Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20607
Title: An in-depth study of the potentially confounding effect of class size in fault prediction
Authors: Zhou, Y
Xu, B
Leung, H 
Chen, L
Keywords: Class size
Confounding effect
Fault
Metrics
Prediction
Issue Date: 2014
Publisher: Assoc Computing Machinery
Source: ACM transactions on software engineering and methodology, 2014, v. 23, no. 1, 2556777 How to cite?
Journal: ACM Transactions on Software Engineering and Methodology 
Abstract: The article offers an in-depth understanding of the effect of class size on the true associations between object-oriented metrics and fault-proneness. The third variable that distorts the true association between the independent and dependent variables is usually called a confounding variable or a confounder. The distortion that results from confounding may lead to overestimation or underestimation of an association, depending on the direction and magnitude of the relations that the confounding variable has with the independent and dependent variables. The reasons are twofold. First, logistic regression is the most commonly used and well-understood modeling technique in the context of fault prediction. Second, a recent systematic review suggests that, compared to other complex modeling techniques, logistic regression performs well when predicting fault-proneness.
URI: http://hdl.handle.net/10397/20607
ISSN: 1049-331X
DOI: 10.1145/2556777
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

15
Citations as of Feb 24, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Feb 24, 2017

Page view(s)

25
Last Week
0
Last month
Checked on Feb 26, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.