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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
Corporate Training in Brazilian Software Engineering: A Q...
Rodrigo Siqueira, Antonio Oliveira, Breno Alves Andrade, Lidiane · 2026-04-07 · via cs.SE updates on arXiv.org

Context: Strategic corporate training is essential for the sustained professional development of software engineers. However, there is a knowledge gap regarding the factors that drive quality and effectiveness of such training from the professionals' perspective, and no validated instrument exists for assessing these factors in the software engineering (SE) domain. Objective: This study aims to quantitatively analyze which factors influence SE professionals' perceptions of corporate training quality and effectiveness. Method: A quantitative survey was conducted with 282 Brazilian SE professionals. A structured questionnaire was developed and polychoric correlation was adopted for data analysis. Results: Three tightly correlated factors (cognitive engagement, variety of activities, and instructor performance) emerged as the strongest predictors of perceived training quality and effectiveness. Mandatory participation significantly reduces motivation and perceived training quality. Perceived impact on personal time proved to be largely independent of training quality. These findings are consistent with the general training effectiveness literature. Conclusions: Training effectiveness in the SE context is predominantly determined by three factors: cognitive engagement, variety of activities, and instructor performance. Mandatory participation negatively influences motivation, perceived relevance, and perceived training quality, while also amplifying the perception of time burden. The consistency with the general literature suggests that software organizations do not need to reinvent training design principles and can apply established guidelines with confidence. Salas and Cannon-Bowers' framework produced coherent results in the SE context, making it a promising candidate for future psychometric validation.