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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
Visualizing Cloud-native Applications with KubeDiagrams
Philippe Merle, Fabio Petrillo · 2025-05-29 · via cs.SE updates on arXiv.org

Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability. As the de facto orchestration standard, Kubernetes enables this abstraction, but its declarative configuration model makes the architectural understanding difficult. Developers, operators, and architects struggle to form accurate mental models from raw manifests, Helm charts, or cluster state descriptions. We introduce KubeDiagrams, an open-source tool that transforms Kubernetes manifests into architecture diagrams. By grounding our design in a user-centered study of real-world visualization practices, we identify the specific challenges Kubernetes users face and map these to concrete design requirements. KubeDiagrams integrates seamlessly with standard Kubernetes artifacts, preserves semantic fidelity to core concepts, and supports extensibility and automation. We detail the tool's architecture, visual encoding strategies, and extensibility mechanisms. Three case studies illustrate how KubeDiagrams enhances system comprehension and supports architectural reasoning in distributed cloud-native systems. KubeDiagrams addresses concrete pain points in Kubernetes-based DevOps practices and is valued for its automation, clarity, and low-friction integration into real-world tooling environments.