Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study
Tianyi Zhang, Björn Hartmann, Miryung Kim, Elena L. Glassman
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers7
- Using an LLM to Help With Code UnderstandingDaye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu et al.ICSE 2024 · 264 citations
- Wish You Were Here: Mental and Physiological Effects of Remote Music Collaboration in Mixed RealityRuben Schlagowski, Dariia Nazarenko, Yekta Said Can, Kunal Gupta et al.CHI 2023 · 39 citations
- How statically-typed functional programmers write codeJustin Lubin, Sarah E. ChasinsOOPSLA 2021 · 17 citations
- Concept-Annotated Examples for Library ComparisonLitao Yan, Miryung Kim, Bjoern Hartmann, Tianyi Zhang et al.UIST 2022 · 14 citations
- Visualizing Examples of Deep Neural Networks at ScaleLitao Yan, Elena L. Glassman, Tianyi ZhangCHI 2021 · 10 citations
Related papers
- When APIs are intentionally bypassed: an exploratory study of API workaroundsMaxime Lamothe, Weiyi ShangICSE 2020 · 14 citations
- ARBITRAR: User-Guided API Misuse DetectionZiyang Li, Aravind Machiry, Binghong Chen, Mayur Naik et al.S&P 2021 · 30 citations
- Demystify official API usage directives with crowdsourced API misuse scenarios, erroneous code examples and patchesXiaoxue Ren, Jiamou Sun, Zhenchang Xing, Xin Xia et al.ICSE 2020 · 28 citations
- A Qualitative Analysis of Fuzzer Usability and ChallengesYunze Zhao, Wentao Guo, Harrison Goldstein, Daniel Votipka et al.CCS 2025
- APISan: Sanitizing API Usages through Semantic Cross-CheckingInsu Yun, Changwoo Min, Xujie Si, Yeongjin Jang et al.USENIX Security 2016 · 107 citations
