A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning-Reference-Cited by-同舟云学术

A Survey of Feature Selection for Vulnerability Prediction Using Feature-based Machine Learning

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 2019 11th International Conference on Machine Learning and Computing - ICMLC '19
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Author:

Li ZhanJun¹,Shao Yan¹

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui Province

Publisher

ACM Press

Reference32 articles.

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3. S. Lessmann, B. Baesens, C. Mues, and S. Pietsch. 2008. Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng., vol. 34, no. 4, pp. 485--496, Jul./Aug. 2008.

4. T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener. 2010. Defect prediction from static code features: current results, limitations, new approaches. Automated Software. Eng., vol. 17, no. 4, pp. 375--407, 2010.

5. N. Nagappan, T. Ball, and B. Murphy. 2006. Using historical in-process and product metrics for early estimation of software failures. In Proc. Int. Symp. Softw. Rel. Eng., 2006, pp. 62--74.

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