Title: gazel: Supporting Source Code Edits in Eye-Tracking Studies
Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace community infrastructure. We introduce the iTrace-Atom plugin and gazel—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring. more »« less
Sharif, Bonita; Peterson, Cole; Guarnera, Drew; Bryant, Corey; Buchanan, Zachary; Zyrianov, Vlas; Maletic, Jonathan
(, 2019 IEEE/ACM 6th International Workshop on Eye Movements in Programming (EMIP))
null
(Ed.)
The evolution and effort in designing and implementing iTrace, an infrastructure for integrating eye tracking into developer environments, is presented. The goal is to make eye tracking practical for various stakeholders in software engineering namely researchers, practitioners, and educators. An overview of iTrace and the general process involved in conducting an eye tracking study with human subjects using iTrace is presented in this tool demo paper. Upcoming features and ongoing plans for community involvement are also presented.
Sharif, B.; Peterson, C.; Guarnera, D.T.; Bryant, C.; Buchanan, Z.; Zyrianov, V.; Maletic, J.I.
(, 6th International Workshop on Eye Movements in Programming (EMIP))
The evolution and effort in designing and implementing iTrace, an infrastructure for integrating eye tracking into developer environments, is presented. The goal is to make eye tracking practical for various stakeholders in software engineering namely researchers, practitioners, and educators. An overview of iTrace and the general process involved in conducting an eye tracking study with human subjects using iTrace is presented in this tool demo paper. Upcoming features and ongoing plans for community involvement are also presented.
Sharif, B; Peterson, C; Guarnera, D; Bryant, C; Buchanan, Z; Zyrianov, V; Maletic, J
(, Proceedings of the 6th International Workshop on Eye Movements in Programming (EMIP))
The evolution and effort in designing and implementing iTrace, an infrastructure for integrating eye tracking into developer environments, is presented. The goal is to make eye tracking practical for various stakeholders in software engineering namely researchers, practitioners, and educators. An overview of iTrace and the general process involved in conducting an eye tracking study with human subjects using iTrace is presented in this tool demo paper. Upcoming features and ongoing plans for community involvement are also presented.
Roy, Devjeet; Fakhoury, Sarah; Arnaoudova, Venera
(, ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings)
null
(Ed.)
Recent research in empirical software engineering is applying techniques from neurocognitive science and breaking new grounds in the ways that researchers can model and analyze the cognitive processes of developers as they interact with software artifacts. However, given the novelty of this line of research, only one tool exists to help researchers represent and analyze this kind of multi-modal biometric data. While this tool does help with visualizing temporal eyetracking and physiological data, it does not allow for the mapping of physiological data to source code elements, instead projecting information over images of code. One drawback of this is that researchers are still unable to meaningfully combine and map physiological and eye tracking data to source code artifacts. The use of images also bars the support of long or multiple code files, which prevents researchers from analyzing data from experiments conducted in realistic settings. To address these drawbacks, we propose VITALSE, a tool for the interactive visualization of combined multi-modal biometric data for software engineering tasks. VITALSE provides interactive and customizable temporal heatmaps created with synchronized eyetracking and biometric data. The tool supports analysis on multiple files, user defined annotations for points of interest over source code elements, and high level customizable metric summaries for the provided dataset. VITALSE, a video demonstration, and sample data to demonstrate its capabilities can be found at http://www.vitalse.app.
For several years, the software engineering research community used eye trackers to study program comprehension, bug localization, pair programming, and other software engineering tasks. Eye trackers provide researchers with insights on software engineers’ cognitive processes, data that can augment those acquired through other means, such as on-line surveys and questionnaires. While there are many ways to take advantage of eye trackers, advancing their use requires defining standards for experimental design, execution, and reporting. We begin by presenting the foundations of eye tracking to provide context and perspective. Based on previous surveys of eye tracking for programming and software engineering tasks and our collective, extensive experience with eye trackers, we discuss when and why researchers should use eye trackers as well as how they should use them. We compile a list of typical use cases—real and anticipated—of eye trackers, as well as metrics, visualizations, and statistical analyses to analyze and report eye-tracking data. We also discuss the pragmatics of eye tracking studies. Finally, we offer lessons learned about using eye trackers to study software engineering tasks. This paper is intended to be a one-stop resource for researchers interested in designing, executing, and reporting eye tracking studies of software engineering tasks.
@article{osti_10217103,
place = {Country unknown/Code not available},
title = {gazel: Supporting Source Code Edits in Eye-Tracking Studies},
url = {https://par.nsf.gov/biblio/10217103},
abstractNote = {Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace community infrastructure. We introduce the iTrace-Atom plugin and gazel—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring.},
journal = {International Conference on Software Engineering (ICSE) - Demonstrations Track},
author = {Fakhoury, Sarah and Roy, Devjeet and Pines, Harry and Clevelandand, Tyler and Peterson, Cole S. and Arnaoudova, Venera and Sharif, Bonita and Maletic, Jonathan I.},
editor = {null}
}
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