Towards Activity-Aware Tool Support for Change Tasks
Open accessAbstract
To complete a change task, software developers perform a number of activities, such as locating and editing the relevant code. While there is a variety of approaches to support developers for change tasks, these approaches mainly focus on a single activity each. Given the wide variety of activities during a change task, a developer has to keep track of and switch between the different approaches. By knowing more about a developer's activities and in particular by knowing when she is working on which activity, we would be able to provide better and more tailored tool support, thereby reducing developer effort.In our research we investigate the characteristics of these activities, whether they can be identified, and whether we can use this additional information to improve developer support for change tasks. We conducted two exploratory studies with a total of 21 software developers collecting data on activities in the lab and field. An empirical analysis of the data shows, amongst other results, that activities comprise a consistently small amount of code elements across all developers and tasks (approx. 8.7 elements). Further analysis of the data shows, that we can automatically detect the boundaries and types of activities, and that the information on activity types can be used to improve the identification of relevant code elements.
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Kevic and Fritz found that activity switches as well as six self-identified activity types can be predicted with more than 75 percent accuracy.
“In two studies with 21 participants, they found that activity switches as well as the six self-identified activity types can be predicted with more than 75 percent accuracy.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [31]
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Detecting Developers' Task Switches and Types
Meyer, A. N., Satterfield, C., Zuger, M., Kevic, K., Murphy, G. C., Zimmermann, T., & Fritz, T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2984086 · Cited by 14 · see citing works
“Finally, on a more fine-grained level, Kevic and Fritz [31] examined the detection of activity switches and types within a change task using semantic, temporal and structural features.”
BackgroundInfluentialSelf-citation
- Authors
- Kevic, K., & Fritz, T.
- Affiliations
- University of Zurich (CH)
- ORCID
- Fritz, Thomas
- Venue
- 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 171-182
- ISSN
- 1063-6773 (ISSN-L)
- OpenAlex
- W2767747912
- Cited by
- 5
- References
- 47
- Citations by year
- 2021: 2 · 2020: 3
- FWCI
- 0.63
- Type
- conference paper
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- other-oa
- Locations
- IEEE International Conference on Software Maintenance (ICSM) (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- developer activity, activity detection, developer effort, software development support
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