Robust Statistical Methods for Empirical Software Engineering
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 103130
- 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
- 13823256
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
https://doi.org/10.1007/s10664-016-9437-5
- 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)
-
A - Innovative Computing
- Citation count
- 71
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The guidance related to the use of statistical analysis with software engineering (and computing) data related to human-centric studies that is provided in this paper has subsequently been followed by many papers in the empirical software engineering literature. It has also been adopted in a number of papers related to other branches of computing that also employ empirical studies, and where these produce data profiles of a similar form to those described here.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -