Fork It: Supporting Stateful Alternatives in Computational Notebooks


Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives, data scientists must carefully create many-step manipulations in visually distant cells. We conducted formative interviews with 6 professional data scientists, motivating design principles behind exposing multiple states. We introduce forking --- creating a new interpreter session --- and backtracking --- navigating through previous states. We implement these interactions as an extension to notebooks that help data scientists more directly express and navigate through decision points a single notebook. In a qualitative evaluation, 11 professional data scientists found the tool would be useful for exploring alternatives and debugging code to create a predictive model. Their insights highlight further challenges to scaling this functionality.

Nathaniel Weinman
University of California, Berkeley, Berkeley, California, United States
Steven M.. Drucker
Microsoft Research, Redmond, Washington, United States
Titus Barik
Microsoft, Redmond, Washington, United States
Robert A. DeLine
Microsoft Corp, Redmond, Washington, United States




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Engineering Development Support

[A] Paper Room 05, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 05, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 05, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 05
14 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00