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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 and Personality Profiles of Global Software...
Sherlock A. Licorish, Stephen G. MacDonell · 2021-02-25 · via cs.SE updates on arXiv.org

Context: Prior research has established that a small proportion of individuals dominate team communication during global software development. It is not known, however, how these members' contributions affect their teams' knowledge diffusion process, or whether their personality profiles are responsible for their dominant presence. Objective: We set out to address this gap through the study of repository artifacts. Method: Artifacts from ten teams were mined from the IBM Rational Jazz repository. We employed social network analysis (SNA) to group practitioners into two clusters, Top Members and Others, based on the numbers of messages they communicated and their engagement in task changes. SNA metrics (density, in-degree and closeness) were then used to study practitioners' importance in knowledge diffusion. Thereafter, we performed psycholinguistic analysis on practitioners' messages using linguistic dimensions that had been previously correlated with the Big Five personality profiles. Results: For our sample of 146 practitioners we found that Top Members occupied critical roles in knowledge diffusion, and demonstrated more openness to experience than the Others. Additionally, all personality profiles were represented during teamwork, although openness to experience, agreeableness and extroversion were particularly evident. However, no specific personality predicted members' involvement in knowledge diffusion. Conclusion: Task assignment that promotes highly connected team communication networks may mitigate tacit knowledge loss in global software teams. Additionally, while members expressing openness to experience are likely to be particularly driven to perform, this is not entirely responsible for a global team's success.