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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
Qualitative Analysis of the Teacher and Student Roles in ...
Linus Ververs, Trang Linh Lam, Lutz Prechelt · 2025-07-14 · via cs.SE updates on arXiv.org

Background: Pair programming is a well-established and versatile agile practice. Previous research has found it to involve far more different roles than the well-known Driver and Observer/Navigator roles. Pair programming often involves heavy knowledge transfer from mainly one partner to the other. Objective: Understand how to fill the ensuing Teacher and Student roles well (positive behavioral patterns). Understand how they may break (anti-patterns). Method: Open coding and axial coding of 17 recorded pair programming sessions with 18 developers from 5 German software companies, plus interviews with 6 different developers from 4 other German companies. Results: We describe six facets of effective Teacher behavior (e.g. Prioritizing Knowledge Transfer) and two facets of effective Student behavior (e.g. Expressing Knowledge Wants). We describe four harmful would-be-Teacher behaviors (e.g. Pushing Unwanted Knowledge), and one harmful would-be-Student behavior (Failing to Provide a Back Channel). Conclusions: The role facets can serve as learning goals and to-do list for developers who want to develop strong pair programming skill. The anti-patterns can serve as warnings for one's own general behavior and as triggers for immediate meta-discussion if they occur within a pairing session.