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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
Empirical Analysis of Pull Requests for Google Summer of ...
Saheed Popoola · 2024-12-18 · via cs.SE updates on arXiv.org

Internship and industry-affiliated capstone projects are popular ways to expose students to real world experiences and bridge the gap between academic training and industry requirements. However, these two approaches often require active industry collaboration, and many students struggle to find industry placements. Open-source contributions are a crucial alternative to gain real world experience, earn publicly verifiable contribution with real-world impact, and learn from experienced open-source contributors. The Google Summer of Code (GSoC) is a global initiative that matches students or new contributors with experienced mentors to work on open-source projects. The program aims to introduce the students to open-source development, help them gain valuable skills under the guidance of mentors, and hopefully encourage them to continue contributing to open-source projects. The realization of the program objectives will provide a continuous pool of talented new contributors necessary for maintaining open-source projects. This study presents an empirical analysis of pull requests created by interns during the GSoC program. We extracted and analyzed 17,232 pull requests from 2,456 interns across 1,937 open-source projects. The results show most tasks involve both code-intensive activities like adding new features and fixing bugs, as well as non-code tasks like updating documentation and restructuring the codebase. Feedback from reviewers covers code functionality and programming logic, testing coverage, error handling, code readability, and adherence to best practices. Finally, we discuss the implications of these results for software engineering education.