The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Empirical validation of object-oriented metrics on open source software for fault prediction
Abstract
Open source software systems are becoming increasingly important these days. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database - called Bugzilla - using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.
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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
Software fault prediction metrics: A systematic literature review
Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013) · Information and Software Technology · doi:10.1016/j.infsof.2013.02.009 · Cited by 538
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
An empirical study on software defect prediction with a simplified metric set
He, P., Li, B., Liu, X., Chen, J., & Ma, Y. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2014.11.006 · Cited by 309
Metadata
- Authors
- Gyimothy, T., Ferenc, R., & Siket, I.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2005-10
- Volume
- 31
- Issue
- 10
- Pages
- 897-910
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2128666163
- Cited by
- 904
- References
- 3
- FWCI
- 94.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gyimothy, T., Ferenc, R., & Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2005.112
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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.
