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Yao, \u201cA learning-to-rank approach to software defect prediction,\u201d IEEE Trans. Rel., vol.64, no.1, pp.234-246, 2015. 10.1109\/tr.2014.2370891","DOI":"10.1109\/TR.2014.2370891"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] X. Xia, D. Lo, S.J. Pan, N. Nagappan, and X. Wang, \u201cHYDRA: Massively Compositional Model for Cross-Project Defect Prediction,\u201d IEEE Trans. Softw. Eng., vol.42, no.10, pp.977-998, 2016. 10.1109\/tse.2016.2543218","DOI":"10.1109\/TSE.2016.2543218"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] \u00d6.F. Arar and K. Ayan, \u201cA feature dependent naive Bayes approach and its application to the software defect prediction problem,\u201d Applied Soft Computing, vol.59, pp.197-209, 2017.","DOI":"10.1016\/j.asoc.2017.05.043"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] T. Menzies, J. Greenwald, and A. Frank, \u201cData mining static code attributes to learn defect predictors,\u201d IEEE Trans. Softw. Eng., vol.33, no.1, pp.2-13, 2007. 10.1109\/tse.2007.256941","DOI":"10.1109\/TSE.2007.256941"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] B. Turhan and A. Bener, \u201cAnalysis of naive bayes&apos; assumptions on software fault data: An empirical study,\u201d Data &amp; Knowledge Engineering, vol.68, no.2, pp.278-290, 2009. 10.1016\/j.datak.2008.10.005","DOI":"10.1016\/j.datak.2008.10.005"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] C. Jin and J.-A. Liu, \u201cApplications of Support Vector Machine and Unsupervised Learning for Predicting Maintainability Using Object-Oriented Metrics,\u201d Second International Conference on Multimedia and Information Technology, pp.24-27, 2010. 10.1109\/mmit.2010.10","DOI":"10.1109\/MMIT.2010.10"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] H.M. Olague, L.H. Etzkorn, S. Gholston, and S. Quattlebaum, \u201cEmpirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes,\u201d IEEE Trans. Softw. Eng., vol.33, no.6, pp.402-419, 2007. 10.1109\/tse.2007.1015","DOI":"10.1109\/TSE.2007.1015"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] G. Jagannathan, K. Pillaipakkamnatt, and R.N. Wright, \u201cA Practical Differentially Private Random Decision Tree Classifier,\u201d IEEE International Conference on Data Mining Workshops, pp.114-121, 2009. 10.1109\/icdmw.2009.93","DOI":"10.1109\/ICDMW.2009.93"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] Z. He, F. Shu, Y. Yang, M. Li, and Q. Wang, \u201cAn investigation on the feasibility of cross-project defect prediction,\u201d Automated Software Engineering, vol.19, no.2, pp.167-199, 2012. 10.1007\/s10515-011-0090-3","DOI":"10.1007\/s10515-011-0090-3"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] R. Malhotra, \u201cA systematic review of machine learning techniques for software fault prediction,\u201d Applied Soft Computing Journal, vol.27, no.c, pp.504-518, 2015. 10.1016\/j.asoc.2014.11.023","DOI":"10.1016\/j.asoc.2014.11.023"},{"key":"14","unstructured":"[14] N.M. Razali and Y.B. Wah, \u201cPower comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests,\u201d Journal of Statistical Modeling and Analytics, vol.2, no.1, pp.21-33, 2011."},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] E. Parzen, \u201cOn estimation of a probability density function and mode,\u201d The Annals of Mathematical Statistics, vol.33, no.3, pp.1065-1076, 1962. 10.1214\/aoms\/1177704472","DOI":"10.1214\/aoms\/1177704472"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] D.J. Hand and K. Yu, \u201cIdiot&apos;s Bayes: Not So Stupid after All?,\u201d International Statistical Review, vol.69, no.3, pp.385-398, 2001. 10.1111\/j.1751-5823.2001.tb00465.x","DOI":"10.1111\/j.1751-5823.2001.tb00465.x"},{"key":"17","unstructured":"[17] N.A. Zaidi, J. Cerquides, M.J. Carman, and G.I. Webb, \u201cAlleviating naive Bayes attribute independence assumption by attribute weighting,\u201d Journal of Machine Learning Research, vol.14, no.1, pp.1947-1988, 2013."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] C. Catal and B. Diri, \u201cInvestigating the effect of dataset size, metrics sets, and feature selection techniques on software,\u201d Information Sciences, vol.179, no.8, pp.1040-1058, 2009.","DOI":"10.1016\/j.ins.2008.12.001"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] C. Catal, \u201cSoftware fault prediction: A literature review and current trends,\u201d Expert Syst. Appl., vol.38, no.4, pp.4626-4636, 2011. 10.1016\/j.eswa.2010.10.024","DOI":"10.1016\/j.eswa.2010.10.024"},{"key":"20","unstructured":"[20] L.H. Witten, E. Frank, and M.A. Hell, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition, ACM Sigsoft Software Engineering Notes, pp.90-99, Morgan Kaufmann, Burlington, 2011."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] B.W. Silverman, Density Estimation for Statistics and Data Analysis, vol.26, CRC Press, London, 1986.","DOI":"10.1007\/978-1-4899-3324-9"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] M.P. Wand and M.C. Jones, Kernel Smoothing, CRC Press,London, 1994.","DOI":"10.1201\/b14876"},{"key":"23","unstructured":"[23] B.E. Hansen, Lecture notes on nonparametrics, University of Wisconsin-Madison, WI, USA, http:\/\/www.ssc.wisc.edu\/~bhansen\/718\/NonParametrics1.pdf, 2009."},{"key":"24","unstructured":"[24] A. Schindler, \u201cBandwidth selection in nonparametric kernel estimation,\u201d PhD Thesis, G\u00f6ttingen, Georg-August Universit\u00e4t, Diss, 2011."},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Analytical Methods Committee, \u201cRobust statistics-How not to reject outliers. Part 1: Basic concepts,\u201d Analyst, vol.114, no.12, pp.1693-1697, 1989. 10.1039\/an9891401693","DOI":"10.1039\/an9891401693"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu, \u201cA general software defect-proneness prediction framework,\u201d IEEE Trans. Softw. 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