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arXiv · 2608.09875

Comprehending Python Repetition Structures: An Eye-Tracking Study with Novice Developers

José Júnior Silva da Costa·Rohit Gheyi·José Aldo Silva da Costa·Márcio Ribeiro
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Abstract

Code comprehension is central to software maintenance and evolution, yet different Python repetition structures may impose distinct cognitive demands. We conducted a controlled eye-tracking experiment with 32 undergraduate students with prior Python experience to compare comprehension of for loops, while loops, recursion, and list comprehensions (LCs). Participants solved six comprehension tasks in a Latin Square design while we measured completion behavior and eye-tracking metrics over full snippets and construct-specific Areas of Interest (AOIs). for loops showed the lowest visual effort. Relative to for, while loops increased AOI fixation duration by up to 97% and regression count by 114%, with regressions concentrated around counter management. Recursion showed a descriptive 50% increase in regressions, mainly between the base case and recursive call. LCs increased AOI time by 62.5% and fixation duration by 80.9%, with horizontal regressions suggesting dense token-by-token parsing. LC comparisons yielded the clearest statistically significant pairwise differences, while the combined comparison of all non-for structures was significant across all eye-tracking metrics. These findings provide process-level evidence that Python repetition structures induce distinct visual-effort patterns, with implications for readability, code review, refactoring, onboarding, and maintainability.

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José Júnior Silva da Costa, Rohit Gheyi, José Aldo Silva da Costa, Márcio Ribeiro. 2026-08-10. Comprehending Python Repetition Structures: An Eye-Tracking Study with Novice Developers. https://arxiv.org/abs/2608.09875

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