PCA Based Improved Feature Selection Using Software Metrics Correlation Data for Improved Decision Making
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Abstract
A large number of metrics are available in the market today so it’s difficult to choose the relevant metrics for a particular context and further to control them. Thus, correlation is mandatory to be calculated between various metrics. PCA based feature selection is applied on the correlated metrics data for finding the most significant features. These features can be used for improving decision making by making available the principal features, reducing the use of redundant metrics.
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Mathur, Kirti, and Amber Jain. "A Comparative Survey of Software Quality Metrics."International Journal of Research(2013),Issue-4,Volume.2
E. Fenton and Martin Neil, Software Metrics: Roadmap, International Conference on Software Engineering, Limerick, Ireland, pp 357-370, 2000,
Honglei, Tu, Sun Wei, and Zhang Yanan. "The research on software metrics and software complexity metrics."Computer Science-Technology and Applications, 2009.IFCSTA'09.International Forum on.Vol. 1. IEEE, 2009
Tashtoush, Yahva, Mohammed Al-Maolegi, and Bassam Arkok. “The correlation among Software Complexity Metrics with Case Study.”International Journal of Advanced Computer Research:Issue-15,Vol.4 (2014).
Senousy, Mohamed B., and Tamer ShMazen. “Correlations and Weights of Maintainability Index (MI) of Open source Linux Kernel Modules.” International Journal of Computer Applications 91.7 (2014):30-37.
Rawat, Mrinal Singh, Arpita Mittal, and Sanjay Kumar Dubey. "Survey on Impact of Software Metrics on Software Quality." International Journal of Advanced Computer Science & Applications 3.1 (2012).
Debbarma, MrinalKanti, et al. "A Review and Analysis of Software Complexity Metrics in Structural Testing." International Journal of Computer and Communication Engineering 2 (2013): 129-133.
Pressman, Roger S. Software engineering: a practitioner's approach. Palgrave Macmillan, 2005.
Gao, Kehan, et.al “Choosing software metrics for defect prediction: An investigation on feature selection techniques.” Software: Practice and Experience 41.5(2011):579-606.
Wang, Huanjing, et.al “Measuring robustness of feature selection techniques on software engineering datasets.” Information Reuse and Integration (IRI), 2011 IEEE International Conference on IEEE,2011.
Bro, Rasmus, and Age K. Smilde. "Principal component analysis."Analytical Methods 6.9 (2014): 2812-2831