Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study
Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study
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DOI：
10.1145/3313831.3376382
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发表时间：
2020-04
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影响因子：
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通讯作者：
Tianyi Zhang;Bjoern Hartmann;Miryung Kim;Elena L. Glassman
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作者：
Tianyi Zhang;Bjoern Hartmann;Miryung Kim;Elena L. Glassman
API成为现代软件的基本构建基础，其可用性对于编程效率和软件质量至关重要。然而，API设计师很难收集和解释其API的用户反馈。为了缩小差距，我们采访了来自6家公司和11个开源项目的23位API设计师，以了解其实践和需求。收集用户反馈的主要方法是通过错误报告和同行评审，因为在实践中进行的正式可用性测试非常昂贵。参与者表示强烈希望收集现实世界中的用例并了解用户的心理模型，但缺乏对这种需求的工具支持。特别是，参与者对用户被卡住，解决方法，常见错误和意外的角案件感到好奇。我们重点介绍了一些未满足的需求的机会，包括开发系统地引起用户心理模型的新机制，建立采矿框架，这些框架可以识别超出有关API使用的浅层统计数据的经常性模式，并探索在类似库中做出的替代设计选择。
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.