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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
Verifying User Interfaces using SPARK Ada: A Case Study o...
Peterson Jean · 2025-09-20 · via cs.SE updates on arXiv.org

The increase in safety and critical systems improved Healthcare. Due to their risk of harm, such systems are subject to stringent guidelines and compliances. These safety measures ensure a seamless experience and mitigate the risk to end-users. Institutions like the Food and Drug Administration and the NHS, respectively, established international standards and competency frameworks to ensure industry compliance with these safety concerns. Medical device manufacturing is mainly concerned with standards. Consequently, these standards now advocate for better human factors considered in user interaction for medical devices. This forces manufacturers to rely on heavy testing and review to cover many of these factors during development. Sadly, many human factor risks will not be caught until proper testing in real life, which might be catastrophic in the case of an ambulatory device like the T34 syringe pump. Therefore, effort in formal methods research may propose new solutions in anticipating these errors in the early stages of development or even reducing their occurrence based on the use of standard generic model. These generically developed models will provide a common framework for safety integration in industry and may potentially be proven using formal verification mathematical proofs. This research uses SPARK Ada's formal verification tool against a behavioural model of the T34 syringe driver. A Generic Infusion Pump model refinement is explored and implemented in SPARK Ada. As a subset of the Ada language, the verification level of the end prototype is evaluated using SPARK. Exploring potential limitations defines the proposed model's implementation liability when considering abstraction and components of User Interface design in SPARK Ada.