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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
Software Frugality in an Accelerating World: the Case of ...
Quentin Perez, Romain Lefeuvre, Thomas Degueule, Olivier Barais, · 2024-10-21 · via cs.SE updates on arXiv.org

The acceleration of software development and delivery requires rigorous continuous testing and deployment of software systems, which are being deployed in increasingly diverse, complex, and dynamic environments. In recent years, the popularization of DevOps and integrated software forges like GitLab and GitHub has largely democratized Continuous Integration (CI) practices for a growing number of software. However, this trend intersects significantly with global energy consumption concerns and the growing demand for frugality in the Information and Communication Technology (ICT) sector. CI pipelines typically run in data centers which contribute significantly to the environmental footprint of ICT, yet there is little information available regarding their environmental impact. This article aims to bridge this gap by conducting the first large-scale analysis of the energy footprint of CI pipelines implemented with GitHub Actions and to provide a first overview of the energy impact of CI. We collect, instrument, and reproduce 838 workflows from 396 Java repositories hosted on GitHub to measure their energy consumption. We observe that the average unitary energy cost of a pipeline is relatively low, at 10 Wh. However, due to repeated invocations of these pipelines in real settings, the aggregated energy consumption cost per project is high, averaging 22 kWh. When evaluating CO2 emissions based on regional Wh-to-CO2 estimates, we observe that the average aggregated CO2 emissions are significant, averaging 10.5 kg. To put this into perspective, this is akin to the emissions produced by driving approximately 100 kilometers in a typical European car (110 gCO2/km). In light of our results, we advocate that developers should have the means to better anticipate and reflect on the environmental consequences of their CI choices when implementing DevOps practices.