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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Detecting Developers' Task Switches and Types
Organization Unit
  • André Meyer
  • Chris Satterfield
  • Manuela Züger
  • Katja Kevic
  • Gail C. Murphy
  • Thomas Zimmermann
  • Thomas Fritz
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title IEEE transactions on software engineering
Publisher Institute of Electrical and Electronics Engineers
Geographical Reach international
ISSN 0098-5589
Volume 48
Number 1
Page Range 225 - 240
Date 2022
Abstract Text Developers work on a broad variety of tasks during their workdays and constantly switch between them. While these task switches can be beneficial, they can also incur a high cognitive burden on developers, since they have to continuously remember and rebuild task context - the artifacts and applications relevant to the task. Researchers have therefore proposed to capture task context more explicitly and use it to provide better task support, such as task switch reduction or task resumption support. Yet, these approaches generally require the developer to manually identify task switches. Automatic approaches for predicting task switches have so far been limited in their accuracy, scope, evaluation, and the time discrepancy between predicted and actual task switches. In our work, we examine the use of automatically collected computer interaction data for detecting developers' task switches as well as task types. In two field studies - a 4h observational study and a multi-day study with experience sampling - we collected data from a total of 25 professional developers. Our study results show that we are able to use temporal and semantic features from developers' computer interaction data to detect task switches and types in the field with high accuracy of 84% and 61% respectively, and within a short time window of less than 1.6 minutes on average from the actual task switch. We discuss our findings and their practical value for a wide range of applications in real work settings.
Free access at DOI
Digital Object Identifier 10.1109/TSE.2020.2984086
Other Identification Number merlin-id:19540
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