Publications

2026

1. Scientific Software

   Game engines for sustainable open science software: a case study

   William Hurst, Caspar Krampe, Kwabena Ebo Bennin, and 1 more author

   Entertainment Computing, May 2026

   DOI Bib HTML

   @article{Hurst2026May,
     author = {Hurst, William and Krampe, Caspar and Bennin, Kwabena Ebo and Sallou, June},
     title = {{Game engines for sustainable open science software: a case study}},
     journal = {Entertainment Computing},
     volume = {57},
     pages = {101123},
     year = {2026},
     month = may,
     issn = {1875-9521},
     publisher = {Elsevier},
     doi = {10.1016/j.entcom.2026.101123},
     url = {https://doi.org/10.1016/j.entcom.2026.101123},
   }

2. Green AI

   Carbon Chat: A Virtual Reality Game for Creating Awareness of the Environment Impact of LLMs

   William Hurst, Caspar Krampe, Kwabena Ebo Bennin, and 1 more author

   In 2026 12th International Conference on Virtual Reality (ICVR), 2026

   Accepted

   Bib HTML

   @inproceedings{HKS26,
     author = {Hurst, William and Krampe, Caspar and Bennin, Kwabena Ebo and Sallou, June},
     title = {{Carbon Chat: A Virtual Reality Game for Creating Awareness of the Environment Impact of LLMs}},
     booktitle = {2026 12th International Conference on Virtual Reality (ICVR)},
     year = {2026},
     note = {Accepted},
     url = {https://www.icvr.org/cfp.html#},
   }

3. Agentic AI

   Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners

   Ruoyu Su, Matteo Esposito, Roberta Capuano, and 4 more authors

   IEEE Software, Mar 2026

   DOI Bib HTML PDF

   @article{Su2026Mar,
     author = {Su, Ruoyu and Esposito, Matteo and Capuano, Roberta and Omar, Rafiullah and Sallou, June and Muccini, Henry and Taibi, Davide},
     title = {{Making Sense of AI Agents Hype: Adoption, Architectures, and Takeaways from Practitioners}},
     journal = {IEEE Software},
     year = {2026},
     month = mar,
     doi = {10.48550/arXiv.2604.00189},
     url = {https://arxiv.org/abs/2604.00189},
     selected = true
   }

4. Software Engineering

   Advancing Research Software Engineering with AI: A Research Framework

   Siamak Farshidi, Kwabena Bennin, Önder Babur, and 3 more authors

   2026

   DOI Bib HTML

   @misc{Farshidi2025Aug,
     author = {Farshidi, Siamak and Bennin, Kwabena and Babur, {\"{O}}nder and Sallou, June and Kassahun, Ayalew and Tekinerdogan, Bedir},
     title = {{Advancing Research Software Engineering with AI: A Research Framework}},
     year = {2026},
     doi = {10.21203/rs.3.rs-7178452/v1},
     url = {https://doi.org/10.21203/rs.3.rs-7178452/v1},
   }

5. Scientific Software

   Technical note: HydroModPy – a Python toolbox for deploying catchment-scale shallow groundwater models

   Alexandre Gauvain, Ronan Abhervé, Bastien Boivin, and 24 more authors

   EGUsphere [preprint], 2026

   Under review for Hydrology and Earth System Sciences (HESS)

   DOI Bib HTML

   @article{Gauvain2026HydroModPy,
     author = {Gauvain, Alexandre and Abhervé, Ronan and Boivin, Bastien and Roques, Clément and Le Mesnil, Martin and Coche, Alexandre and Babey, Tristan and Marçais, Jean and Bouchez, Camille and Leray, Sarah and Marti, Etienne and Bresciani, Etienne and Figueroa, Ronny and Pélissier, Mathias and Guillaumot, Luca and Touzeau, Théa and Issolah, Imene and Maugan, Enzo and Bagagnan, Rock S. and Vautier, Camille and Sallou, June and Bourcier, Johan and Combemale, Benoit and Brunner, Philip and Longuevergne, Laurent and Aquilina, Luc and de Dreuzy, Jean-Raynald},
     title = {Technical note: HydroModPy -- a Python toolbox for deploying catchment-scale shallow groundwater models},
     journal = {EGUsphere [preprint]},
     year = {2026},
     doi = {10.5194/egusphere-2026-868},
     url = {https://egusphere.copernicus.org/preprints/2026/egusphere-2026-868/},
     note = {Under review for Hydrology and Earth System Sciences (HESS)},
   }

