Affiliation:
1. Federal University of Bahia, Institute of Computing (IC-UFBA), Salvador, Brazil
2. Federal University Rural of Pernambuco (UFRPE), Recife, Brazil
3. University of California, Irvine, CA, USA
Abstract
Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, few studies have been found to understand Jupyter development challenges from the practitioners’ point of view. This article presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. We mined 14,740 commits from 105 GitHub open source projects with Jupyter Notebook code. Next, we analyzed 30,416 StackOverflow posts, which gave us insights into bugs that practitioners face when developing Jupyter Notebook projects. Next, we conducted 19 interviews with data scientists to uncover more details about Jupyter bugs and to gain insight into Jupyter developers’ challenges. Finally, to validate the study results and proposed taxonomy, we conducted a survey with 91 data scientists. We highlight bug categories, their root causes, and the challenges that Jupyter practitioners face.
Publisher
Association for Computing Machinery (ACM)
Reference44 articles.
1. We don't need another hero?
2. Analyze this! 145 questions for data scientists in software engineering
3. Data Science
4. Souti Chattopadhyay, Ishita Prasad, Austin Z. Henley, Anita Sarma, and Titus Barik. 2020. What’s wrong with computational notebooks? Pain points, needs, and design opportunities. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’20). ACM, 1–12.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献