Green Architectural Tactics in ML-enabled Systems: An LLM-based Repository Mining Study

TL;DR

This study investigates the adoption of green AI practices in real-world ML systems, using repository mining and LLMs to identify both known and new sustainable tactics, providing practical insights and automation foundations.

Contribution

It introduces a novel LLM-based method to detect both documented and undocumented green tactics in open-source ML projects, enhancing understanding of sustainable practice adoption.

Findings

Green tactics are variably adopted in practice.

Nine previously undocumented sustainable practices were identified.

Code examples support the implementation of new tactics.

Abstract

Context: The increasing adoption of machine learning (ML) and artificial intelligence (AI) technologies raises growing concerns about their environmental sustainability. Developing and deploying ML-enabled systems is computationally intensive, particularly during training and inference. Green AI has emerged to address these issues by promoting efficiency without sacrificing accuracy. While prior research has proposed catalogs of sustainable practices (i.e., green tactics), there remains limited understanding of their adoption in practice and whether additional, undocumented tactics exist. Objective: This study aims to investigate the extent to which existing sustainable practices are implemented in real-world ML-enabled systems and to identify previously undocumented practices that support environmental sustainability. Method: We conduct a mining software repository study on 205 open-source ML projects on GitHub. To support our analysis, we design a novel mechanism based on large language models (LLMs) capable of identifying both known and new sustainable practices from code repositories. Results: Our findings confirm that green tactics reported in the literature are used in practice, although adoption rates vary. Furthermore, our LLM-based approach reveals nine previously undocumented sustainable practices. Each tactic is supported with code examples to aid adoption and integration. Conclusions: We finally provide insights for practitioners seeking to reduce the environmental impact of ML-enabled systems and offer a foundation for future research in automating the detection and adoption of sustainable practices.