WEI Hua, SHAN Chun, HU Changzhen, ZHANG Yu, YU Xiao. Software Defect Prediction via Deep Belief Network[J]. Chinese Journal of Electronics, 2019, 28(5): 925-932. doi: 10.1049/cje.2019.06.012
Citation: WEI Hua, SHAN Chun, HU Changzhen, ZHANG Yu, YU Xiao. Software Defect Prediction via Deep Belief Network[J]. Chinese Journal of Electronics, 2019, 28(5): 925-932. doi: 10.1049/cje.2019.06.012

Software Defect Prediction via Deep Belief Network

doi: 10.1049/cje.2019.06.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.U1636115, No.61876019), the Fundamental Research Funds for Beijing Universities of Civil Engineering and Architecture (Response by ZhangYu), and Excellent Teachers Development Foundation of BUCEA (Response by ZhangYu), and National Key R&D Program of China (No.2016YFC060090).
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  • Corresponding author: YU Xiao (corresponding author) received the Ph.D.degree in computer science and technology at Beijing Institute of Technology.He is currently a lecture and master supervisor at Shandong University of Technology.His research interests include information security,network storage and embedded system.(Email:yuxiao8907118@163.com)
  • Received Date: 2018-07-30
  • Rev Recd Date: 2018-12-29
  • Publish Date: 2019-09-10
  • Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs. The problem of data redundancy exists in software defect data set, which has great influence on prediction effect. We propose a defect distribution prediction model (Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network (DBN) technology, which is an effective deep learning technique in image processing and natural language processing, whose features are similar to defects in source program. Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM), Locally linear embedding SVM (LLE-SVM), and Neighborhood preserving embedding SVM (NPE-SVM).
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