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 community infrastructure. We introduce the iTrace-Atom plugin and gazel -- 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.
翻译:用于研究各种软件开发活动的软件工程研究中,使用了眼睛跟踪工具来研究各种软件开发活动,然而,这些工具的一个主要局限性在于它们无法跟踪涉及源码编辑的活动的目视数据。我们提出了一个新颖的解决方案,支持对涉及源码编辑的任务进行眼视跟踪实验,作为iTrace社区基础设施的延伸。我们引入了iTrace-Atom插件和Gappel -- -- Python数据处理管道,该插件映射信息以改变源码元素,并为研究人员提供了一个查询这种动态数据的途径。iTrace-Atom通过一系列的模拟来评估,在1000赫兹(iTrace)和Gearl的高眼睛跟踪速度上,99%的精确度超过99%,使眼跟踪研究在现实环境中进行的方式发生彻底革命,同时存在滚动、上下文转换和现在的编辑。这打开了大门,以支持许多日常的软件工程任务,如纠正错误、添加新特征和重新设定。