NDLI: Predicting software black-box defects using stacked generalization

Content Provider	IEEE Xplore Digital Library
Author	Ning Li Zhanhuai Li Yanming Nie Xiling Sun Xia Li
Copyright Year	2011
Description	Author affiliation: School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China (Ning Li; Zhanhuai Li; Yanming Nie; Xiling Sun; Xia Li)
Abstract	Defect number prediction is essential to make a key decision on when to stop testing. For more applicable and accurate prediction, we propose an ensemble prediction model based on stacked generalization (PMoSG), and use it to predict the number of defects detected by third-party black-box testing. Taking the characteristics of black-box defects and causal relationships among factors which influence defect detection into account, Bayesian net and other numeric prediction models are employed in our ensemble models. Experimental results show that our PMoSG model achieves a significant improvement in accuracy of defect numeric prediction than any individual model, and achieves best prediction accuracy when using LWL(Locally Weighted Learning) method as level-1 model.
Starting Page	294
Ending Page	299
File Size	241210
Page Count	6
File Format	PDF
ISBN	9781457715389
e-ISBN	9781457715396
DOI	10.1109/ICDIM.2011.6093330
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-26
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training third-party testing stacked generalization Bayesian methods black-box defects Predictive models Data models Software Numerical models numeric prediction Testing Bayesian net
Content Type	Text
Resource Type	Article

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