Automated Fixation Error Correction to Support Eye Tracking Studies on Source Code

A significant challenge in eye-tracking studies is detecting and fixing errors in data collection that happen for various reasons (drift, calibration issues, etc.). Many errors cannot be fully mitigated and require manual correction (which is intensively time-consuming) or automated correction. The work presented in this paper focuses on error correction, primarily on eye-tracking data on source code written in programming languages such as C++, Java, and C#. Many automated correction solutions are general-purpose, computationally inefficient, and use little information about the stimulus. To bridge this gap, we introduce srcGaze , a heuristic algorithm explicitly developed for correcting fixation gaze events in eye-tracking data from studies using source code as a stimulus. A golden dataset is manually constructed and verified to establish the heuristics. Results show a ≈40% improvement compared to no fixation correction. The approach has a multi-linear complexity and can correct over 44K fixations in approximately 6 seconds.