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Applying Machine Learning to Gaze Data in Software Development: a Mapping Study

Kuang, Peng LU orcid ; Söderberg, Emma LU orcid ; Niehorster, Diederick C LU orcid and Höst, Martin LU (2023) Eleventh International Workshop on Eye Movements in Programming, EMIP 2023
Abstract
Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies.

In this paper, our objective is to summarize the research literature with respect to the application of ML techniques to gaze data in the field of software engineering. To this end, we have conducted a systematic mapping study, where research articles are identified through a search in... (More)
Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies.

In this paper, our objective is to summarize the research literature with respect to the application of ML techniques to gaze data in the field of software engineering. To this end, we have conducted a systematic mapping study, where research articles are identified through a search in academic databases and analyzed qualitatively. After identifying 10 relevant articles, we found that the most common software development activity studied so far with eye-tracking and ML is program comprehension, and Support Vector Machines and Decision Trees are the most commonly used ML techniques. We further report on limitations and challenges reported in the literature and opportunities for future work. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
The 11th International Workshop on Eye Movements in Programming
conference name
Eleventh International Workshop on Eye Movements in Programming, EMIP 2023
conference location
Tübingen, Germany
conference dates
2023-06-02 - 2023-06-02
external identifiers
  • scopus:85161110037
DOI
10.1145/3588015.3589190
project
How Can Eye Tracking Support Programmers?
Adaptive Developer Tools
language
English
LU publication?
yes
id
fecd50ce-a7f3-4ece-9fc5-06019de8bf4e
date added to LUP
2023-03-31 14:15:07
date last changed
2024-04-20 12:14:37
@inproceedings{fecd50ce-a7f3-4ece-9fc5-06019de8bf4e,
  abstract     = {{Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies. <br/><br/>In this paper, our objective is to summarize the research literature with respect to the application of ML techniques to gaze data in the field of software engineering. To this end, we have conducted a systematic mapping study, where research articles are identified through a search in academic databases and analyzed qualitatively. After identifying 10 relevant articles, we found that the most common software development activity studied so far with eye-tracking and ML is program comprehension, and Support Vector Machines and Decision Trees are the most commonly used ML techniques. We further report on limitations and challenges reported in the literature and opportunities for future work.}},
  author       = {{Kuang, Peng and Söderberg, Emma and Niehorster, Diederick C and Höst, Martin}},
  booktitle    = {{The 11th International Workshop on Eye Movements in Programming}},
  language     = {{eng}},
  title        = {{Applying Machine Learning to Gaze Data in Software Development: a Mapping Study}},
  url          = {{http://dx.doi.org/10.1145/3588015.3589190}},
  doi          = {{10.1145/3588015.3589190}},
  year         = {{2023}},
}