Using Class Imbalance Learning for Software Defect Prediction

To facilitate software testing, and save testing costs, a wide range of machine learning methods have been studied to predict defects in software modules. Unfortunately, the imbalanced nature of this type of data increases the learning difficulty of such a task. Class imbalance learning specializes...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on reliability Vol. 62; no. 2; pp. 434 - 443
Main Authors: Shuo Wang, Xin Yao
Format: Journal Article
Language:English
Published: IEEE 01.06.2013
Subjects:
Class imbalance learning
ensemble learning
Learning systems
Measurement
negative correlation learning
Niobium
Prediction algorithms
Software
Software algorithms
software defect prediction
Training
ISSN:0018-9529, 1558-1721
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To facilitate software testing, and save testing costs, a wide range of machine learning methods have been studied to predict defects in software modules. Unfortunately, the imbalanced nature of this type of data increases the learning difficulty of such a task. Class imbalance learning specializes in tackling classification problems with imbalanced distributions, which could be helpful for defect prediction, but has not been investigated in depth so far. In this paper, we study the issue of if and how class imbalance learning methods can benefit software defect prediction with the aim of finding better solutions. We investigate different types of class imbalance learning methods, including resampling techniques, threshold moving, and ensemble algorithms. Among those methods we studied, AdaBoost.NC shows the best overall performance in terms of the measures including balance, G-mean, and Area Under the Curve (AUC). To further improve the performance of the algorithm, and facilitate its use in software defect prediction, we propose a dynamic version of AdaBoost.NC, which adjusts its parameter automatically during training. Without the need to pre-define any parameters, it is shown to be more effective and efficient than the original AdaBoost.NC.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2013.2259203