2025

1. Green AI

   Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations

   Pooja Rani, Jan-Andrea Bard, June Sallou, and 3 more authors

   arXiv preprint, 2025

   Ongoing work: manuscript under revision for resubmission

   DOI Bib HTML PDF

   @article{Rani2025Mar,
     author = {Rani, Pooja and Bard, Jan-Andrea and Sallou, June and Boll, Alexander and Kehrer, Timo and Bacchelli, Alberto},
     title = {{Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations}},
     journal = {arXiv preprint},
     year = {2025},
     doi = {10.48550/arXiv.2503.20126},
     url = {https://arxiv.org/abs/2503.20126},
     note = {Ongoing work: manuscript under revision for resubmission},
   }

2. Green AI

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

   Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby, and 22 more authors

   SIGSOFT Softw. Eng. Notes, Jul 2025

   DOI Bib HTML

   @article{Cruz2025Jul,
     author = {Cruz, Luís and Fernandes, João Paulo and Kirkeby, Maja H. and Mart{\ifmmode\acute{\imath}\else\'{\i}\fi}nez-Fern{\ifmmode\acute{a}\else\'{a}\fi}ndez, Silverio and Sallou, June and Anwar, Hina and Barba Roque, Enrique and Bogner, Justus and Casta{\ifmmode\tilde{n}\else\~{n}\fi}o, Joel and Castor, Fernando and Chasmawala, Aadil and Cunha, Sim{\ifmmode\tilde{a}\else\~{a}\fi}o and Feitosa, Daniel and Gonz{\ifmmode\acute{a}\else\'{a}\fi}lez, Alexandra and Jedlitschka, Andreas and Lago, Patricia and Muccini, Henry and Oprescu, Ana and Rani, Pooja and Saraiva, Jo{\ifmmode\tilde{a}\else\~{a}\fi}o and Sarro, Federica and Selvan, Raghavendra and Vaidhyanathan, Karthik and Verdecchia, Roberto and Yamshchikov, Ivan P.},
     title = {{Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices}},
     journal = {SIGSOFT Softw. Eng. Notes},
     volume = {50},
     number = {3},
     pages = {14--23},
     year = {2025},
     month = jul,
     issn = {0163-5948},
     publisher = {Association for Computing Machinery},
     doi = {10.1145/3743095.3743099},
     url = {https://doi.org/10.1145/3743095.3743099},
     selected = true
   }

3. Green AI

   Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy

   Lorena Poenaru-Olaru, June Sallou, Luis Cruz, and 2 more authors

   In 2025 11th International Conference on ICT for Sustainability (ICT4S), 2025

   DOI Bib HTML

   @incollection{Poenaru-Olaru,
     author = {Poenaru-Olaru, Lorena and Sallou, June and Cruz, Luis and Rellermeyer, Jan S. and van Deursen, Arie},
     title = {{Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy}},
     booktitle = {{2025 11th International Conference on ICT for Sustainability (ICT4S)}},
     journal = {Published in: 2025 11th International Conference on ICT for Sustainability (ICT4S)},
     pages = {09--13},
     year = {2025},
     publisher = {IEEE},
     doi = {10.1109/ICT4S68164.2025.00019},
   }

2024

1. Software Engineering

   Breaking the Silence: the Threats of Using LLMs in Software Engineering

   June Sallou, Thomas Durieux, and Annibale Panichella

   In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering: New Ideas and Emerging Results, Apr 2024

   DOI Bib HTML

   @incollection{Sallou2024Apr,
     author = {Sallou, June and Durieux, Thomas and Panichella, Annibale},
     title = {{Breaking the Silence: the Threats of Using LLMs in Software Engineering}},
     booktitle = {{Proceedings of the IEEE/ACM 46th International Conference on Software Engineering: New Ideas and Emerging Results}},
     pages = {102--106},
     year = {2024},
     month = apr,
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3639476.3639764},
     url = {https://doi.org/10.1145/3639476.3639764},
   }

2. Green AI

   Green AI in Action: Strategic Model Selection for Ensembles in Production

   Nienke Nijkamp, June Sallou, Niels Heijden, and 1 more author

   In Proceedings of the 1st ACM International Conference on AI-Powered Software, Jul 2024

