Some code smells have a significant but small effect on faults
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 010-96679-11409
- 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
- September
- Year of publication
- 2014
- URL
-
https://eprints.lancs.ac.uk/id/eprint/127419/1/Tosem_code_smells.pdf
- 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)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 68
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research was jointly undertaken with the U of Hertfordshire and resulted in a static analyser to detect so-called "bad smells" for java code. The code analyser is freely available from sourceforge, Soft112 (80 downloads and an average rating of 4/5) plus an Eclipse plug-in on GitHub. The associated empirical analysis of various large open source java systems has led to practical advice for developers and has influenced subsequent analyses by teams such as Tim Menzies's group at North Carolina U. Furthermore, the paper is the 4th most cited among all papers published in ACM TSEM since 2014.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
