Joel Castaño

According to our database, Joel Castaño authored at least 17 papers between 2023 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A Methodological Framework for LLM-Based Mining of Software Repositories.

Vincenzo De Martino, Joel Castaño, Fabio Palomba, Xavier Franch, Silverio Martínez-Fernández

CoRR, August, 2025

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

Luis Cruz, João Paulo Fernandes, Maja H. Kirkeby, Silverio Martínez-Fernández, June Sallou, Hina Anwar, Enrique Barba Roque, Justus Bogner, Joel Castaño, Fernando Castor, Aadil Chasmawala, Simão Cunha, Daniel Feitosa, Alexandra González, Andreas Jedlitschka, Patricia Lago, Henry Muccini, Ana Oprescu, Pooja Rani, João Saraiva, Federica Sarro, Raghavendra Selvan, Karthik Vaidhyanathan, Roberto Verdecchia, Ivan P. Yamshchikov

CoRR, June, 2025

Impact of ML optimization tactics on greener pre-trained ML models.

Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández

Computing, April, 2025

Replication Package for "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models".

Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández

Dataset, February, 2025

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering.

Vincenzo De Martino, Joel Castaño, Fabio Palomba, Xavier Franch, Silverio Martínez-Fernández

Proceedings of the IEEE/ACM International Workshop on Methodological Issues with Empirical Studies in Software Engineering, 2025

2024

Replication Package for 'How do Machine Learning Models Change?'.

Joel Castaño Fernández, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández

Dataset, November, 2024

Replication Package for 'How do Machine Learning Models Change?'.

Joel Castaño Fernández, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández

Dataset, November, 2024

Replication Package for "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models".

Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández

Dataset, October, 2024

Replication Package for "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models".

Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández

Dataset, July, 2024

Replication Package for "Impact of ML Optimization Tactics on Greener Pre-Trained ML Models".

Alexandra González Álvarez, Joel Castaño, Xavier Franch, Silverio Martínez-Fernández

Dataset, July, 2024

Experiences from Using LLMs for Repository Mining Studies in Empirical Software Engineering.

Vincenzo De Martino, Joel Castaño, Fabio Palomba, Xavier Franch, Silverio Martínez-Fernández

CoRR, 2024

How do Machine Learning Models Change?

Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández

CoRR, 2024

Lessons Learned from Mining the Hugging Face Repository.

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch

Proceedings of the 1st IEEE/ACM International Workshop on Methodological Issues with Empirical Studies in Software Engineering, 2024

GAISSALabel: A Tool for Energy Labeling of ML Models.

Pau Duran, Joel Castaño, Cristina Gómez, Silverio Martínez-Fernández

Proceedings of the Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, 2024

Analyzing the Evolution and Maintenance of ML Models on Hugging Face.

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner

Proceedings of the 21st IEEE/ACM International Conference on Mining Software Repositories, 2024

2023

Replication Package for 'Analyzing the Evolution and Maintenance of ML Models on Hugging Face'.

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner

Dataset, November, 2023

Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study.

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner

Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2023