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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
Design and Evaluation of a Microservices Cloud Framework ...
Biman Barua, M. Shamim Kaiser · 2025-05-20 · via cs.SE updates on arXiv.org

Handling online travel agents globally requires efficient and flexible software solution architectures. When it needs to handle thousands of agents and billions of clients data globally. Microservices architecture is used to break down a large program into numerous, smaller services which can run individually and perform individual tasks. This paper analyses and integrates a unique Microservices Cloud Framework designed to support Online Travel Platforms (MCF-OTP). MCF-OTPs main goal is to increase the performance, flexibility, and maintenance of online travel platforms via cloud computing and microservice technologies. Large-scale travel apps, including managing numerous data sources, dealing with traffic peaks, and providing fault tolerance, can be addressed by the suggested framework. The framework increases good interpretation between flawless data synchronization, microservices, and dynamic scaling based on demand technology. An organization framework that optimizes service borders and minimizes inter-service dependencies is recommended. Thus, this can result in elevated development adaptability. In this research, the principal goal is to evaluate MCF-OTPs efficiency using the indicators of fault tolerance and response time. It is indicated by the findings that the MCF-OTP structure excels traditional monolithic designs in terms of dependability and scalability, managing traffic spikes seamlessly and decreasing downtime. The cost-effective analysis helps ascertain the net gain attained by the startup fees and the ongoing operational costs. The cloud-based environment is used to reduce the fracture cost which also helps to increase the efficiency of resource allocation, according to the research.