Comparative Performance of Fault-Prone Prediction Classes with K-means Clustering and MLP-Reference-Cited by-同舟云学术

Comparative Performance of Fault-Prone Prediction Classes with K-means Clustering and MLP

Published:2016 Issue: Volume: Page:
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Container-title:Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies - ICTCS '16
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Author:

Singh Satwinder¹,Singla Rozy²

Affiliation:

1. Central University of Punjab, Bathinda

2. Shankara Institute of Technology, Jaipur

Publisher

ACM Press

Reference14 articles.

1. Ramaswamy, V., Pushphavathi, T.P., and Suma, V., "Defect Prediction Apporaches for Software Projects using Genetic Fuzzy Data Mining", ICT and Critical Infrastructure, Proceedings of the 48th Annual Convention of CSI Vol II, CSI Vishakapatnam Chapter, 13-15 Dec, 2013, pp. 313--320.

2. Rawat, M.S., Dubey, S.K., "Software Defect Prediction Models for Quality Improvement: A Literature Study", IJCSI International Journal of Computer Science Issues, Vol. 9, No 2, pp. 288--296, September, 2012.

3. Sharma, R., Budhija, N., Singh, B., "Study of Predicting Fault Prone Software Modules", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 2, February 2012.

4. Rahman, F., Posnett, D. and Devanbu, P., "Recalling the "im- precision" of cross-project defect prediction," in Proceedings of the ACM-Sigsoft 20th International Symposium on the Foundations of Software Engineering (FSE-20). Research Triangle Park, NC, USA: ACM, 11-16 Nov, 2012.

5. Fenton, N. E. and Neil, M., "A Critique of Software Defect Prediction Models", IEEE Transactions on Software Engineering, Vol. 25, No. 5, pp. 1--15, September-October 1999.

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