Some code smells have a significant but small effect on faults
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 250153167
- Type
- D - Journal article
- DOI
-
10.1145/2629648
- Title of journal
- ACM Transactions on Software Engineering and Methodology
- Article number
- 33
- First page
- -
- Volume
- 23
- Issue
- 4
- ISSN
- 1049-331X
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- 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
-
3
- Research group(s)
-
H - Software Engineering
- Citation count
- 68
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first to report the effect of the size of the relationship between code smells and defects showing that the impact of three code smells on the number of defects in code is minimal. It has led to subsequent code smell studies reporting effect sizes. Findings from this paper led to a EPSRC funded defect prediction project (ELFF, EP/LO11751) which implemented a highly usable machine learning models allowing developers to predict defects in their code. The prediction models generated by the ELFF project were evaluated in use by Sky PLC developers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
