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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
Améliorer les performances de l'industrie logicielle par ...
2009-06-08 · via cs.SE updates on arXiv.org

Actual organization are structured and act with the help of their information systems. In spite of considerable progresses made by computer technology, we note that actors are very often critical on their information systems. Difficulties to product specifications enough detailed for functional profile and interpretable by information system expert is one of reason of this gap between hopes and reality. Our proposition wants to get over this obstacle by organizing user requirements in a common language of operational profile and technical expert.-- Les organisations actuelles se structurent et agissent en s'appuyant sur leurs systèmes d'information. Malgré les progrès considérables réalisés par la technologie informatique, on constate que les acteurs restent très souvent critiques par rapport à leur systèmes d'information. Une des causes de cet écart entre les espoirs et la réalité trouve sa source dans la difficulté à produire un cahier des charges suffisamment détaillé pour les opérationnels et interprétable par les spécialistes des systèmes d'information. Notre proposition vise à surmonter cet obstacle en organisant l'expression des besoins dans un langage commun aux opérationnels et aux experts techniques. Pour cela, le langage proposé pour exprimer les besoins est basé sur la notion de but. L'ingénierie dirigée par les modèles est présente à toute les étapes, c'est-à-dire au moment de la capture et de l'interprétation.