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

Guarnera, Drew Thomas
http://orcid.org/0000-0001-8900-6860
http://rave.ohiolink.edu/etdc/view?acc_num=kent1730414272746663

Abstract Details

2024, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
Eye trackers are an important tool in cognitive science research. Advances in eye-tracking technology reduce the invasive nature of early forms of the equipment along with the costs to acquire the devices for research. As such, eye-tracking is becoming an essential device for research on software engineering and program comprehension. The main focus of this work is to study how developers read and understand source code in the context of various software engineering tasks (e.g., debugging, summarizing code, defect localization, etc.). However, eye-tracking devices create challenges for researchers. Eye tracking devices have inherent margins of error in sampling introduced by various factors, which introduces errors in data sampling during studies that cannot be controlled. As such, despite best efforts, eye-tracking data always needs some form of correction to help identify and remove invalid data or adjust the data as samples drift or become displaced over time by participant movements, fatigue, or other factors. The processes used to correct eye-tracking data are time-consuming and require trial and error to correct the data reasonably while mitigating the impact of the corrections. Additionally, eye-tracking infrastructure to support program comprehension studies with realistic stimuli in natural software development environments is limited. This dissertation addresses these limitations. Focusing on program comprehension-based studies using eye-tracking, SrcGaze, an algorithm to support the automatic correction of fixation gaze event data using source code stimulus, is presented. Compared to the state-of-the-art fixation correction methods, SrcGaze is capable of 73\% agreement with manually corrected fixation data. The algorithm also has a linear run time, making it the most computationally efficient approach; it can correct over 44,000 fixations in only 6 seconds. During this work, contributions are also made to iTrace, an infrastructure to support eye-tracking studies using source code. This work includes a redesign of the infrastructure, the creation of plugins to support multiple development environments and tools, implementation of eye-tracking data analysis and storage tools, and D\'ej\'a Vu, a novel approach to supporting high-speed eye-tracking devices sampling at speeds \textgreater60 Hz while also supporting record and playback of participant studies for unlimited research opportunities post data collection.
Jonathan Maletic (Committee Chair)
Jong-Hoon Kim (Committee Member)
Qiang Guan (Committee Member)
Michael Carl (Committee Member)
Bonita Sharif (Committee Member)
Jocelyn Folk (Committee Member)
Michael Collard (Committee Member)
121 p.
Computer Science
computer science; source code; eye tracking; fixation correction; gaze event correction; heuristic algorithm; program comprehension

Recommended Citations

Citations

  • Guarnera, D. T. (2024). SrcGaze: Automated Fixation Error Correction to Support Eye Tracking Studies on Source Code [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1730414272746663

    APA Style (7th edition)

  • Guarnera, Drew. SrcGaze: Automated Fixation Error Correction to Support Eye Tracking Studies on Source Code. 2024. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1730414272746663.

    MLA Style (8th edition)

  • Guarnera, Drew. "SrcGaze: Automated Fixation Error Correction to Support Eye Tracking Studies on Source Code." Doctoral dissertation, Kent State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=kent1730414272746663

    Chicago Manual of Style (17th edition)

kent1730414272746663
67
© 2024, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.