Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts

要旨

Data analysts frequently employ code completion tools in writing custom scripts to tackle complex tabular data wrangling tasks. However, existing tools do not sufficiently link the data contexts such as schemas and values with the code being edited. This not only leads to poor code suggestions, but also frequent interruptions in coding processes as users need additional code to locate and understand relevant data. We introduce Xavier, a tool designed to enhance data wrangling script authoring in computational notebooks. Xavier maintains users' awareness of data contexts while providing data-aware code suggestions. It automatically highlights the most relevant data based on the user's code, integrates both code and data contexts for more accurate suggestions, and instantly previews data transformation results for easy verification. To evaluate the effectiveness and usability of Xavier, we conducted a user study with 16 data analysts, showing its potential to streamline data wrangling scripts authoring.

著者
Yunfan Zhou
Zhejiang University, Hangzhou, Zhejiang, China
Xiwen Cai
Zhejiang University, Hangzhou, Zhejiang, China
Qiming Shi
Zhejiang University, Hangzhou, Zhejiang, China
Yanwei Huang
Zhejiang University, Hangzhou, Zhejiang, China
Haotian Li
Microsoft Research Asia, Beijing, China
Huamin Qu
The Hong Kong University of Science and Technology, Hong Kong, China
Di Weng
Zhejiang University, Ningbo, Zhejiang, China
Yingcai Wu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3714239

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714239

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Playing with Data

Annex Hall F206
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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