The Green Software Landscape: A Systematic Mapping Study on Evolution, Applications, Software Lifecycle, and Best Practices

• URL: http://arxiv.org/abs/2608.26229v1
• Date: Wed, 26 Aug 2026 16:34:32 GMT
• Title: The Green Software Landscape: A Systematic Mapping Study on Evolution, Applications, Software Lifecycle, and Best Practices
• Abstract summary: We conducted a systematic mapping study of Green SE research published between 2010 and 2024.<n>We collected 390 publications, categorizing them by application domain (e.g., mobile, cloud, AI) and research type (e.g., optimisation study, benchmarking literature review)<n>Our findings indicate that SE conferences host the majority of energy-related literature.
• Score: 7.875933305657834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Energy consumption and climate change have made sustainability critical in Software Engineering (SE), driving the emergence of Green SE. Over the past 15 years, numerous solutions for sustainable software systems have been published by the SE community, offering a rich resource for analyzing the field's evolution. To explore this, we conducted a systematic mapping study of Green SE research published between 2010 and 2024. We collected 390 publications, categorizing them by application domain (e.g., mobile, cloud, AI) and research type (e.g., optimisation study, benchmarking, literature review Additionally, we analyzed a representative subset of 79 papers to classify the key elements-such as hardware, measurement, stability, and replicability-considered during energy measurement experiments. Our findings indicate that SE conferences host the majority of energy-related literature. Notably, Green SE studies surged in popularity starting in 2023, largely driven by AI-related publications. Optimization and benchmarking emerged as the most prevalent research types. Ultimately, we aim to inform the SE community about current approaches to energy concerns, highlight critical experimental practices, and advocate for continued action toward more sustainable software engineering.

Related papers

• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
  We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
  arXiv  Detail & Related papers  (2026-05-18T17:08:26Z)
• AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery [55.70879973230979]
  AutoResearchBench is a benchmark for autonomous scientific literature discovery.<n>Compared to previous benchmarks on agentic web browsing, AutoResearchBench is research-oriented.<n>Even the most powerful LLMs, despite having largely conquered general agentic web-browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research.
  arXiv  Detail & Related papers  (2026-04-28T06:05:17Z)
• Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices [70.24403396375277]
  The "Greening AI with Software Engineering" CECAM-Lorentz workshop was held February 3-7, 2025 in Lausanne, Switzerland.<n>This report presents a research agenda emerging from the workshop.<n>It outlines open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems.
  arXiv  Detail & Related papers  (2025-06-02T15:19:49Z)
• Ten Years of Software Engineering in Society [6.551497209671684]
  We want to use the articles published in the SEIS track as a proxy or example of the research in this field.<n>We conducted a mapping study of the 123 articles published in the SEIS track and among the results identified trends pertaining sustainability, diversity and inclusion, and open-source software.<n>As future work, our aim is to stimulate discussion in the community and we hope to inspire replications of this work in other conference venues.
  arXiv  Detail & Related papers  (2025-05-23T12:19:58Z)
• Systems-of-Systems for Environmental Sustainability: A Systematic Mapping Study [0.7095350526841507]
  This study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation.<n>We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities.<n>Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored.
  arXiv  Detail & Related papers  (2025-02-27T12:00:27Z)
• Carbon-Efficient Software Design and Development: A Systematic Literature Review [1.6071754144962787]
  We conduct a systematic literature review on state-of-the-art proposals for designing and developing carbon-efficient software.<n>We identify and analyse 65 primary studies by classifying them through a taxonomy aimed at answering the 5W1H questions of carbon-efficient software design and development.
  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)
• 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)
• 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)
• A Systematic Review of Green AI [8.465228064780744]
  Green AI is the study of AI environmental sustainability. The topic experienced a considerable growth from 2020 onward. From this review emerges that the time is suitable to adopt other Green AI research strategies.
  arXiv  Detail & Related papers  (2023-01-26T11:41:46Z)

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.