Vincenzo De Martino🚀 Starting the new year on a high note! 🎉 Our paper "Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study" has been accepted at #ICSE-SEIS2025!
We analyze 168 ML projects to uncover how developers adopt green architectural tactics for sustainable software. Huge thanks to @silveriomf & Fabio Palomba!
📄 Pre-Print here: https://arxiv.org/abs/2410.06708
#GreenAI #SoftwareEngineering #EmpiricalSE
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 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.
Stefan Wagner
