Eye tracking: empirical foundations for a minimal reporting guideline.

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

Deja Vu: semantics-aware recording and replay of high-speed eye tracking and interaction data to support cognitive studies of software engineering tasks-methodology and analyses.

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 always 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, Deja Vu, is presented. An evaluation of the method and tool is conducted using three different eye trackers running at four different speeds (60 Hz, 120 Hz, 150 Hz, and 300 Hz). This timing evaluation is performed in Visual Studio, Eclipse, and Atom IDEs. Results show that Deja 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. Finally, a proof of concept replication analysis of four tasks from two previous studies is performed. Due to using the Deja Vu approach, this replication resulted in richer collected data and improved on the number of distinct syntactic categories that gaze was mapped on in the code.