David Lo +1

Context: Open-source Pre-Trained Models (PTMs) provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software E…