Exploring eye tracking data on source code via dual space analysis

Li Zhang, Jianxin Sun, Cole Peterson, Bonita Sharif, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.

Original languageEnglish (US)
Title of host publicationProceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-77
Number of pages11
ISBN (Electronic)9781728149394
DOIs
StatePublished - Sep 2019
Event7th IEEE Working Conference on Software Visualization, VISSOFT 2019 - Cleveland, United States
Duration: Sep 30 2019Oct 1 2019

Publication series

NameProceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019

Conference

Conference7th IEEE Working Conference on Software Visualization, VISSOFT 2019
CountryUnited States
CityCleveland
Period9/30/1910/1/19

Fingerprint

Visualization
Eye movements
Software engineering
Fluids

Keywords

  • Content space
  • Developer classification
  • Embedded space
  • Eye tracking
  • Visualization

ASJC Scopus subject areas

  • Software
  • Media Technology

Cite this

Zhang, L., Sun, J., Peterson, C., Sharif, B., & Yu, H. (2019). Exploring eye tracking data on source code via dual space analysis. In Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019 (pp. 67-77). [8900970] (Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VISSOFT.2019.00016

Exploring eye tracking data on source code via dual space analysis. / Zhang, Li; Sun, Jianxin; Peterson, Cole; Sharif, Bonita; Yu, Hongfeng.

Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 67-77 8900970 (Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, L, Sun, J, Peterson, C, Sharif, B & Yu, H 2019, Exploring eye tracking data on source code via dual space analysis. in Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019., 8900970, Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019, Institute of Electrical and Electronics Engineers Inc., pp. 67-77, 7th IEEE Working Conference on Software Visualization, VISSOFT 2019, Cleveland, United States, 9/30/19. https://doi.org/10.1109/VISSOFT.2019.00016
Zhang L, Sun J, Peterson C, Sharif B, Yu H. Exploring eye tracking data on source code via dual space analysis. In Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 67-77. 8900970. (Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019). https://doi.org/10.1109/VISSOFT.2019.00016
Zhang, Li ; Sun, Jianxin ; Peterson, Cole ; Sharif, Bonita ; Yu, Hongfeng. / Exploring eye tracking data on source code via dual space analysis. Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 67-77 (Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019).
@inproceedings{89a5f675977c40dc8f108e7200a2b8b2,
title = "Exploring eye tracking data on source code via dual space analysis",
abstract = "Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.",
keywords = "Content space, Developer classification, Embedded space, Eye tracking, Visualization",
author = "Li Zhang and Jianxin Sun and Cole Peterson and Bonita Sharif and Hongfeng Yu",
year = "2019",
month = "9",
doi = "10.1109/VISSOFT.2019.00016",
language = "English (US)",
series = "Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "67--77",
booktitle = "Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019",

}

TY - GEN

T1 - Exploring eye tracking data on source code via dual space analysis

AU - Zhang, Li

AU - Sun, Jianxin

AU - Peterson, Cole

AU - Sharif, Bonita

AU - Yu, Hongfeng

PY - 2019/9

Y1 - 2019/9

N2 - Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.

AB - Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.

KW - Content space

KW - Developer classification

KW - Embedded space

KW - Eye tracking

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=85075858648&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075858648&partnerID=8YFLogxK

U2 - 10.1109/VISSOFT.2019.00016

DO - 10.1109/VISSOFT.2019.00016

M3 - Conference contribution

AN - SCOPUS:85075858648

T3 - Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019

SP - 67

EP - 77

BT - Proceedings - 7th IEEE Working Conference on Software Visualization, VISSOFT 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -