Inference of development activities from interaction with uninstrumented applications
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Claims other papers make about this one
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That one approach was limited to specific development activities only and did not consider the whole range of non-development tasks.
“Yet, their examination was limited to specific development activities only, and did not consider the whole range of non-development tasks that developers are working on, such as administrative or planning tasks.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [22]
Only one approach so far has looked at automatic classification of developers' activities on a task level.
“To the best of our knowledge, there has been only one approach so far that looked at the automatic classification of developers' activities on a task level [22].”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [22]
Bao et al. found that a Conditional Random Field is able to classify development activity with 73 percent accuracy.
“An analysis of data collected from ten developers over a week shows that CRF is able to classify the activity with 73 percent accuracy.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [22]
The classifier can make predictions even on previously unseen applications, distinguishing it from previous work.
“One important aspect of our approach that distinguishes our classifier from previous work (e.g., [15], [22], [47]) is its ability to make predictions even on previously unseen applications.”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [22]
Only very little prior work exists on automatically detecting, characterizing and summarizing developers' tasks.
“We have been able to find only very little work on automatically detecting, characterizing and summarizing (developers) tasks yet [22], [32], [33].”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [22]
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Inference of development activities from interaction with uninstrumented applications
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Self-citationCAPIRS: COVID-19-Based Application Programming Interface Recommendation System for the Developers
Nawaz, M. S., Khan, S. U. R., Ahmad, B., Iqbal, J., & Inayat-ur-Rehman (2022) · Journal of Information & Knowledge Management · doi:10.1142/s0219649222400044
- Authors
- Bao, L., Xing, Z., Xia, X., Lo, D., & Hassan, A. E.
- Affiliations
- Zhejiang University (CN)
- Australian National University (AU)
- Monash University (AU)
- Singapore Management University (SG)
- Queen's University (CA)
- Corresponding author
- Xia, Xin
- Venue
- Empirical Software Engineering
- Published
- 2017-09-16
- Volume
- 23
- Issue
- 3
- Pages
- 1313-1351
- ISSN
- 1382-3256 (ISSN-L)
- 1573-7616
- OpenAlex
- W2755579216
- Cited by
- 25
- References
- 57
- Citations by year
- 2022: 4 · 2021: 4 · 2020: 9 · 2019: 3 · 2018: 5
- FWCI
- 8.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Empirical Software Engineering (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- 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 › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- developer behavior, sequence labeling
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