Publications

Data-Centric Green AI An Exploratory Empirical Study

Authors: Roberto Verdecchia, Luís Cruz, June Sallou, Michelle Lin, James Wickenden, Estelle Hotellier

Published in: 2022 International Conference on ICT for Sustainability (ICT4S).

Abstract: The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. In particular, code smells have rarely been studied in this domain. Although machine learning code is usually integrated as a small part of an overarching system, it usually plays an important role in its core functionality. Hence ensuring code quality is quintessential to avoid issues in the long run. This paper proposes and identifies a list of 22 machine learning-specific code smells collected from various sources, including papers, grey literature, GitHub commits, and Stack Overflow posts. We pinpoint each smell with a description of its context, potential issues in the long run, and proposed solutions. In addition, we link them to their respective pipeline stage and the evidence from both academic and grey literature. The code smell catalog helps data scientists and developers produce and maintain high-quality machine learning application code.

@INPROCEEDINGS{verdecchia2022data,
author={Roberto Verdecchia and Luís Cruz and June Sallou and Michelle Lin and James Wickenden and Estelle Hotellier},
booktitle={2022 International Conference on ICT for Sustainability (ICT4S)}, 
title={Data-Centric Green AI An Exploratory Empirical Study}, 
year={2022}}

Read me: Arxiv. Preprint. DOI: 10.1109/ICT4S55073.2022.00015. 👩‍💻Replication package.