Methods for Investigating Mental Models for Learners of APIs
Methods for Investigating Mental Models for Learners of APIs
复制标题
API 学习者心智模型研究方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
B. Myers
中科院分区：
文献类型：
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作者：
Amber Horvath;Mariann Nagy;Finn Voichick;Mary Beth Kery;B. Myers
尽管几乎所有的软件开发都涉及应用程序编程接口（API），但令人惊讶的是，人们如何使用API以及如何评估和改进API的可用性的工作却很少。调查API可用性的一种可能方法是通过用户对API的心理模型。通过与Google的开发人员和UX从业人员的讨论沿着我们自己的评估，一个名为Apache Beam的分布式数据处理API被认为很难使用和学习。在我们正在进行的研究中，我们研究了理解用户分布式数据处理的心理模型的方法，以及这种理解如何导致对Beam及其文档的设计见解。我们提出了我们的新方法，它结合了两个自然的编程启发段的背景采访：第一个设计为参与者表达一个高层次的心理模型的数据处理API，而第二个问的问题上下文的数据处理任务，看看参与者如何将他们的概念理解到一个更具体的情况。我们的方法显示出了希望，因为试点参与者表达了一种“低”的心理模型，这种模型与Beam描述的一种方式相匹配，从而导致了潜在的设计修改。
Despite almost all software development involving application programming interfaces (APIs), there is surprisingly little work on how people use APIs and how to evaluate and improve the usability of an API. One possible way of investigating the usability of APIs is through the user's mental model of the API. Through discussions with the developers and UX practitioners at Google along with our own evaluations, a distributed data processing API called Apache Beam has been identified as difficult to use and learn. In our on-going study, we investigate methods for understanding users' mental models of distributed data processing and how this understanding can lead to design insights for Beam and its documentation. We present our novel approach, which combines a background interview with two natural programming elicitation segments: the first designed for participants to express a high level mental model of a data processing API while the second asks questions contextualized to a data processing task to see how participants apply their conceptual understanding to a more specific situation. Our method shows promise as pilot participants expressed a "dataflow" mental model that matched one way that Beam has been described, resulting in a potential design modification.