Investigating the Feasibility of Conducting Webcam-Based Eye-Tracking Studies in Code Comprehension

Zihan Fang, Robert Wallace, Zachary Karas, Toby Li, Collin McMillan, Yu Huang

Abstract: Researchers in Software Engineering (SE) often use onsite screen-mounted eye-tracking experiments to investigate programmers’ visual attention patterns in various programming activities. The pandemic and the difficulty of recruiting many participants, especially those with special expertise in SE, have hastened the shift towards conducting eye-tracking studies offsite, which use integrated webcams to track participants’ gaze in natural settings. This study compares the efficacy of a webcam-based eye tracker to a research-focused screen-mounted eye tracker in code comprehension tasks. We conducted onsite experiments with 49 participants, each using both types of eye trackers simultaneously to assess the webcam-based eye tracker’s capability to capture visual patterns at general, semantic, and token levels and detect individual differences. Additionally, we conducted offsite experiments with 10 participants to supplement the findings. Our findings indicate that while the webcam-based eye tracker effectively captures programmers’ semantic comprehension, but faces challenges in accurately identifying cognitive patterns at a more detailed token level in onsite settings. Furthermore, the elevated noise observed in real-world offsite conditions significantly limits the tracker’s reliability for drawing accurate conclusions. Participants also encountered challenges with calibration and task initiation, highlighting areas for improvement in conducting webcam-based eye-tracking studies offsite in the future. This study investigates the feasibility of webcam-based eye-tracking studies in SE, offers insights to enhance the accuracy of webcam-based eye-tracking in programming potentially, and provides guidelines for future webcam-based eye-tracking study designs.

External IDs:doi:10.1109/tse.2026.3651615