Advancing Dynamic-Time Warp Techniques for Correcting Eye Tracking Data in Reading Source Code

Background: Automated eye tracking data correction algorithms such as Dynamic-Time Warp always made a trade-off between the ability to handle regressions (jumps back) and distortions (fixation drift). At the same time, eye movement in code reading is characterized by non-linearity and regressions. Objective: In this paper, we present a family of hybrid algorithms that aim to handles both regressions and distortions with high accuracy. Method: Through simulations with synthetic data we replicate known eye movement phenomena to assess our algorithms against Warp algorithm as a baseline. Furthermore, we utilize three real datasets to evaluate the algorithms in correcting data from reading source code and see if the proposed algorithms generalize to correcting data from reading natural language text. Results: Our results demonstrate that most proposed algorithms match or outperform baseline warp in correcting both synthetic and real data. Also, we show the prevalence of regressions in reading source code. Conclusion: Our results highlight our hybrid algorithms as an improvement to Dynamic-Time Warp in handling regressions with higher accuracy and better runtime.