Mutation-aware fault prediction
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 250153415
- Type
- E - Conference contribution
- DOI
-
10.1145/2931037.2931039
- Title of conference / published proceedings
- ISSTA 2016 Proceedings of the 25th International Symposium on Software Testing and Analysis
- First page
- 330
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2016
- 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
-
5
- Research group(s)
-
H - Software Engineering
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper has been the foundation for research which combines mutation testing, metrics, feature analysis and defect prediction. The combination of these research topics is unique and has enabled others to demonstrate the usefulness of new metrics to improve prediction accuracy and thereby reduce the potential for software to fail. The paper is part of our ongoing research into defect prediction using metrics (~£1M EPSRC grant) and is being extended in collaboration with multination industrial partners. Follow on work by others has confirmed our results and has led to a better understanding of the usefulness of mutation testing.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
