Robust Statistical Methods for Empirical Software Engineering
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 362
- Type
- D - Journal article
- DOI
-
10.1007/s10664-016-9437-5
- Title of journal
- Empirical Software Engineering
- Article number
- -
- First page
- 579
- Volume
- 22
- Issue
- 2
- ISSN
- 1382-3256
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2016
- URL
-
https://link.springer.com/article/10.1007%2Fs10664-016-9437-5
- 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
-
7
- Research group(s)
-
-
- Citation count
- 71
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This international collaboration, led. by Kitchenham, tested and expanded on her earlier work on statistical methods for computer science (1996 book ISBN:978-1-85554-765-0). The paper is the first thorough analysis of inappropriate or non-robust statistical methods in softawre engineering, and is widely cited -- in software engineering and beyond -- to support the validity of experimental design and statistical analysis (e.g. COCOMO: doi.org/10.1016/j.jss.2018.10.019; software vulnerabilities, doi.org/10.1016/j.infsof.2018.06.005, etc.).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -