End-user software customization by direct manipulation of tabular data
Name
3426428.3426914.pdf
Description
Published version
Size
5.54 MB
Format
Adobe PDF
Checksum (MD5)
1f56538fec404f8a61aa6afa6d985171
Author(s)
Litt, Geoffrey
Jackson, Daniel
Millis, Tyler
Quaye, Jessica
Date Issued
2020
Journal
Onward! 2020 - Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Co-located with SPLASH 2020
Publisher
ACM
Citation
Litt, Geoffrey, Jackson, Daniel, Millis, Tyler and Quaye, Jessica. 2020. "End-user software customization by direct manipulation of tabular data." Onward! 2020 - Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Co-located with SPLASH 2020.
Version
Final published version
Abstract
© 2020 Owner/Author. Customizing software should be as easy as using it. Unfortunately, most customization methods require users to abruptly shift from using a graphical interface to writing scripts in a programming language. We introduce data-driven customization, a new way for end users to extend software by direct manipulation without doing traditional programming. We augment existing user interfaces with a table view showing the structured data inside the application. When users edit the table, their changes are reflected in the original UI. This simple model accommodates a spreadsheet formula language and custom data-editing widgets, providing enough power to implement a variety of useful extensions. We illustrate the approach with Wildcard, a browser extension that implements data-driven customization on the web using web scraping. Through concrete examples, we show that this paradigm can support useful extensions to many real websites, and we share reflections from our experiences using the tool. Finally, we share our broader vision for data-driven customization: a future where end users have more access to the data inside their applications, and can more flexibly repurpose that data as part of everyday software usage.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3426428.3426914
