Part of the problem is that there exists no systematic way to generate their workloads, observe their network behavior today and keep track of the changing trends of these applications. Despite increase of their popularity, we have little understanding of characteristics of these cloud applications. 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.Ĭloud-based Web applications powered by new technologies such as Asynchronous Javascript and XML (Ajax) place a significant burden on network operators and enterprises to effectively manage traffic. 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.