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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Using the support vector machine as a classification method for software defect prediction with static code metrics. / Gray, D.; Bowes, D.; Davey, N. et al.
International Conference on Engineering Applications of Neural Networks EANN 2009: Engineering Applications of Neural Networks. Springer, 2009. p. 223-234 (Communications in Computer and Information Science; Vol. 43).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gray, D, Bowes, D, Davey, N, Sun, Y & 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: Engineering Applications of Neural Networks. Communications in Computer and Information Science, vol. 43, Springer, pp. 223-234. https://doi.org/10.1007/978-3-642-03969-0_21

APA

Gray, D., Bowes, D., Davey, N., Sun, Y., & 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: Engineering Applications of Neural Networks (pp. 223-234). (Communications in Computer and Information Science; Vol. 43). Springer. https://doi.org/10.1007/978-3-642-03969-0_21

Vancouver

Gray D, Bowes D, Davey N, Sun Y, Christianson B. 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: Engineering Applications of Neural Networks. Springer. 2009. p. 223-234. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-03969-0_21

Author

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

Bibtex

@inproceedings{9808864595fd497e9b7bd7c46bdceb9f,
title = "Using the support vector machine as a classification method for software defect prediction with static code metrics",
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.",
keywords = "fuzzy, software, artificial intelligence, cluster analysis, data mining, data reconstruction, development, face recognition, fuzzy logic, Intelligent agents, modeling, programming, quality, quality assurance, visualization",
author = "D. Gray and D. Bowes and N. Davey and Y. Sun and B. Christianson",
year = "2009",
doi = "10.1007/978-3-642-03969-0_21",
language = "English",
isbn = "9783642039683",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "223--234",
booktitle = "International Conference on Engineering Applications of Neural Networks EANN 2009",

}

RIS

TY - GEN

T1 - Using the support vector machine as a classification method for software defect prediction with static code metrics

AU - Gray, D.

AU - Bowes, D.

AU - Davey, N.

AU - Sun, Y.

AU - Christianson, B.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - fuzzy

KW - software

KW - artificial intelligence

KW - cluster analysis

KW - data mining

KW - data reconstruction

KW - development

KW - face recognition

KW - fuzzy logic

KW - Intelligent agents

KW - modeling

KW - programming

KW - quality

KW - quality assurance

KW - visualization

U2 - 10.1007/978-3-642-03969-0_21

DO - 10.1007/978-3-642-03969-0_21

M3 - Conference contribution/Paper

SN - 9783642039683

T3 - Communications in Computer and Information Science

SP - 223

EP - 234

BT - International Conference on Engineering Applications of Neural Networks EANN 2009

PB - Springer

ER -