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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
OwlEyes-Online: A Fully Automated Platform for Detecting ...
Yuhui Su, Zhe Liu, Chunyang Chen, Junjie Wang, Qing Wang · 2021-07-06 · via cs.SE updates on arXiv.org

Graphical User Interface (GUI) provides visual bridges between software apps and end users. However, due to the compatibility of software or hardware, UI display issues such as text overlap, blurred screen, image missing always occur during GUI rendering on different devices. Because these UI display issues can be found directly by human eyes, in this paper, we implement an online UI display issue detection tool OwlEyes-Online, which provides a simple and easy-to-use platform for users to realize the automatic detection and localization of UI display issues. The OwlEyes-Online can automatically run the app and get its screenshots and XML files, and then detect the existence of issues by analyzing the screenshots. In addition, OwlEyes-Online can also find the detailed area of the issue in the given screenshots to further remind developers. Finally, OwlEyes-Online will automatically generate test reports with UI display issues detected in app screenshots and send them to users. The OwlEyes-Online was evaluated and proved to be able to accurately detect UI display issues. Tool Link: http://www.owleyes.online:7476 Github Link: https://github.com/franklinbill/owleyes Demo Video Link: https://youtu.be/002nHZBxtCY