Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study

要旨

APIs are becoming the fundamental building block of modern software and their usability is crucial to programming efficiency and software quality. Yet API designers find it hard to gather and interpret user feedback on their APIs. To close the gap, we interviewed 23 API designers from 6 companies and 11 open-source projects to understand their practices and needs. The primary way of gathering user feedback is through bug reports and peer reviews, as formal usability testing is prohibitively expensive to conduct in practice. Participants expressed a strong desire to gather real-world use cases and understand users' mental models, but there was a lack of tool support for such needs. In particular, participants were curious about where users got stuck, their workarounds, common mistakes, and unanticipated corner cases. We highlight several opportunities to address those unmet needs, including developing new mechanisms that systematically elicit users' mental models, building mining frameworks that identify recurring patterns beyond shallow statistics about API usage, and exploring alternative design choices made in similar libraries.

キーワード
API Design
Community
Information Needs
Tool Support
著者
Tianyi Zhang
Harvard University, Cambridge, MA, USA
Björn Hartmann
University of California, Berkeley, Berkeley, CA, USA
Miryung Kim
University of California, Los Angeles, Los Angeles, CA, USA
Elena L. Glassman
Harvard University, Cambridge, MA, USA
DOI

10.1145/3313831.3376382

論文URL

https://doi.org/10.1145/3313831.3376382

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: APIs & debugging

Paper session
312 NI'IHAU
4 件の発表
2020-04-30 23:00:00
2020-05-01 00:15:00
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