The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Data Mining Static Code Attributes to Learn Defect Predictors
Abstract
The value of using static code attributes to learn defect predictors has been widely debated. Prior work has explored issues like the merits of "McCabes versus Halstead versus lines of code counts" for generating defect predictors. We show here that such debates are irrelevant since how the attributes are used to build predictors is much more important than which particular attributes are used. Also, contrary to prior pessimism, we show that such defect predictors are demonstrably useful and, on the data studied here, yield predictors with a mean probability of detection of 71 percent and mean false alarms rates of 25 percent. These predictors would be useful for prioritizing a resource-bound exploration of code that has yet to be inspected
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
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
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
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
Defect prediction from static code features: current results, limitations, new approaches
Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., & Bener, A. (2010) · Automated Software Engineering · doi:10.1007/s10515-010-0069-5 · Cited by 466
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Metadata
- Authors
- Menzies, T., Greenwald, J., & Frank, A.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2007-01
- Volume
- 33
- Issue
- 1
- Pages
- 2-13
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W3141989311
- Cited by
- 1,215
- References
- 1
- FWCI
- 78.78
- Type
- journal article
- Language
- en
- Open access
- closed
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