Potentials of Green Coding -- Findings and Recommendations for Industry, Education and Science -- Extended Paper

• URL: http://arxiv.org/abs/2402.18227v1
• Date: Wed, 28 Feb 2024 10:48:56 GMT
• Title: Potentials of Green Coding -- Findings and Recommendations for Industry, Education and Science -- Extended Paper
• Authors: Dennis Junger (HTW Berlin), Max Westing (Umwelt-Campus Birkenfeld), Christopher P. Freitag (HTW Berlin), Achim Guldner (Umwelt-Campus Birkenfeld), Konstantin Mittelbach (HTW Berlin), Kira Oberg\"oker (Umwelt-Campus Birkenfeld), Sebastian Weber (Umwelt-Campus Birkenfeld), Stefan Naumann (Umwelt-Campus Birkenfeld), Volker Wohlgemuth (HTW Berlin)
• Abstract summary: We conduct an analysis to gather and present existing literature on three research questions relating to the production of ecologically sustainable software. We compile the approaches to Green Coding and Green Software Engineering that have been published since 2010. We consider ways to integrate the findings into existing industrial processes and higher education curricula to influence future development in an environmentally friendly way.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Progressing digitalization and increasing demand and use of software cause rises in energy- and resource consumption from information and communication technologies (ICT). This raises the issue of sustainability in ICT, which increasingly includes the sustainability of the software products themselves and the art of creating sustainable software. To this end, we conducted an analysis to gather and present existing literature on three research questions relating to the production of ecologically sustainable software ("Green Coding") and to provide orientation for stakeholders approaching the subject. We compile the approaches to Green Coding and Green Software Engineering (GSE) that have been published since 2010. Furthermore, we considered ways to integrate the findings into existing industrial processes and higher education curricula to influence future development in an environmentally friendly way.

Related papers

• Green Federated Learning: A new era of Green Aware AI [11.536626349203361]
  Federated Learning (FL) presents new opportunities to address this need. It's crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works.
  arXiv  Detail & Related papers  (2024-09-19T09:54:18Z)
• Environmentally Sustainable Software Design and Development: A Systematic Literature Review [1.6071754144962787]
  The ICT sector is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. We conduct a systematic literature review on state-of-the-art proposals for designing and developing sustainable software.
  arXiv  Detail & Related papers  (2024-07-29T11:24:11Z)
• Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
  quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
  arXiv  Detail & Related papers  (2024-07-16T11:21:30Z)
• The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
  Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
  arXiv  Detail & Related papers  (2024-06-24T15:55:49Z)
• Towards Green AI: Current status and future research [0.3749861135832072]
  We aim to broaden the discourse on Green AI by investigating the current status of approaches to both environmental assessment and ecodesign of AI systems. We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI. We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI.
  arXiv  Detail & Related papers  (2024-05-01T08:10:01Z)
• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation [7.8273713434806345]
  We evaluate the sustainability of auto-generate codes produced by generative commercial AI language models. We compare the performance and green capacity of human-generated code and code generated by the three AI language models.
  arXiv  Detail & Related papers  (2024-03-05T22:12:01Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods [58.31888171187044]
  We present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. We present initial results demonstrating that ML models trained with our data can reliably predict the sustainability label of products.
  arXiv  Detail & Related papers  (2022-07-21T19:59:42Z)
• Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review [0.0]
  Circular economy (CE) aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. This study explores the adoption and integration of applied AI techniques in CE.
  arXiv  Detail & Related papers  (2022-04-01T07:05:13Z)
• Data Science for Engineers: A Teaching Ecosystem [59.00739310930656]
  We describe an ecosystem for teaching data science to engineers at the Faculty of Physical and Mathematical Sciences, Universidad de Chile. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments. By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems.
  arXiv  Detail & Related papers  (2021-01-14T14:17:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.