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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
Factors Impacting Faculty Adoption of Project-Based Learn...
Ahmad D. Suleiman, Yiming Tang, Daqing Hou · 2025-07-24 · via cs.SE updates on arXiv.org

This research full paper investigates the factors influencing computing educators' adoption of project-based learning (PjBL) in software engineering and computing curricula. Recognized as a student-centered pedagogical approach, PjBL has the potential to enhance student motivation, engagement, critical thinking, collaboration, and problem-solving skills. Despite these benefits, faculty adoption remains inconsistent due to challenges such as insufficient institutional support, time constraints, limited training opportunities, designing or sourcing projects, and aligning them with course objectives. This research explores these barriers and investigates the strategies and resources that facilitate a successful adoption. Using a mixed-methods approach, data from 80 computing faculty were collected through an online survey comprising closed-ended questions to quantify barriers, enablers, and resource needs, along with an open-ended question to gather qualitative insights. Quantitative data were analyzed using statistical methods, while qualitative responses underwent thematic analysis. Results reveal that while PjBL is widely valued, its adoption is often selective and impacted by challenges in planning and managing the learning process, designing suitable projects, and a lack of institutional support, such as time, funding, and teaching assistants. Faculty are more likely to adopt or sustain PjBL when they have access to peer collaboration, professional development, and institutional incentives. In addition, sourcing projects from research, industry partnerships, and borrowing from peers emerged as key facilitators for new projects. These findings underscore the need for systemic support structures to empower faculty to experiment with and scale PjBL practices.