Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (11): 1771-1780.doi: 10.23940/ijpe.20.11.p9.17711780

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Software Defect Prediction Incremental Model using Ensemble Learning

Shibo Wanga, Yong Lia,b,*, Wenbo Mia and Ying Liua   

  1. aCollege of Computer Science and Technology, Xinjiang Normal University, Urumqi, 830054, China;
    bKey Laboratory of Safety-Critical Software of Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics Nanjing, 211106, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: *E-mail address: liyong@live.com

Abstract: Software defect prediction is an important way to find software defects and improve software quality. In recent years, with the rapid development of the software industry and the continuous improvement of data mining technology, a large amount of labeled software data is continuously generated to form a software data stream. However, traditional software defect prediction based on batch learning cannot fully adapt to this form of data flow, so a method of software defects prediction incremental model using ensemble learning (SDPIE) is proposed to learn and process real-time software data streams. In this process, the under-sampling method and selective learning method are used to solve the problem of software data imbalance and the increase of the incremental learning classifier scale. Through a comparison with two incremental learning algorithms and two batch learning algorithms, it is shown that the SDPIE algorithm can effectively control the size of the algorithm and the prediction performance is better than the other four comparison algorithms.

Key words: software defect prediction, incremental learning, ensemble learning, under-sampling, selective learning method