Robert A. DeLine
Microsoft Corp, Redmond, Washington, United States
Researchers have explored several avenues to mitigate data scientists' frustrations with computational notebooks, including: (1) live programming, to keep notebook results consistent and up to date; (2) supplementing scripting with graphical user interfaces (GUIs), to improve ease of use; and (3) providing domain-specific languages (DSLs), to raise a script's level of abstraction. This paper introduces Glinda, which combines these three approaches by providing a live programming experience, with interactive results, for a domain-specific language for data science. The language's compiler uses an open-ended set of ``recipes'' to execute steps in the user's data science workflow. Each recipe is intended to combine the expressiveness of a written notation with the ease-of-use of a GUI. Live programming provides immediate feedback to a user's input, whether in the form of program edits or GUI gestures. In a qualitative evaluation with 12 professional data scientists, participants highly rated the live programming and interactive results. They found the language productive and sufficiently expressive and suggested opportunities to extend it.
https://doi.org/10.1145/3411764.3445267
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)