Towards Effective Generation of Interactive Visualizations with Vibe Coding: An Empirical Study

2026-08-30

AI Summary

研究通过用户实验评估了vibe编码在生成交互式可视化中的能力与用户体验，探索了人机协作策略，以减少构建过程中的负担。

Abstract

Constructing interactive visualizations has traditionally required substantial human effort, involving both technical implementation and design decision-making. Recently, vibe coding, a programming paradigm leveraging Large Language Models to generate, interpret, and refactor code from natural language specifications, has emerged as a promising approach to reduce the burden. However, the capabilities and limitations of vibe coding in building interactive visualizations remain unexplored. To address this gap, we conducted a user study with 78 participants that were tasked with constructing interactive visualizations using vibe coding. We further collected users feedback through questionnaires, interviews, and case analyses. Based on this study, we examine (1) the capabilities and (2) user experience of vibe coding in generating interactive visualizations, and (3) the practical human-agent collaboration strategies adopted. Our findings provide the first systematic assessment of vibe coding for interactive visualization construction, revealing both its strengths and limitations, explaining the shift in developer labor and identifying the hybrid collaboration strategies participants adopted. Furthermore, our study offers insights for more intuitive and robust vibe coding practices.

Problem

• interactive visualizations
• vibe coding
• user experience
• human-agent collaboration

Innovation

• vibe coding
• interactive visualizations
• Large Language Models
• human-agent collaboration