   DOI Bib HTML

   @incollection{Nijkamp2024Jul,
     author = {Nijkamp, Nienke and Sallou, June and van der Heijden, Niels and Cruz, Lu{\ifmmode\acute{\imath}\else\'{\i}\fi}s},
     title = {{Green AI in Action: Strategic Model Selection for Ensembles in Production}},
     booktitle = {{Proceedings of the 1st ACM International Conference on AI-Powered Software}},
     pages = {50--58},
     year = {2024},
     month = jul,
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3664646.3664763},
     url = {https://doi.org/10.1145/3664646.3664763},
   }

2023

1. Green AI

   A systematic review of Green AI

   Roberto Verdecchia, June Sallou, and Luís Cruz

   WIREs Data Min. Knowl. Discovery, Jul 2023

   DOI Bib HTML PDF

   @article{Verdecchia2023Jul,
     author = {Verdecchia, Roberto and Sallou, June and Cruz, Luís},
     title = {{A systematic review of Green AI}},
     journal = {WIREs Data Min. Knowl. Discovery},
     volume = {13},
     number = {4},
     pages = {e1507},
     year = {2023},
     month = jul,
     issn = {1942-4787},
     publisher = {John Wiley {\&} Sons, Ltd},
     doi = {10.1002/widm.1507},
     selected = true,
   }

2. Green AI

   Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI

   Tim Yarally, Luís Cruz, Daniel Feitosa, and 2 more authors

   In 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2023

   Best Paper Award DOI Bib HTML

   Best Paper Award [CAIN’23]

   @incollection{Yarally,
     author = {Yarally, Tim and Cruz, Luís and Feitosa, Daniel and Sallou, June and van Deursen, Arie},
     title = {{Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI}},
     booktitle = {{2023 IEEE/ACM 2nd International Conference on AI Engineering {\textendash} Software Engineering for AI (CAIN)}},
     journal = {Published in: 2023 IEEE/ACM 2nd International Conference on AI Engineering {\textendash} Software Engineering for AI (CAIN)},
     pages = {15--16},
     publisher = {IEEE},
     year = {2023},
     doi = {10.1109/CAIN58948.2023.00012},
   }

3. Green AI

   Batching for Green AI - An Exploratory Study on Inference

   Tim Yarally, Luís Cruz, Daniel Feitosa, and 2 more authors

   In 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2023

   DOI Bib HTML

   @inproceedings{YCFSD23,
     author = {Yarally, Tim and Cruz, Luís and Feitosa, Daniel and Sallou, June and van Deursen, Arie},
     booktitle = {2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)},
     title = {Batching for Green AI - An Exploratory Study on Inference},
     year = {2023},
     volume = {},
     number = {},
     pages = {112-119},
     doi = {10.1109/SEAA60479.2023.00026},
   }

4. Green AI

   Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

   Lorena Poenaru-Olaru, June Sallou, Luis Cruz, and 2 more authors

   In 2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS), 2023

   DOI Bib HTML

   @incollection{Poenaru-Olaru2023,
     author = {Poenaru-Olaru, Lorena and Sallou, June and Cruz, Luis and Rellermeyer, Jan S. and van Deursen, Arie},
     title = {{Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques}},
     booktitle = {{2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS)}},
     journal = {Published in: 2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software (GREENS)},
     pages = {14},
     year = {2023},
     publisher = {IEEE},
     doi = {10.1109/GREENS59328.2023.00009},
   }

5. Green AI

   The Two Faces of AI in Green Mobile Computing: A Literature Review

   Wander Siemers, June Sallou, and Luís Cruz

   In 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2023

   DOI Bib HTML PDF

   @inproceedings{SSC23,
     author = {Siemers, Wander and Sallou, June and Cruz, Luís},
     booktitle = {2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)},
     title = {The Two Faces of AI in Green Mobile Computing: A Literature Review},
     year = {2023},
     volume = {},
     number = {},
     pages = {301-309},
     doi = {10.1109/SEAA60479.2023.00053},
   }

2022

1. Green AI

   Data-Centric Green AI An Exploratory Empirical Study

   Roberto Verdecchia, Luís Cruz, June Sallou, and 3 more authors

   In 2022 International Conference on ICT for Sustainability (ICT4S), 2022

   DOI Bib HTML

   @incollection{VLS+22,
     author = {Verdecchia, Roberto and Cruz, Luís and Sallou, June and Lin, Michelle and Wickenden, James and Hotellier, Estelle},
     title = {{Data-Centric Green AI An Exploratory Empirical Study}},
     booktitle = {{2022 International Conference on ICT for Sustainability (ICT4S)}},
     journal = {Published in: 2022 International Conference on ICT for Sustainability (ICT4S)},
     pages = {13--17},
     publisher = {IEEE},
     year = {2022},
     doi = {10.1109/ICT4S55073.2022.00015},
   }

