research-article

Learn to Code Sustainably: An Empirical Study on Green Code Generation

Authors:

Tina Vartziotis,

Ippolyti Dellatolas,

George Dasoulas,

Maximilian Schmidt,

Florian Schneider,

Sotirios Kotsopoulos,

Michael KeckeisenAuthors Info & Claims

LLM4Code '24: Proceedings of the 1st International Workshop on Large Language Models for Code

Pages 30 - 37

https://doi.org/10.1145/3643795.3648394

Published: 10 September 2024 Publication History

Abstract

The increasing use of information technology has led to a significant share of energy consumption and carbon emissions from data centers. These contributions are expected to rise with the growing demand for big data analytics, increasing digitization, and the development of large artificial intelligence (AI) models. The need to address the environmental impact of software development has led to increased interest in green (sustainable) coding and claims that the use of AI models can lead to energy efficiency gains. Here, we provide an empirical study on green code and an overview of green coding practices, as well as metrics used to quantify the sustainability awareness of AI models. In this framework, we evaluate the sustainability of auto-generated code. The auto-generated code considered in this study is produced by generative commercial AI language models, GitHub Copilot, OpenAI ChatGPT-3, and Amazon CodeWhisperer. Within our methodology, in order to quantify the sustainability awareness of these AI models, we propose a definition of the code's "green capacity", based on certain sustainability metrics. We compare the performance and green capacity of human-generated code and code generated by the three AI language models in response to easy-to-hard problem statements. Our findings shed light on the current capacity of AI models to contribute to sustainable software development.

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Cited By

Vartziotis TSchmidt MDasoulas GDellatolas IAttademo SLe VWiechmann AHoffmann TKeckeisen MKotsopoulos S(2024)Carbon Footprint Evaluation of Code Generation through LLM as a Service2024 Stuttgart International Symposium on Automotive and Engine Technology10.1007/978-3-658-45010-6_15(230-241)Online publication date: 30-Jun-2024
https://doi.org/10.1007/978-3-658-45010-6_15

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Learn to Code Sustainably: An Empirical Study on Green Code Generation

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cover image ACM Conferences

LLM4Code '24: Proceedings of the 1st International Workshop on Large Language Models for Code

April 2024

144 pages

ISBN:9798400705793

DOI:10.1145/3643795

Copyright © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

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Published: 10 September 2024

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LLM4Code '24: 1st International Workshop on Large Language Models for Code

April 20, 2024

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Cited By

Vartziotis TSchmidt MDasoulas GDellatolas IAttademo SLe VWiechmann AHoffmann TKeckeisen MKotsopoulos S(2024)Carbon Footprint Evaluation of Code Generation through LLM as a Service2024 Stuttgart International Symposium on Automotive and Engine Technology10.1007/978-3-658-45010-6_15(230-241)Online publication date: 30-Jun-2024
https://doi.org/10.1007/978-3-658-45010-6_15

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