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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
An Eye for Trust: An Exploration of Developers' Trust Per...
Sara Yabesi, Mahta Amini, Jelena Ristic, Zohreh Sharafi · 2026-04-10 · via cs.SE updates on arXiv.org

Code reuse is a widespread practice across software development projects, suggesting an inherent trust in the reused code. Yet, there is a lack of a fundamental understanding of developers' trust and how various factors mold their trust-based cognitive processes. Drawing from the psychology of compliance and trust, we present the results of the first controlled experiment (n=37) which uses eye tracking to explore how urgency (represented by code priority level) and reputation (represented by the experience level of the code's author) influence developers' perceptions of code trustworthiness. Our research revealed that the priority assigned to a code patch significantly influenced developers' code review behavior, impacting their evaluation time, cognitive load, and perceived quality. However, the decision to incorporate and implement the code was not affected . Eye tracking data revealed that there were variations in overall visual code scanning and the distribution of attention across identical code patches labeled as written by senior vs. junior developers. Yet, there were no significant performance differences. Moreover, our participants nominate code functionality, quality, and comprehensibility as primary factors in code evaluation. Despite noticeable changes in code review behavior, our participants surprisingly overlooked the substantial influence of urgency and reputation on their decisions to review and reuse code changes. This study takes the next step toward a better understanding of trust in software engineering and may inform future research about code review platforms and guidelines, code reuse, and automated code generation.