Problems with Precision: A Response to "comments on 'data mining static code attributes to learn defect predictors'"

被引:173
作者
Menzies, Tim [1 ]
Dekhtyar, Alex
Distefano, Justin
Greenwald, Jeremy
机构
[1] W Virginia Univ, Dept Comp Sci & Engn, Morgantown, WV 26506 USA
[2] Calif Polytech State Univ San Luis Obispo, Dept Comp Sci, San Luis Obispo, CA 93407 USA
[3] Integrated Software Metr, Fairmont, WV 26554 USA
[4] Portland State Univ, Dept Comp Sci, Portland, OR 97207 USA
关键词
defect prediction; accuracy measures; static code attributes; empirical;
D O I
10.1109/TSE.2007.70721
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Zhang and Zhang argue that predictors are useless unless they have high precison&recall. We have a different view, for two reasons. First, for SE data sets with large neg/pos ratios, it is often required to lower precision to achieve higher recall. Second, there are many domains where low precision detectors are useful.
引用
收藏
页码:637 / 640
页数:4
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