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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
Human Error Management in Requirements Engineering: Shoul...
Sweta Mahaju, Jeffrey C. Carver, Gary L. Bradshaw · 2023-04-06 · via cs.SE updates on arXiv.org

Context: Software development is human-centric and vulnerable to human error. Human errors are errors in the human thought process. To ensure software quality, practitioners must understand how to manage these human errors. Organizations often change the requirements engineering process to prevent human errors from occurring or to mitigate the harm caused when those errors do occur. While there are studies on human error management in other disciplines, research on the prevention and mitigation of human errors in software engineering, and requirements engineering specifically, are limited. The software engineering studies do not provide strong results about the types of changes that are most effective in requirements engineering. Objective: The goal of this paper is to develop a taxonomy of human error prevention and mitigation strategies based on data from requirements engineering professionals. Method: We performed a qualitative analysis of two practitioner surveys on requirements engineering practices to identify and classify strategies for the prevention and mitigation of human errors. Results: We organized the human error management strategies into a taxonomy based on whether they primarily affect People, Processes, or the Environment. Inside each high-level category, we further organized the strategies into low-level classes. More than 50% of the reported strategies require a change in Process, 23% require a change in Environment, 21% require a change in People, with the remaining 5% too ambiguous to classify. In addition, more than 50\% of the strategies focus on Management activities. Conclusions: The Human Error Management Taxonomy provides a systematic classification and organization of strategies for prevention and mitigation of human errors in requirements engineering. This systematic organization provides a foundation upon which research can build.