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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
Communication channels in safety analysis: An industrial ...
Yang Wang, Daniel Graziotin, Stefan Kriso, Stefan Wagner · 2018-04-24 · via cs.SE updates on arXiv.org

Context: Safety analysis is a predominant activity in developing safety-critical systems. It is a highly cooperative task among multiple functional departments due to increasingly sophisticated safety-critical systems and close-knit development processes. Communication occurs pervasively. Motivation: Effective communication channels among multiple functional departments influence safety analysis, quality as well as a safe product delivery. However, the use of communication channels during safety analysis is sometimes arbitrary and poses challenges. Objective: Investige the existing communication channels, their usage frequencies, their purposes and challenges during safety analysis in industry.. Method: Multiple case study of experts (survey: 39, interview: 21) in safety-critical companies including software developers, quality engineers and functional safety managers. Direct observations and documentation review were also conducted. Results: Popular communication channels during safety analysis include formal meetings, project coordination tools, documentation and telephone. Email, personal discussion, training, internal communication software and boards are also in use. Training involving safety analysis happens 1-4 times per year, while other aforementioned communication channels happen ranges from 1-4 times per day to 1-4 times per month. We summarise 28 purposes for these communication channels. Communication happens mostly for the purpose of clarifying safety requirements, fixing temporary problems, conflicts and obstacles and sharing safety knowledge. The top challenges are reported. Conclusion: During safety analysis, to use communication channels effectively and avoid challenges, a clear purpose of communication during safety analysis should be established at the beginning. To derive countermeasures of fixing the top 10 challenges are potential next steps.