Mining reading patterns from eye-tracking data: method and demonstration

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Abstract

Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.

Original languageEnglish
JournalSoftware and Systems Modeling
Number of pages39
ISSN1619-1366
DOIs
Publication statusAccepted/In press - 2019

Keywords

  • Process mining
  • Eye-tracking
  • Reading patterns
  • Source code
  • Behavior driven development

Cite this

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title = "Mining reading patterns from eye-tracking data: method and demonstration",
abstract = "Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.",
keywords = "Process mining, Eye-tracking, Reading patterns, Source code, Behavior driven development",
author = "Constantina Ioannou and Indira Nurdiani and Andrea Burattin and Barbara Weber",
year = "2019",
doi = "10.1007/s10270-019-00759-4",
language = "English",
journal = "Software and Systems Modeling",
issn = "1619-1366",
publisher = "Springer",

}

Mining reading patterns from eye-tracking data: method and demonstration. / Ioannou, Constantina; Nurdiani, Indira; Burattin, Andrea; Weber, Barbara.

In: Software and Systems Modeling, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Mining reading patterns from eye-tracking data: method and demonstration

AU - Ioannou, Constantina

AU - Nurdiani, Indira

AU - Burattin, Andrea

AU - Weber, Barbara

PY - 2019

Y1 - 2019

N2 - Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.

AB - Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eye-tracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior-driven development, a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.

KW - Process mining

KW - Eye-tracking

KW - Reading patterns

KW - Source code

KW - Behavior driven development

U2 - 10.1007/s10270-019-00759-4

DO - 10.1007/s10270-019-00759-4

M3 - Journal article

JO - Software and Systems Modeling

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SN - 1619-1366

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