A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

• Published in: Journal of computer science and technology Vol. 30; no. 5; pp. 969 - 980
• Main Authors: Ryu, Duksan, Jang, Jong-In, Baik, Jongmoon
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2015; Springer Nature B.V; School of Computing, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, Korea
• Subjects: Algorithms; Artificial Intelligence; Classification; Classifiers; Computer programs; Computer Science; Construction; Data Structures and Information Theory; Datasets; Defects; False alarms; Information Systems Applications (incl.Internet); Modules; Performance evaluation; Pilot projects; Pilots; Quality control; Regular Paper; Science; Software; Software Engineering; Software quality; Statistical analysis; Theory of Computation; 交叉项; 实例; 局部放电; 最近邻; 测试数据; 混合分类; 缺陷预测; 软件工程; data cleaning; nearest-neighbor algorithm; software defect analysis; instance-based learning
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-015-1575-5

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 overall performance as well as high PD and low PF. use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.