# Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co-Debugging System

arXiv: https://arxiv.org/abs/2412.07673 · HTML: https://mlsanity.com/paper/2412.07673/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Prompt Optimization](https://mlsanity.com/paper/2502.06855/) (2025)
- [ChartMark: A Structured Grammar for Chart Annotation](https://mlsanity.com/paper/2507.21810/) (2025)
- [ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding](https://mlsanity.com/paper/2505.15046/) (2025)
- [DataMagic: Transforming Tabular Data into Data Insight Video](https://mlsanity.com/paper/2606.20388/) (2026)
- [ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation](https://mlsanity.com/paper/2601.12983/) (2026)

## Abstract (arXiv)

Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches, or how they can be effectively combined. We analyze Vega-Lite debugging cases from Stack Overflow, categorizing question types by askers, evaluating human responses, and assessing AI performance. Guided by these findings, we design a human-AI co-debugging system that combines LLM-generated suggestions with forum knowledge. We evaluated this system in a user study on 36 unresolved problems, comparing it with forum answers and LLM baselines. Our results show that while forum contributors provide accurate but slow solutions and LLMs offer immediate but sometimes misaligned guidance, the hybrid system resolves 86% of cases, higher than either alone.