Researcher bias : The use of machine learning in software defect prediction
- Submitting institution
-
University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13599943
- Type
- D - Journal article
- DOI
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10.1109/TSE.2014.2322358
- Title of journal
- IEEE Transactions in Software Engineering
- Article number
- 6824804
- First page
- 603
- Volume
- 40
- Issue
- 6
- ISSN
- 0098-5589
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- 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)
-
-
- Citation count
- 132
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has had a major impact on the community, evidenced by comments received in the same journal and by it being cited in seven review papers, often as a starting point to carry out follow-up research. The study used a very large set derived from the highest impact transaction paper in the last five years. Recent challenges to the methodology have been rebutted and it has been shown that other researchers can only change the conclusion by using weak techniques. Hall and Bowes have since taken up a professorship and a senior lectureship at Lancaster University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -