Active Learning for Software Defect Prediction

Guangchun LUO  Ying MA  Ke QIN 

Publication
IEICE TRANSACTIONS on Information and Systems  Vol.E95-D  No.6  pp.1680-1683
Publication Date: 2012/06/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Software Engineering
Keyword: 
machine learning,  defect prediction,  active learning,  support vector machine, 

Full Text: PDF(146.6KB)

Summary: 
An active learning method, called Two-stage Active learning algorithm (TAL), is developed for software defect prediction. Combining the clustering and support vector machine techniques, this method improves the performance of the predictor with less labeling effort. Experiments validate its effectiveness.