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From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy 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 MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
GitQ- Towards Using Badges as Visual Cues for GitHub Proj...
Akhila Sri Manasa Venigalla, Kowndinya Boyalakunta, Sridhar Chim · 2021-07-08 · via cs.HC updates on arXiv.org

GitHub hosts millions of software repositories, facilitating developers to contribute to many projects in multiple ways. Most of the information about the repositories is text-based in the form of stars, forks, commits, and so on. However, developers willing to contribute to projects on GitHub often find it challenging to select appropriate projects to contribute to or reuse due to the large number of repositories present on GitHub. Further, obtaining this required information often becomes a tedious process, as one has to carefully mine information hidden inside the repository. To alleviate the effort intensive mining procedures, researchers have proposed npm-badges to outline information relating to build status of a project. However, these badges are static and limit their usage to package dependency and build details. Adding visual cues such as badges to the repositories might reduce the search space for developers. Hence, we present GitQ, to automatically augment GitHub repositories with badges representing information about source code and project maintenance. Presenting GitQ as a browser plugin to GitHub could make it easily accessible to developers using GitHub. GitQ is evaluated with 15 developers based on the UTAUT model to understand developer perception towards its usefulness. We observed that 11 out of 15 developers perceived GitQ to be useful in identifying the right set of repositories using visual cues such as generated by GitQ. The source code and tool are available for download on GitHub at https://github.com/gitq-for-github/plugin, and the demo can be found at https://youtu.be/c0yohmIat3A.