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Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"
September 2007 (vol. 33 no. 9)
pp. 635-637
In this correspondence, we point out a discrepancy in a recent paper "Data Mining Static Code Attributes to Learn Defect Predictors" that was published in this journal. Because of the small percentage of defective modules, using pd and pf as accuracy measures may lead to impractical prediction models.

[1] T. Menzies, J. Greenwald, and A. Frank, “Data Mining Static Code Attributes to Learn Defect Predictors,” IEEE Trans. Software Eng., vol. 33, no. 1, Jan. 2007.
[2] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. ACM Press, 1999.
[3] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.

Index Terms:
defect prediction, accuracy measures, static code attributes, empirical
Citation:
Hongyu Zhang, Xiuzhen Zhang, "Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"," IEEE Transactions on Software Engineering, vol. 33, no. 9, pp. 635-637, Sept. 2007, doi:10.1109/TSE.2007.70706
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