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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Detecting Developers' Task Switches and Types |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| 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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| Additional Information | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work |