Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study
As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensive nature of training and inference processes. Green AI advocates for reducing computational demands while still maintain...
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| Vydáno v: | IEEE/ACM International Conference on Software Engineering: Software Engineering in Society (Online) s. 135 - 139 |
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| Jazyk: | angličtina |
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IEEE
27.04.2025
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| ISSN: | 2832-7616 |
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| Abstract | As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensive nature of training and inference processes. Green AI advocates for reducing computational demands while still maintaining accuracy. Although various strategies for creating sustainable ML systems have been identified, their real-world implementation is still underexplored. This paper addresses this gap by studying 168 open-source ML projects on GitHub. It employs a novel large language model (LLM)-based mining mechanism to identify and analyze green strategies. The findings reveal the adoption of established tactics that offer significant environmental benefits. This provides practical insights for developers and paves the way for future automation of sustainable practices in ML systems. |
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| AbstractList | As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to the resource-intensive nature of training and inference processes. Green AI advocates for reducing computational demands while still maintaining accuracy. Although various strategies for creating sustainable ML systems have been identified, their real-world implementation is still underexplored. This paper addresses this gap by studying 168 open-source ML projects on GitHub. It employs a novel large language model (LLM)-based mining mechanism to identify and analyze green strategies. The findings reveal the adoption of established tactics that offer significant environmental benefits. This provides practical insights for developers and paves the way for future automation of sustainable practices in ML systems. |
| Author | De Martino, Vincenzo Martinez-Fernandez, Silverio Palomba, Fabio |
| Author_xml | – sequence: 1 givenname: Vincenzo surname: De Martino fullname: De Martino, Vincenzo email: vdemartino@unisa.it organization: University of Salerno,Software Engineering (SeSa) Lab,Italy – sequence: 2 givenname: Silverio surname: Martinez-Fernandez fullname: Martinez-Fernandez, Silverio email: silverio.martinez@upc.edu organization: Universitat Politècnica de Catalunya,Spain – sequence: 3 givenname: Fabio surname: Palomba fullname: Palomba, Fabio email: fpalomba@unisa.it organization: University of Salerno,Software Engineering (SeSa) Lab,Italy |
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| Snippet | As machine learning (ML) and artificial intelligence (AI) technologies become more widespread, concerns about their environmental impact are increasing due to... |
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| SubjectTerms | Automation Data mining Empirical Software Engineering Green AI Green products Large language models Machine learning Machine Learning-Enabled Systems Software development management Software engineering Software Sustainability Sustainable development System software Training |
| Title | Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study |
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