一句话：结合程序合成与测试用例选择为数据整理代码自动生成文档，并以 JupyterLab 插件验证其帮助用户更快发现隐蔽缺陷。
数据科学家日常大量时间用于数据整理（清洗数据与抽取特征），但这类代码往往重复、易错，且在复用与改编既有代码时容易引入隐蔽缺陷，进而降低模型质量。为支持数据科学家，本文提出一种为数据整理代码生成文档的技术：一方面利用程序合成技术自动总结数据变换的语义，另一方面基于定制动态程序分析所收集的执行信息，采用测试用例选择技术从数据中特意挑选具有代表性的示例，从而生成可读的按需文档。作者实现了相应的 JupyterLab 扩展，并展示该技术可为流行 notebook 中的大量单元格提供按需文档；用户研究进一步表明，使用该插件的用户在发现数据整理代码中真实缺陷时更快、更有效。
Data scientists reportedly spend a significant amount of their time in their daily routines on data wrangling, i.e. cleaning data and extracting features. However, data wrangling code is often repetitive and error-prone to write. Moreover, it is easy to introduce subtle bugs when reusing and adopting existing code, which results in reduced model quality. To support data scientists with data wrangling, we present a technique to generate documentation for data wrangling code. We use (1) program synthesis techniques to automatically summarize data transformations and (2) test case selection techniques to purposefully select representative examples from the data based on execution information collected with tailored dynamic program analysis. We demonstrate that a JupyterLab extension with our technique can provide on-demand documentation for many cells in popular notebooks and find in a user study that users with our plugin are faster and more effective at finding realistic bugs in data wrangling code.
DOI 原文 ·
@inproceedings{paperbot6619,
title = {Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code},
author = {Chenyang Yang and Shurui Zhou and Jin L.C. Guo and Christian Kastner},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678520}
}