›› 2015, Vol. 30 ›› Issue (5): 969-980.doi: 10.1007/s11390-015-1575-5

Special Issue: Data Management and Data Mining

• Special Section on Software Systems • Previous Articles     Next Articles

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

Duksan Ryu, Jong-In Jang, Jongmoon Baik, Member, ACM, IEEE   

  1. School of Computing, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, Korea
  • Received:2015-03-20 Revised:2015-07-07 Online:2015-09-05 Published:2015-09-05
  • About author:Duksan Ryu earned his Bachelor's degree in computer science from Hanyang University, Seoul, in 1999, and Master's dual degree in software engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, and Carnegie Mellon University, Pittsburgh, in 2012. He is a Ph.D. student in the School of Computing at KAIST. His research areas are software defect prediction and software reliability engineering.
  • Supported by:

    This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT and Future Planning (MSIP)) under Grant No. NRF-2013R1A1A2006985 and Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) under Grant No. R0101-15-0144, Development of Autonomous Intelligent Collaboration Framework for Knowledge Bases and Smart Devices.

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 naïve 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.

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