Automated Recording and Semantics-Aware Replaying of High-Speed Eye Tracking and Interaction Data to Support Cognitive Studies of Software Engineering Tasks
Automated Recording and Semantics-Aware Replaying of High-Speed Eye Tracking and Interaction Data to Support Cognitive Studies of Software Engineering Tasks
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DOI：
10.1109/icsme46990.2020.00051
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Vlas Zyrianov;Drew T. Guarnera;Cole S. Peterson;Bonita Sharif;Jonathan I. Maletic
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作者：
Vlas Zyrianov;Drew T. Guarnera;Cole S. Peterson;Bonita Sharif;Jonathan I. Maletic
本文介绍了一个基本的技术问题，收集高速眼动跟踪数据，同时研究在集成开发环境中的软件工程任务。眼动仪的使用正在迅速成为研究软件开发人员以及他们如何理解源代码和定位错误的重要手段。高质量的眼动仪每秒可以记录120到300个注视点。然而，不可能以超过每秒60个凝视点的数据速率在真实的时间内将这些点中的每一个映射到源代码文件中的行和列位置（在存在滚动和文件切换的情况下）而没有数据丢失。不幸的是，更高的数据速率是更可取的，因为它们允许更细的粒度和更准确的研究分析。为了缓解这一技术问题，提出了一种新的眼动数据采集方法。研究过程中的所有遥测（鼠标、鼠标移动和眼动仪输出）数据都是在发生时记录的，而不是在真实的时间内执行注视分析。然后，会话以慢得多的速度重放，从而允许有充足的时间将注视点位置映射到适当的文件、行和列以执行附加分析。介绍了该方法和相应的工具，似曾相识。使用以四种不同速度（60Hz、120Hz、150Hz和300Hz）运行的三种不同的眼动仪进行方法和工具的评估。这个计时评估在Visual Studio和Eclipse IDE中执行。结果表明，Déjà Vu可以回放100%的数据记录，正确地将目光映射到相应的元素，使其成为未来软件工程中眼动跟踪研究的一个有充分依据和合适的后处理步骤。
The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over 60 gaze points per second without data loss. Unfortunately, higher data rates are more desirable as they allow for finer granularity and more accurate study analyses. To alleviate this technological problem, a novel method for eye tracking data collection is presented. Instead of performing gaze analysis in real time, all telemetry (keystrokes, mouse movements, and eye tracker output) data during a study is recorded as it happens. Sessions are then replayed at a much slower speed allowing for ample time to map gaze point positions to the appropriate file, line, and column to perform additional analysis. A description of the method and corresponding tool, Déjà Vu, is presented. An evaluation of the method and tool is conducted using three different eye trackers running at four different speeds (60Hz, 120Hz, 150Hz, and 300 Hz). This timing evaluation is performed in Visual Studio and Eclipse IDEs. Results show that Déjà Vu can playback 100% of the data recordings, correctly mapping the gaze to corresponding elements, making it a well-founded and suitable post processing step for future eye tracking studies in software engineering.