Using the support vector machine as a classification method for software defect prediction with static code metrics

Gray, D. and Bowes, D. and Davey, N. and Sun, Y. and Christianson, B. (2009) Using the support vector machine as a classification method for software defect prediction with static code metrics. In: International Conference on Engineering Applications of Neural Networks EANN 2009. Communications in Computer and Information Science . Springer, pp. 223-234. ISBN 9783642039683

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Abstract

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: Contribution in Book/Report/Proceedings
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 132050
Deposited By: ep_importer_pure
Deposited On: 18 Mar 2019 09:50
Refereed?: Yes
Published?: Published
Last Modified: 15 Jan 2020 07:01
URI: https://eprints.lancs.ac.uk/id/eprint/132050

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