A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction
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- 作者：Duksan Ryu ; Jong-In Jang ; Jongmoon Baik
- 关键词：software defect analysis ; instance ; based learning ; nearest ; neighbor algorithm ; data cleaning
- 刊名：Journal of Computer Science and Technology
- 出版年：2015
- 出版时间：September 2015
- 年：2015
- 卷：30
- 期：5
- 页码：969-980
- 全文大小：380 KB
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Jong-In Jang (1)
Jongmoon Baik (1)
1. School of Computing, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, 305-701, Korea - 刊物类别：Computer Science
- 刊物主题：Computer Science, general
Software Engineering
Theory of Computation
Data Structures, Cryptology and Information Theory
Artificial Intelligence and Robotics
Information Systems Applications and The Internet
Chinese Library of Science - 出版者：Springer Boston
- ISSN：1860-4749
文摘
Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires sufficient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via na?ve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF. Keywords software defect analysis instance-based learning nearest-neighbor algorithm data cleaning