![]() This paper presents a systematic literature review (SLR) to survey the existing NeuroSE literature. In the past decade, brain and autonomic nervous system activity measurement received increasing attention in the study of software engineering (SE). ![]() This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring. 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. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. 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. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace community infrastructure. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. 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.Įye tracking tools are used in software engineering research to study various software development activities. Finally, a proof of concept replication analysis of four tasks from two previous studies is performed. 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. This timing evaluation is performed in Visual Studio, Eclipse, and Atom IDEs. ![]() 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). A description of the method and corresponding tool, Deja Vu, is presented. 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. 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. To alleviate this technological problem, a novel method for eye tracking data collection is presented. Unfortunately, higher data rates are more desirable as they allow for finer granularity and more accurate study analyses. 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. ![]() High quality eye trackers can record upwards of 120 to 300 gaze points per second. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment.
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