Using the support vector machine as a classification method for software defect prediction with static code metrics
Gray, David, Bowes, D., Davey, N., Sun, Yi and Christianson, B.
(2009)
Using the support vector machine as a classification method for software defect prediction with static code metrics.
Communications in Computer and Information Science.
pp. 223-234.
ISSN 1865-0929
The automated detection of defective modules within software systems could lead to reduced development costs and more reliable software. In this work the static code metrics for a collection of modules contained within eleven NASA data sets are used with a Support Vector Machine classifier. A rigorous sequence of pre-processing steps were applied to the data prior to classification, including the balancing of both classes (defective or otherwise) and the removal of a large number of repeating instances. The Support Vector Machine in this experiment yields an average accuracy of 70% on previously unseen data.
Item Type | Article |
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Date Deposited | 29 May 2025 09:12 |
Last Modified | 29 May 2025 09:12 |