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cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Environmental Impact of CI/CD Pipelines
Nuno Saavedra, Alexandra Mendes, João F. Ferreira · 2025-10-30 · via cs.SE updates on arXiv.org

CI/CD pipelines are widely used in software development, yet their environmental impact, particularly carbon and water footprints (CWF), remains largely unknown to developers, as CI service providers typically do not disclose such information. With the growing environmental impact of cloud computing, understanding the CWF of CI/CD services has become increasingly important. This work investigates the CWF of using GitHub Actions, focusing on open-source repositories where usage is free and unlimited for standard runners. We build upon a methodology from the Cloud Carbon Footprint framework and we use the largest dataset of workflow runs reported in the literature to date, comprising over 2.2 million workflow runs from more than 18,000 repositories. Our analysis reveals that the GitHub Actions ecosystem results in a substantial CWF. Our estimates for the carbon footprint in 2024 range from 150.5 MTCO2e in the most optimistic scenario to 994.9 MTCO2e in the most pessimistic scenario, while the water footprint ranges from 1,989.6 to 37,664.5 kiloliters. The most likely scenario estimates are 456.9 MTCO2e for carbon footprint and 5,738.2 kiloliters for water footprint. To provide perspective, the carbon footprint in the most likely scenario is equivalent to the carbon captured by 7,615 urban trees in a year, and the water footprint is comparable to the water consumed by an average American family over 5,053 years. We explore strategies to mitigate this impact, primarily by reducing wasted computational resources. Key recommendations include deploying runners in regions whose energy production has a low environmental impact such as France and the United Kingdom, implementing stricter deactivation policies for scheduled runs and aligning their execution with periods when the regional energy mix is more environmentally favorable, and reducing the size of repositories.