A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 054-187712-8735
- Type
- D - Journal article
- DOI
-
10.1109/TSE.2018.2836442
- Title of journal
- Ieee Transactions On Software Engineering
- Article number
- -
- First page
- 1253
- Volume
- 45
- Issue
- 12
- ISSN
- 0098-5589
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2018
- URL
-
https://bura.brunel.ac.uk/handle/2438/16318
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 26
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research arose from collaborative research with the Xi'an Jiaotong University and supported by a Visiting Research Student Scholarship funded by the Chinese government. The outcome is a comprehensive set of recommendations on the use imbalanced learning algorithms for software defect prediction. It has assisted subsequent research teams e.g.,South China U of Technology, Zhejiang/Monash/Otago/Singapore/Queen's (Ca), etc. The ideas are also being picked up by the security vulnerability detection community. It is ranked 3/122 for citations for all TSE papers since 2019. The data and results are on Zenodo; they have been viewed 1200+ and downloaded 340+ times.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
