Publications

The publications related to the project are:

A) Journals

• Castanyer, R.C., Martínez-Fernández, S. & Franch, X. Which design decisions in AI-enabled mobile applications contribute to greener AI?. Empir Software Eng 29, 2 (2024). https://doi.org/10.1007/s10664-023-10407-7
• Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta: Training future ML engineers: a project-based course on MLOps. IEEE Software, 2024.
  https://ieeexplore.ieee.org/abstract/document/10243109.
• Francisco Durán, Silverio Martínez-Fernández, Michael Felderer and Xavier Franch. Guiding the retraining of convolutional neural networks against adversarial inputs. Accepted in PeerJ Computer Science, 2023. https://peerj.com/articles/cs-1454/

B) Conferences

• Pau Duran, Joel Castaño, Cristina Gómez, Silverio Martínez-Fernández: GAISSALabel: A tool for energy labeling of ML models, FSE Demo, pp. 622-626, 2024, https://arxiv.org/abs/2401.17150 . https://doi.org/10.1145/3663529.3663811
• Carles Farré, Xavier Franch: Validity at the Forefront: Investigating Threats in Green AI Research. CAiSE Forum 2024: 55-63. https://doi.org/10.1007/978-3-031-61000-4_7
• Santiago del Rey: Software Design Decisions for Greener Machine Learning-based Systems, DS@CAIN 2024, pp. 256-258. https://doi.org/10.1145/3644815.3644972
• Francisco Durán, Silverio Martínez-Fernández, Matias Martinez, Patricia Lago: Identifying architectural design decisions for achieving green ML serving, CAIN@ICSE2024, pp. 18-23. [pdf]. https://doi.org/10.1145/3644815.3644962
• Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner: Analyzing the Evolution and Maintenance of ML Models on Hugging Face, MSR 2024, pp. 607-618. [pdf]. https://ieeexplore.ieee.org/document/10555709
• Joel Castaño, Silverio Martínez-Fernández, Xavier Franch: Lessons Learned from Mining the Hugging Face Repository, WSESE@ICSE 2024
• Matias Martinez, Silverio Martínez-Fernández, Xavier Franch: Energy Consumption of Automated Program Repair, Póster track ICSE 2024, pp. 358-359. [pdf]
• Santiago del Rey, Silverio Martínez-Fernández, Luís Cruz and Xavier Franch. Do DL models and training environments have an impact on energy consumption? Accepted in DSD/SEAA 2023 conference. https://ieeexplore.ieee.org/document/10371661
• Silverio Martínez-Fernández, Xavier Franch and Francisco Durán. Towards green AI-based software systems: an architecture-centric approach (GAISSA). Accepted in DSD/SEAA 2023 conference. https://ieeexplore.ieee.org/abstract/document/10371616
  
• H. Ahmed, A. Boshchenko, N. Khan, D. Knyajev, D. Garifollina, G. Scoccia, M. Martinez, I. Malavolta. Evolution of Kotlin Apps in terms of Energy Consumption: An Exploratory Study.  (ICT4S 2023). [pdf]
• Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner: Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study. (2023). Accepted in the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) 2023. [pdf]
• Filippo Lanubile, Silverio Martínez-Fernández, Luigi Quaranta: Teaching MLOps in Higher Education through Project-Based Learning. ICSE SEET (2023) [pdf]
• Santiago del Rey, Silverio Martínez-Fernández and Xavier Franch A review on green deployment for Edge AI. ICT4S 2023
• Xavier Franch, Andreas Jedlitschka, Silverio Martínez-Fernández. A Requirements Engineering Perspective to AI-Based Systems Development: A Vision Paper. In Proceedings of the 29th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2023). LNCS 13975: 223-232, Springer. https://doi.org/10.1007/978-3-031-29786-1_15.
• Yinlena Xu, Silverio Martínez-Fernández, Matias Martinez, Xavier Franch. Energy Efficiency of Training Neural Network Architectures: An Empirical Study. In Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS 2023): 781-790. https://hdl.handle.net/10125/102727 [pdf]

C) In arxiv

• Omar, R., Bogner, J., Muccini, H., Lago, P., Martínez-Fernández, S., & Franch, X. (2024). The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems. arXiv preprint arXiv:2407.02914. https://arxiv.org/abs/2407.02914
• Cruz, L., Gutierrez, X. F., & Martínez-Fernández, S. (2024). Innovating for Tomorrow: The Convergence of SE and Green AI. arXiv preprint arXiv:2406.18142. https://arxiv.org/abs/2406.18142

 D) Master and bachelor theses

• González Álvarez, Alexandra: "Analyzing how model optimization techniques affect model correctness, inference time and energy consumption of computer vision models". June 2024. GCED, UPC
• Duran Manzano, Pau: "Eina de generació d'etiquetes energètiques per a models d'aprenentatge automàtic". January 2024. GEI, UPC
• Domingo Reguero, Álvaro: "Energy-aware training of neural network architectures: Trade-off between correctness and energy consumption". October 2023. GCED, UPC
• Córdova Pou, Gonzalo: "Computer screenshot classification for boosting ADHD productivity in a VR environment". June 2023. GCED, UPC.
• Lagarde Teixidó, Alec: "Energy efficiency measurement in optimization and inference of ML models". June 2023. GCED, UPC.
• Castaño Fernández, Joel: "A greenability evaluation sheet for AI-based systems". June 2023. GCED, UPC.
• Santiago del Rey: “An analysis of modeling and training decisions for greener computer vision systems“. January 2023. MAI, UPC. Excellent Cum Laude.
• Daniel Escribano Pérez: “Energy consumption of machine learning deployment in cloud providers”. January 2023. GEI, UPC.
• Pablo Gámiz Nieto: “Mesura de l’eficiència energètica de sistemes software i components urbans”. June 2022. GEI, UPC.

  E) Related publications from other related projects (not GAISSA):

• Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, Stefan Wagner. Software Engineering for AI-Based Systems: A Survey. ACM Transactions on Software Engineering Methodology 31(2): 37e:1-37e:59 (2022). [pdf]
• Xavier Franch, Silverio Martínez-Fernández, Claudia P. Ayala, Cristina Gómez. Architectural Decisions in AI-based Systems: An Ontological View. In Procedings of the 15th International Conference on Quality of Information and Communications Technology (QUATIC 2022). CCIS 1621: 18-27, Springer. https://doi.org/10.1007/978-3-031-14179-9_2
• Roger Creus Castanyer, Silverio Martínez-Fernández, Xavier Franch. Integration of Convolutional Neural Networks in Mobile Applications. In Proceedings of the 1st IEEE/ACM Workshop on AI Engineering-Software Engineering for AI (WAIN 2021): 27-34. https://doi.org/10.1109/WAIN52551.2021.00010 [pdf]