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Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
SecCityVR: Visualization and Collaborative Exploration of...
Dennis Wüppelman, Enes Yigitbas · 2025-04-25 · via cs.HC updates on arXiv.org

Security vulnerabilities in software systems represent significant risks as potential entry points for malicious attacks. Traditional dashboards that display the results of static analysis security testing often use 2D or 3D visualizations, which tend to lack the spatial details required to effectively reveal issues such as the propagation of vulnerabilities across the codebase or the appearance of concurrent vulnerabilities. Additionally, most reporting solutions only treat the analysis results as an artifact that can be reviewed or edited asynchronously by developers, limiting real-time, collaborative exploration. To the best of our knowledge, no VR-based approach exists for the visualization and interactive exploration of software security vulnerabilities. Addressing these challenges, the virtual reality (VR) environment SecCityVR was developed as a proof-of-concept implementation that employs the code city metaphor within VR to visualize software security vulnerabilities as colored building floors inside the surrounding virtual city. By integrating the application's call graph, vulnerabilities are contextualized within related software components. SecCityVR supports multi-user collaboration and interactive exploration. It provides explanations and mitigations for detected issues. A user study comparing SecCityVR with the traditional dashboard find-sec-bugs showed the VR approach provided a favorable experience, with higher usability, lower temporal demand, and significantly lower frustration despite having longer task completion times. This paper and its results contribute to the fields of collaborative and secure software engineering, as well as software visualization. It provides a new application of VR code cities to visualize security vulnerabilities, as well as a novel environment for security audits using collaborative and immersive technologies.