.png){kind=icon}
Return
Problems with Precision: A Response to comments on 'data mining static code attributes to learn defect predictors'
.png){kind=icon}
OA
AI
DOI：10.1109/TSE.2007.70721.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
defect prediction
accuracy measures
static code attributes
empirical
Journal
IF:
5.6
Papers:
2.9K
Citations:
1.1W
Organization
No organization information available
Cited Papers
.png){kind=icon}
Formation of coronene:water complexes: FTIR study in argon matrices and theoretical characterisation
PREAI
ShareSaveShareSave
View more