ERICA: Interaction Mining Mobile Apps
ERICA: Interaction Mining Mobile Apps
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DOI：
10.1145/2984511.2984581
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发表时间：
2016-10
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影响因子：
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通讯作者：
Biplab Deka;Zifeng Huang;Ranjitha Kumar
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文献类型：
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作者：
Biplab Deka;Zifeng Huang;Ranjitha Kumar
设计在应用程序的采用中发挥着重要作用。然而，应用程序设计是一个包含多种设计活动的复杂过程。为了实现数据驱动的应用程序设计应用程序，我们提出了交互挖掘——捕获应用程序设计的静态（UI 布局、视觉细节）和动态（用户流程、运动细节）组件。我们推出了 ERICA，这是一个系统，它采用可扩展的人机方法来交互挖掘现有的 Android 应用程序，而无需以任何方式修改它们。当用户通过 ERICA 与应用程序交互时，它会检测 UI 更改，在后台无缝记录多个数据流，并将它们统一到用户交互跟踪中。我们使用 ERICA 收集了 1000 多个流行 Android 应用程序的交互痕迹。利用这些跟踪数据，我们构建了机器学习分类器来检测指示 23 种常见用户流的元素和布局。用户流程是 UX 设计的重要组成部分，由一系列 UI 状态组成，这些状态代表语义上有意义的任务，例如搜索或撰写。通过这些分类器，我们识别并索引了 3000 多个流程示例，并发布了 Android 应用中最大的用户流程在线搜索引擎。
Design plays an important role in adoption of apps. App design, however, is a complex process with multiple design activities. To enable data-driven app design applications, we present interaction mining -- capturing both static (UI layouts, visual details) and dynamic (user flows, motion details) components of an app's design. We present ERICA, a system that takes a scalable, human-computer approach to interaction mining existing Android apps without the need to modify them in any way. As users interact with apps through ERICA, it detects UI changes, seamlessly records multiple data-streams in the background, and unifies them into a user interaction trace. Using ERICA we collected interaction traces from over a thousand popular Android apps. Leveraging this trace data, we built machine learning classifiers to detect elements and layouts indicative of 23 common user flows. User flows are an important component of UX design and consists of a sequence of UI states that represent semantically meaningful tasks such as searching or composing. With these classifiers, we identified and indexed more than 3000 flow examples, and released the largest online search engine of user flows in Android apps.