Supplementary Material for the Paper "Detecting Developers' Task Switches and Types"
| Main Authors: | André N Meyer, Manuela Züger, Chris Satterfield, Katja Kevic, Gail C Murphy, Thomas Zimmermann, Thomas Fritz |
|---|---|
| Format: | info Lainnya eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2019
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| Online Access: |
https://zenodo.org/record/3238263 |
Daftar Isi:
- Contains the supplementary material for the paper "Detecting Developers' Task Switches and Types". All contents are explained in the file README.txt. Abstract: Developers' work on a broad variety of tasks during their days and constantly switch between tasks. 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 this to provide better task support, such as task switch reduction or 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 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 87% 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. Source Code: Upon acceptance of the paper, a link to GitHub where the monitoring tool and observation tool used for the field studies can be found will be provided here.