gazel:支持眼动追踪研究中的源代码编辑

Sarah Fakhoury, Devjeet Roy, Harry Pines, Tyler Cleveland, Cole S. Peterson, V. Arnaoudova, Bonita Sharif, Jonathan I. Maletic
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引用次数: 7

摘要

眼动追踪工具在软件工程研究中用于研究各种软件开发活动。然而,这些工具的一个主要限制是它们无法跟踪涉及源代码编辑的活动的注视数据。作为iTrace[9]社区基础设施的扩展,我们提出了一种新颖的解决方案来支持涉及源代码编辑的任务的眼动追踪实验。我们介绍了iTrace-Atom插件和gazel [g@"zel]——一个Python数据处理管道,它将注视信息映射到不断变化的源代码元素,并为研究人员提供了一种查询动态数据的方法。iTrace-Atom通过一系列模拟进行评估,在超过1000赫兹的高眼球追踪速度下,准确率超过99%。iTrace和gazel完全改变了眼动追踪研究在现实环境中进行的方式,包括滚动、上下文切换和编辑。这为支持许多日常的软件工程任务打开了大门,例如bug修复、添加新特性和重构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
gazel: Supporting Source Code Edits in Eye-Tracking Studies
Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace [9] community infrastructure. We introduce the iTrace-Atom plugin and gazel [g@"zel]—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring.
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