Data Visualization and Analytics
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Abstract
Abstract
Data visualization is essential for data analysis and communication, yet creating expressive visualizations remains labor-intensive. Recent AI-driven ``vibe coding'' tools enable users to generate visualizations through natural language interaction, lowering the barrier to entry. However, visualization implementation requires precise alignment between user intent and visual representation, which may differ from general software development practices. We present an empirical study with 16 participants of varying expertise to examine how users employ vibe coding tools for visualization implementation. Participants completed two visualization tasks and a semi-structured interview. Our findings characterize the diverse practices users adopt across prompting, evaluation, and iteration, and surface the challenges they encounter throughout the process.
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@article{Sun2026Vibe,
title = {Vibe Coding for Visualization Implementation: An Empirical Study of Practices and Challenges},
author = {Z. T. Sun and Xiaolin Wen and Fang Wang and Can Liu and Yi Lai and Christophe Hurter and Yong Wang},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2606.19703},
url = {https://doi.org/10.48550/arxiv.2606.19703}
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