The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction
Abstract
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.
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Citations
Cites
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
Cited by
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
A systematic review of machine learning techniques for software fault prediction
Malhotra, R. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2014.11.023 · Cited by 620
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.91 · Cited by 364
A survey of AIOps methods for failure management
Notaro, P., Cardoso, J., & Gerndt, M. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3483424 · Cited by 142
Examining the Impact of Self-Admitted Technical Debt on Software Quality
Wehaibi, S., Shihab, E., & Guerrouj, L. (2016) · 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) · doi:10.1109/saner.2016.72 · Cited by 114
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture
Cai, Y., Xiao, L., Kazman, R., Mo, R., & Feng, Q. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2797899 · Cited by 39
On the Relationship between Program Evolution and Fault-Proneness: An Empirical Study
Jaafar, F., Hassaine, S., Gueheneuc, Y., Hamel, S., & Adams, B. (2013) · 2013 17th European Conference on Software Maintenance and Reengineering · doi:10.1109/csmr.2013.12 · Cited by 5
Metadata
- Authors
- Moser, R., Pedrycz, W., & Succi, G.
- Venue
- Proceedings of the 13th international conference on Software engineering - ICSE '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 181
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2132887549
- Cited by
- 701
- References
- 1
- FWCI
- 19.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