2. Scientific Software

   On the Role of Computer Languages in Scientific Computing

   Dorian Leroy, June Sallou, Johann Bourcier, and 1 more author

   Computing in Science & Engineering, 2022

   DOI Bib

   @article{Leroy2022,
     author = {Leroy, Dorian and Sallou, June and Bourcier, Johann and Combemale, Benoit},
     journal = {Computing in Science & Engineering},
     title = {On the Role of Computer Languages in Scientific Computing},
     year = {2022},
     volume = {24},
     number = {4},
     pages = {55-59},
     doi = {10.1109/MCSE.2022.3221672},
   }

2021

1. Scientific Software

   When Scientific Software Meets Software Engineering

   Dorian Leroy, June Sallou, Johann Bourcier, and 1 more author

   Computer, 2021

   DOI Bib HTML

   @article{leroy2021,
     author = {Leroy, Dorian and Sallou, June and Bourcier, Johann and Combemale, Benoit},
     doi = {10.1109/MC.2021.3102299},
     journal = {Computer},
     number = {12},
     pages = {60-71},
     title = {When Scientific Software Meets Software Engineering},
     volume = {54},
     year = {2021},
   }

2020

1. Software Engineering

   A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems

   Benoit Combemale, Jorg Kienzle, Gunter Mussbacher, and 22 more authors

   IEEE Software, May 2020

   DOI Bib

   @article{Combemale2020May,
     author = {Combemale, Benoit and Kienzle, Jorg and Mussbacher, Gunter and Ali, Hyacinth and Amyot, Daniel and Bagherzadeh, Mojtaba and Batot, Edouard and Bencomo, Nelly and Benni, Benjamin and Bruel, Jean-Michel and Cabot, Jordi and Cheng, Betty H.C. and Collet, Philippe and Engels, Gregor and Heinrich, Robert and Jezequel, Jean-Marc and Koziolek, Anne and Mosser, Sebastien and Reussner, Ralf and Sahraoui, Houari and Saini, Rijul and Sallou, June and Stinckwich, Serge and Syriani, Eugene and Wimmer, Manuel},
     title = {{A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems}},
     journal = {IEEE Software},
     volume = {38},
     number = {4},
     pages = {71--84},
     year = {2020},
     month = may,
     publisher = {IEEE},
     doi = {10.1109/MS.2020.2995125},
   }

2. Scientific Software

   Loop Aggregation for Approximate Scientific Computing

   June Sallou, Alexandre Gauvain, Johann Bourcier, and 2 more authors

   In Computational Science – ICCS 2020, 2020

   Abs Bib

   Trading off some accuracy for better performances in scientific computing is an appealing approach to ease the exploration of various alternatives on complex simulation models. Existing approaches involve the application of either time-consuming model reduction techniques or resource-demanding statistical approaches. Such requirements prevent any opportunistic model exploration, e.g., exploring various scenarios on environmental models. This limits the ability to analyse new models for scientists, to support trade-off analysis for decision-makers and to empower the general public towards informed environmental intelligence. In this paper, we present a new approximate computing technique, aka. loop aggregation, which consists in automatically reducing the main loop of a simulation model by aggregating the corresponding spatial or temporal data. We apply this approximate scientific computing approach on a geophysical model of a hydraulic simulation with various input data. The experimentation demonstrates the ability to drastically decrease the simulation time while preserving acceptable results with a minimal set-up. We obtain a median speed-up of 95.13% and up to 99.78% across all the 23 case studies.

   @inproceedings{Sallou2020,
     address = {Cham},
     author = {Sallou, June and Gauvain, Alexandre and Bourcier, Johann and Combemale, Benoit and de Dreuzy, Jean-Raynald},
     booktitle = {Computational Science -- ICCS 2020},
     editor = {Krzhizhanovskaya, Valeria V. and Závodszky, Gábor and Lees, Michael H. and Dongarra, Jack J. and Sloot, Peter M. A. and Brissos, Sérgio and Teixeira, João},
     isbn = {978-3-030-50417-5},
     pages = {141--155},
     publisher = {Springer International Publishing},
     title = {Loop Aggregation for Approximate Scientific Computing},
     year = {2020},
   }