Environmentally Sustainable Software Design and Development: A Systematic Literature Review
- URL: http://arxiv.org/abs/2407.19901v1
- Date: Mon, 29 Jul 2024 11:24:11 GMT
- Title: Environmentally Sustainable Software Design and Development: A Systematic Literature Review
- Authors: Ornela Danushi, Stefano Forti, Jacopo Soldani,
- Abstract summary: 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.
- Score: 1.6071754144962787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ICT sector, responsible for 2% of global carbon emissions and significant energy consumption, is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. However, the software engineering solutions for designing and developing sustainable software are currently scattered over multiple different pieces of literature, which makes it difficult to consult the body of knowledge on the topic. In this article, we precisely conduct a systematic literature review on state-of-the-art proposals for designing and developing sustainable software. We identify and analyse 65 primary studies by classifying them through a taxonomy aimed at answering the 5W1H questions of environmentally sustainable software design and development. We first provide a reasoned overview and discussion of the existing guidelines, reference models, measurement solutions and techniques for measuring, reducing, or minimising the energy consumption and carbon footprint of software. Ultimately, we identify open challenges and research gaps, offering insights for future work in this field.
Related papers
- Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement [62.94719119451089]
Lingma SWE-GPT series learns from and simulating real-world code submission activities.
Lingma SWE-GPT 72B resolves 30.20% of GitHub issues, marking a significant improvement in automatic issue resolution.
arXiv Detail & Related papers (2024-11-01T14:27:16Z) - 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) - Potentials of Green Coding -- Findings and Recommendations for Industry,
Education and Science -- Extended Paper [0.0]
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.
arXiv Detail & Related papers (2024-02-28T10:48:56Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - 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) - 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) - Sustainable AI: Environmental Implications, Challenges and Opportunities [13.089123643565724]
We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases.
We present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI.
arXiv Detail & Related papers (2021-10-30T23:36:10Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - Software engineering for artificial intelligence and machine learning
software: A systematic literature review [6.681725960709127]
This study aims to investigate how software engineering has been applied in the development of AI/ML systems.
Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
arXiv Detail & Related papers (2020-11-07T11:06:28Z)
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.