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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
Gender-dependent Contribution, Code and Creativity in a V...
Isabella Graßl, Gordon Fraser · 2022-08-29 · via cs.SE updates on arXiv.org

Since computer science is still mainly male dominated, academia, industry and education jointly seek ways to motivate and inspire girls, for example by introducing them to programming at an early age. The recent COVID-19 pandemic has forced many such endeavours to move to an online setting. While the gender-dependent differences in programming courses have been studied previously, for example revealing that girls may feel safer in same-sex groups, much less is known about gender-specific differences in online programming courses. In order to investigate whether gender-specific differences can be observed in online courses, we conducted an online introductory programming course for Scratch, in which we observed the gender-specific characteristics of participants with respect to how they interact, their enjoyment, the code they produce, and the creativity exposed by their programs. Overall, we observed no significant differences between how girls participated in all-female vs. mixed groups, and girls generally engaged with the course more actively than boys. This suggests that online courses can be a useful means to avoid gender-dependent group dynamics. However, when encouraging creative freedom in programming, girls and boys seem to fall back to socially inherited stereotypical behavior also in an online setting, influencing the choice of programming concepts applied. This may inhibit learning and is a challenge that needs to be addressed independently of whether courses are held online.