惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 司徒正美
大猫的无限游戏
大猫的无限游戏
腾讯CDC
J
Java Code Geeks
博客园 - 【当耐特】
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
人人都是产品经理
人人都是产品经理
博客园 - Franky
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
美团技术团队
云风的 BLOG
云风的 BLOG
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
雷峰网
雷峰网
B
Blog RSS Feed
博客园_首页
量子位
F
Fortinet All Blogs
罗磊的独立博客
H
Hackread – Cybersecurity News, Data Breaches, AI and More
酷 壳 – CoolShell
酷 壳 – CoolShell
C
Check Point Blog

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
MoCheQoS: Automated Analysis of Quality of Service Proper...
Carlos G. Lopez Pombo, Agustín E. Martinez Suñé, Emilio Tuosto · 2023-11-03 · via cs.SE updates on arXiv.org

We present MoCheQoS, a bounded model checker to analyse (QoS) properties of message-passing systems. Building on the dynamic temporal logic, the choreographic model, and the bounded model checking algorithm defined in our ICTAC 2023 paper, MoCheQoS enables the static analysis of QoS properties of systems built out from the composition of services. We consider QoS properties on measurable application-level attributes as well as resource consumption metrics for example those relating monetary cost to memory usage. The implementation of the tool is accompanied by an experimental evaluation. More precisely, we present two case studies meant to evaluate the applicability of MoCheQoS; the first is based on the AWS cloud while the second analyses a communicating system automatically extracted from code. Additionally, we consider synthetically generated experiments to assess the scalability of MoCheQoS. These experiments showed that our model can faithfully capture and effectively analyse QoS properties in industrial-strength scenarios.