Active Learning for Software Defect Prediction

被引：8

作者：

Luo, Guangchun ^{[1
]}

Ma, Ying ^{[1
]}

Qin, Ke ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2012年 / E95D卷 / 06期

关键词：

machine learning; defect prediction; active learning; support vector machine; MODULES;

D O I：

10.1587/transinf.E95.D.1680

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1680 / 1683

页数：4

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