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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
Prof. CI: Employing Continuous Integration Services and G...
Christoph Matthies, Arian Treffer, Matthias Uflacker · 2018-09-03 · via cs.SE updates on arXiv.org

Teaching programming using Massive Open Online Courses (MOOCs) is gaining popularity due to their scalability and efficiency of knowledge distribution. However, participating in these courses usually means fully committing to the supplied programming environment in the browser. While this allows a consistent and controllable setup, learners do not gain experience with actual development tools, such as local code editors, testing frameworks, issue trackers or continuous integration (CI) services, which is critical for subsequent real-world projects. Furthermore, the tests for the functionality that is to be developed are oftentimes already available in MOOCs and simply need to be executed, leading to less involvement with developing appropriate tests. In order to tackle these issues while maintaining a high degree of automation and scalability, we developed Prof. CI, a novel approach to conducting online exercises. Prof. CI leverages the existing automation infrastructure that developers use daily, i.e. CI services and Github workflows, to teach test-driven development (TDD) practices. Participants work on their own repositories in Github and receive feedback and new challenges from the CI server when they push their code. We have successfully applied this approach in a pilot project with 30 undergraduate students learning the Ruby on Rails web development framework. Our evaluation shows that the exercise effectively increased students' motivation to write tests for their code. We also present the results of participant surveys, students' experiences and teachers' observations.