Active learning for software defect prediction

Guangchun Luo; Ying Ma; Ke Qin

doi:10.1587/transinf.E95.D.1680

Active learning for software defect prediction

Guangchun Luo^*
, Ying Ma
, Ke Qin

^*Corresponding author for this work

University of Electronic Science and Technology of China

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	1680-1683
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E95-D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.E95.D.1680
State	Published - Jun 2012
Externally published	Yes

Keywords

Active learning
Defect prediction
Machine learning
Support vector machine

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10.1587/transinf.E95.D.1680

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@article{b035fcf8cdf845b89b178bbc090d3b00,

title = "Active learning for software defect prediction",

abstract = "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.",

keywords = "Active learning, Defect prediction, Machine learning, Support vector machine",

author = "Guangchun Luo and Ying Ma and Ke Qin",

year = "2012",

month = jun,

doi = "10.1587/transinf.E95.D.1680",

language = "英语",

volume = "E95-D",

pages = "1680--1683",

journal = "IEICE Transactions on Information and Systems",

issn = "0916-8532",

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number = "6",

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TY - JOUR

T1 - Active learning for software defect prediction

AU - Luo, Guangchun

AU - Ma, Ying

AU - Qin, Ke

PY - 2012/6

Y1 - 2012/6

N2 - 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.

AB - 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.

KW - Active learning

KW - Defect prediction

KW - Machine learning

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/84861919426

U2 - 10.1587/transinf.E95.D.1680

DO - 10.1587/transinf.E95.D.1680

M3 - 文章

AN - SCOPUS:84861919426

SN - 0916-8532

VL - E95-D

SP - 1680

EP - 1683

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 6

ER -

Active learning for software defect prediction

Abstract

Keywords

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