[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83561-en":3,"doc-seo-83561-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83561,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Identifying Effective Program Comprehension Strategies through Gaze Transitions over Syntactic Elements","Program comprehension is a core topic in software engineering, examining how developers understand a program’s structure, behavior, and intent. Traditional eye-tracking research often maps gaze to display coordinates, while newer syntax-based analyses translate eye movements into transitions over syntactic structures. This study converts eye-tracking data into transitions between syntactic nodes, then compares fixation proportions and gaze-transition patterns between correct and incorrect task performers. Findings show distinct pattern differences, with successful participants using more systematic transitions across syntactic elements.","Identifying Effective Program Comprehension Strategies through Gaze Transitions over Syntactic Elements  \nKyogo Horikawa  \n[ai1221@nara.kosen-ac.jp](ai1221@nara.kosen-ac.jp)[ ](ai1221@nara.kosen-ac.jp)National Institute of Technology(KOSEN), Nara College Yamatokoriyama, Nara, Japan  \nHidetake Uwano  \n[uwano@info.nara-k.ac.jp](uwano@info.nara-k.ac.jp)[ ](uwano@info.nara-k.ac.jp)National Institute of Technology(KOSEN), Nara College Yamatokoriyama, Nara, Japan  \nHaruhiko Yoshioka  \n[yoshioka.haruhiko.yi4@naist.ac.jp](yoshioka.haruhiko.yi4@naist.ac.jp)[ ](yoshioka.haruhiko.yi4@naist.ac.jp)Nara Institute of Science and Technology Ikoma, Nara, Japan  \narXiv :2607 .0 1042v 1 [ cs . SE] 1 Jul 2026  \nAbstract  \nProgram comprehension is a central research topic in software engineering, focusing on how developers understand a program’s structure, behavior, and intent. Eye-tracking studies have traditionally relied on display-based measurements, where gaze positions are represented as screen coordinates. However, syntax-based analyses have recently emerged. Prior work proposed methods to convert eye movements into transitions between nodes in an abstract syntax tree, but the relationship between task correctness and eyemovement features for specific syntactic elements remains unclear. This study converts eye-tracking data into transitions between syntactic nodes and analyzes fixation proportions and gaze transition patterns. We investigate the relationship between these patterns and task correctness, comparing correct and incorrect groups. Our results reveal distinct differences in gaze transition patterns between the two groups. In particular, successful participants exhibit more systematic transitions across syntactic elements, suggesting the use of structured reading strategies.  \nCCS Concepts  \n• Human-centered computing → Empirical studies in HCI; Human computer interaction (HCI); • Software and its engineering → Software notations and tools.  \nACM Reference Format:  \nKyogo Horikawa, Hidetake Uwano, and Haruhiko Yoshioka. 2026. Identifying Effective Program Comprehension Strategies through Gaze Transitions over Syntactic Elements. In 2026 Symposium on Eye Tracking Research and Applications (ETRA’26), June 01–04, 2026, Marrakesh, Morocco. ACM, New York, NY, USA, 6 pages. [https://doi.org/10.1145/3797246.3806012](https://doi.org/10.1145/3797246.3806012)  \n1 Introduction  \nProgram comprehension is a research area within software engineering that investigates how developers understand source code. It focuses on the process by which developers read source code to grasp a program’s structure, behavior, and intent. Analyzing program comprehension helps reveal developers’ cognitive processes and identify challenging program constructs, thereby supporting improvements in code readability and developer education. Various approaches have been used to study program comprehension, including the measurement of physiological signals, such as eye  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. ETRA’26, Marrakesh, Morocco  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2519-7/2026/06  \n[https://doi.org/10.1145/3797246.3806012](https://doi.org/10.1145/3797246.3806012)  \nmovements [Busjahn et al. 2015], brain activity [Siegmund et al. 2014], and the analysis of behavioral logs [Ko et al. 2006] . In particular, eye-tracking studies have examined the characteristics of developers’ gaze behavior while reading source code to better understand comprehension processes [Abid et al. 2019; Ishida and Uwano 2019; Lin et al. 2016; Sharafi et al. 2022] .  \nMost eye-tracking studies in program comprehension have relied on display-positional information, where gaze positions are represented as coordinates on the screen. Recently, syntax-based measures have emerged. Yoshioka proposed a method for converting eye movement data that was recorded as display coordinates into transitions between nodes in a syn","cbCaipVPW53v47yM","https://ap.wps.com/l/cbCaipVPW53v47yM","pdf",630915,5,1,6,"English","en",105,"# Introduction\n## Background and related work\n## Study goal and research questions","[{\"question\":\"What is the central problem addressed in the study?\",\"answer\":\"The study examines how program comprehension success relates to eye-movement features across specific syntactic elements, which is still unclear in prior work.\"},{\"question\":\"How are eye-tracking data used in this research?\",\"answer\":\"Eye-tracking gaze data are converted from screen coordinates into transitions between nodes in a syntax tree, enabling analysis at the syntactic level.\"},{\"question\":\"What differences are reported between successful and unsuccessful participants?\",\"answer\":\"Successful participants show distinct, more systematic gaze transition patterns across syntactic elements compared with unsuccessful 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