CarbonWise：炭素フットプリント推計とコード品質のためのAI駆動フレームワーク

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a reproducible framework for repository-level software carbon estimation and a composite sustainability scoring approach that can be empirically validated.

🏢実務担当者:Offers a practical way for engineering and sustainability teams to baseline CI/CD and codebase carbon footprints and prioritize Green DevOps improvements.

🏛政策担当者:Illustrates how software-sector emissions could be measured and disclosed, informing guidance on digital-sector Scope 3 accounting.

The increasing adoption of cloud computing, distributed systems, continuous integration and continuous deployment (CI/CD), and large-scale software repositories has increased the computational infrastructure required throughout the software development lifecycle. Although software is intangible, its development, storage, testing, execution, deployment, and data-transfer activities depend on physical computing resources and consequently consume energy. CarbonWise is an artificial intelligence (AI)-driven Green Software Engineering platform designed to analyze GitHub repositories and provide repository-specific sustainability insights through a unified developer-oriented environment. The proposed framework integrates GitHub repository analysis, software metrics, energy estimation, carbon-emission estimation, sustainability scoring, CI/CD analysis, dependency analysis, AI-assisted recommendations, interactive analytics, and automated reporting. CarbonWise retrieves repository metadata, programming-language information, repository size, dependencies, development dependencies, file structure, large files, configuration files, GitHub Actions workflows, workflow execution information, and CI runtime. These parameters are processed through a deterministic analysis engine to generate verified repository findings. Energy consumption is estimated using CI runtime, storage requirements, and network-related activity, while associated carbon dioxide equivalent (CO₂e) emissions are estimated using grid carbon intensity. A composite sustainability score combines carbon, CI/CD, dependency, and codebase indicators. The resulting verified findings are then supplied to an AI recommendation module based on the Google Gemini API. Unlike generic AI-based recommendations, CarbonWise uses an evidence-first architecture in which the AI layer reasons over structured and pre-verified repository findings. The platform presents the resulting information through dashboards, analytics, and PDF/CSV reports. The proposed system provides a practical framework for integrating sustainability considerations into everyday software engineering and Green DevOps practices. However, the current energy and carbon values are model-based estimates and should not be interpreted as direct hardware-level measurements. Future work will focus on real-time energy monitoring, cloud-provider integration, region-specific carbon intensity, advanced static analysis, carbon-aware CI/CD, and empirical validation.

• openalex https://doi.org/10.55041/ijsrem67980first seen 2026-10-17 04:47:51