Exploring Eye Tracking Data on Source Code via Dual Space Analysis
Exploring Eye Tracking Data on Source Code via Dual Space Analysis
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DOI：
10.1109/vissoft.2019.00016
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发表时间：
2019-09
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影响因子：
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通讯作者：
Li Zhang;Jianxin Sun;Cole S. Peterson;Bonita Sharif;Hongfeng Yu
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作者：
Li Zhang;Jianxin Sun;Cole S. Peterson;Bonita Sharif;Hongfeng Yu
眼动追踪是一种常用的数据收集技术，用于捕捉用户处理信息的策略和行为。了解程序员如何在大量的类和方法中找到bug，对于软件工程的教育者和实践者来说是很重要的。然而，与在一个静态页面上收集的传统眼动追踪数据相比，在现实代码库上收集的眼动追踪数据是巨大的。当用户在集成开发环境（IDE）中滚动时，相同的内容可能出现在屏幕上的不同区域。层次结构的内容和流动的方法位置构成可视化的两大挑战。我们提出了一种双空间分析方法，利用现有的软件可视化和一种新的图形嵌入可视化来探索眼动追踪数据。我们使用图嵌入技术来量化两种任意方法之间的距离，与直接的软件结构和调用图相比，它在内在关系方面提供了更准确的距离可视化。可视化既自然又易读地显示了内容空间和嵌入空间中随时间变化的眼动数据，并为开发人员的眼动追踪行为提供了新的发现。
Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.