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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
PHiLIP on the HiL: Automated Multi-platform OS Testing wi...
Kevin Weiss, Michel Rottleuthner, Thomas C. Schmidt, Matthias Wä · 2021-07-15 · via cs.SE updates on arXiv.org

Developing an operating system (OS) for low-end embedded devices requires continuous adaptation to new hardware architectures and components, while serviceability of features needs to be assured for each individual platform under tight resource constraints. It is challenging to design a versatile and accurate heterogeneous test environment that is agile enough to cover a continuous evolution of the code base and platforms. This mission is even morehallenging when organized in an agile open-source community process with many contributors such as for the RIOT OS. Hardware in the Loop (HiL) testing and Continuous Integration (CI) are automatable approaches to verify functionality, prevent regressions, and improve the overall quality at development speed in large community projects. In this paper, we present PHiLIP (Primitive Hardware in the Loop Integration Product), an open-source external reference device together with tools that validate the system software while it controls hardware and interprets physical signals. Instead of focusing on a specific test setting, PHiLIP takes the approach of a tool-assisted agile HiL test process, designed for continuous evolution and deployment cycles. We explain its design, describe how it supports HiL tests, evaluate performance metrics, and report on practical experiences of employing PHiLIP in an automated CI test infrastructure. Our initial deployment comprises 22 unique platforms, each of which executes 98 peripheral tests every night. PHiLIP allows for easy extension of low-cost, adaptive testing infrastructures but serves testing techniques and tools to a much wider range of applications.