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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
Pandemic Software Development: The Student Experiences fr...
Benjamin Koh, Mojtaba Shahin, Annette Ong, Soo Ying Yeap, Priyan · 2021-09-20 · via cs.SE updates on arXiv.org

The COVID-19 pandemic has birthed a wealth of information through many publicly accessible sources, such as news outlets and social media. However, gathering and understanding the content can be difficult due to inaccuracies or inconsistencies between the different sources. To alleviate this challenge in Australia, a team of 48 student volunteers developed an open-source COVID-19 information dashboard to provide accurate, reliable, and real-time COVID-19 information for Australians. The students developed this software while working under legislative restrictions that required social isolation. The goal of this study is to characterize the experiences of the students throughout the project. We conducted an online survey completed by 39 of the volunteering students contributing to the COVID-19 dashboard project. Our results indicate that playing a positive role in the COVID-19 crisis and learning new skills and technologies were the most cited motivating factors for the students to participate in the project. While working on the project, some students struggled to maintain a work-life balance due to working from home. However, the students generally did not express strong sentiment towards general project challenges. The students expressed more strongly that data collection was a significant challenge as it was difficult to collect reliable, accurate, and up-to-date data from various government sources. The students have been able to mitigate these challenges by establishing a systematic data collection process in the team, leveraging frequent and clear communication through text, and appreciating and encouraging each other's efforts. By participating in the project, the students boosted their technical (e.g., front-end development) and non-technical (e.g., task prioritization) skills. Our study discusses several implications for students, educators, and policymakers.