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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
Query Game 2.0: Improvement of a Web-Based Query Game for...
Mark Philip M. Sy, Christian James M. Historillo, Allen Cris T. · 2020-11-21 · via cs.SE updates on arXiv.org

Purpose: The study aimed to improve the previous study covering a web-based query game for Cavite State University. The study created a new mechanic and gameplay for the students to learn Structured Query Language (SQL). The enhancements also focused on the interactions of one or more students playing the game. Method: The researchers used iterative development process methodology in the development of the study. The system was assessed and evaluated using different testing methods: unit, integration, and system testing. After passing the tests, 90 students of Information Technology and Computer Science program along with 10 IT experts evaluated the system. Results: The respondents were classified into technical and non-technical respondents and the study garnered an evaluation score of 4.69 and 4.70 respectively. The overall interpretation of the results of the evaluation is Excellent. Conclusion: The study created a system where the user can read and watch lectures and experience the tutorial. Instructors and students may communicate in the system, hence, promoting better relations and healthy competition between students. Recommendations: Based on the conclusions of the study, the system can be used as a supplementary tool in teaching courses with Database Management. To enhance the analysis of query construction of the students, it is recommended to add a module that can analyze the pattern of creating queries and answering questions for every user.