Using Coding-Based Ensemble Learning to Improve Software Defect Prediction

Using classification methods to predict software defect proneness with static code attributes has attracted a great deal of attention. The class-imbalance characteristic of software defect data makes the prediction much difficult; thus, a number of methods have been employed to address this problem....

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2012-11, Vol.42 (6), p.1806-1817
Main Authors: Sun, Zhongbin, Song, Qinbao, Zhu, Xiaoyan
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Boosting
Class-imbalance data
Classification
Coding
Computer programs
Computer science
control theory
systems
Computer systems performance. Reliability
Data processing. List processing. Character string processing
Defects
Encoding
Exact sciences and technology
Learning
Memory organisation. Data processing
meta learning
multiclassifier
Prediction algorithms
Predictive models
Sampling
Software
Software algorithms
software defect prediction
Software defects
Studies
Theoretical computing
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Summary:Using classification methods to predict software defect proneness with static code attributes has attracted a great deal of attention. The class-imbalance characteristic of software defect data makes the prediction much difficult; thus, a number of methods have been employed to address this problem. However, these conventional methods, such as sampling, cost-sensitive learning, Bagging, and Boosting, could suffer from the loss of important information, unexpected mistakes, and overfitting because they alter the original data distribution. This paper presents a novel method that first converts the imbalanced binary-class data into balanced multiclass data and then builds a defect predictor on the multiclass data with a specific coding scheme. A thorough experiment with four different types of classification algorithms, three data coding schemes, and six conventional imbalance data-handling methods was conducted over the 14 NASA datasets. The experimental results show that the proposed method with a one-against-one coding scheme is averagely superior to the conventional methods.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2012.2226152