吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 2034-2041.doi: 10.13229/j.cnki.jdxbgxb201606037

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New approach of cross-project defect prediction based on multi-source data

LI Yong1, 2, HUANG Zhi-qiu1, WANG Yong1, FANG Bing-wu1   

  1. 1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2.Key Laboratory of Network Information Security and Public Opinion Analysis, Xinjiang Normal University, Urumqi 830054, China
  • Received:2015-11-30 Online:2016-11-20 Published:2016-11-20

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Abstract: Software defect prediction is significant to the optimization of quality assurance activities. The Within Project Defect Prediction (WPDP) can produce high quality results, but requires historical data of the project, which is often not available in practical scenarios. The cross-project Defect Prediction (CPDP) can effectively overcome the drawback of WPDP. However, existing research suggested that CPDP is particularly challenging and often yields poor performance, and very few studies investigated the practical guidelines on how to select suitable training data for CPDP from multi-source project data. A novel multi-source data driven approach for CPDP is proposed. First, the hierarchical filter strategy based on the characteristics of both projects and modules is developed to select training data. Then, the Naive Bayes algorithm is used to realize the prediction model. Experimental results of 14 open-source projects show that the proposed approach significantly improves CPDP performance, and can compete with WPDP.

Key words: computer software, cross project defects prediction, multi-source projects data, hierarchical data selection, Naive Bayes algorithm

CLC Number: 

  • TP311.5
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