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).
More Information
  • 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).
  • loading
  • S. Bellon, R. Koschke, G. Antoniol, et al., "Comparison and evaluation of clone detection tools", IEEE Transactions on Software Engineering, Vol.33, No.9, pp.577-591, 2007.
    D. Brumley, J. Newsome, D. Song, et al., "Towards automatic generation of vulnerability-based signatures", Proc. of IEEE Symposium on Security and Privacy, Washington, DC, USA, pp.2-16, 2006.
    I. Chowdhury and M. Zulkernine, "Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities", Journal of Systems Architecture, Vol.57, No.3, pp.294-313, 2011.
    K.R. Chanchal, R.C. James and K. Rainer, "Comparison and evaluation of code clone detection techniques and tools:A qualitative approach", Science of Computer Programming, Vol.74, No.7, pp.470-495, 2009.
    C. Marco, F. Viktoria, B. Greg, et al., "Static detection of vulnerabilities in x86 executables", Proc. of the 22nd Annual Computer Security Applications Conference, Washington, DC, USA, pp.269-278, 2011.
    B.D. Gavitt, P. Hulin, E. Kirda, et al., "LAVA:Large-scale automated vulnerability addition", Proc. of IEEE Symposium on Security and Privacy (SP), Fairmont, San Jose, CA, USA, pp.110-121, 2016.
    S. Wang, T. Liu and L. Tan, "Automatically learning semantic features for defect prediction", Proc. of IEEE/ACM 38th International Conference on Software Engineering (ICSE), Austin, Texas, USA, pp.297-308, 2016.
    F.J. Rmy, M. Floral, B. Xavier, et al., "Fine-grained and accurate source code differencing", Proc. of ACM/IEEE International Conference on Automated Software Engineering, Vasteras, Sweden, pp.313-324, 2014.
    L. Pelayo and S. Dick, "Evaluating stratification alternatives to improve software defect prediction", IEEE Transactions on Reliability, Vol.61, No.61, pp.516-525, 2012.
    L. Miao, M. Liu and D. Zhang, "Cost-sensitive feature selection with application in software defect prediction", Proc. of International Conference on Pattern Recognition, Tsukuba Science City, JAPAN, pp.967-970, 2012.
    S. Wang and X. Yao, "Using class imbalance learning for software defect prediction", IEEE Transactions on Reliability, Vol.62, No.2, pp.434-443, 2013.
    Z.G. Huang, J.Z. Lai, W.B. Chen, et al., "Data security against receiver corruptions:SOA security for receivers from simulatable DEMs", Information Sciences, DOI: 10.1016/j.ins.2018.08.059.
    Q.K. Zhang, Y.J. Li, Q. Zhang, et al., "A self-certified crosscluster asymmetric group key agreement for wireless sensor networks", Chinese Journal of Electronics, Vol.28, No.2, pp.280-287, 2019.
    X.S. Zhang, C. Liang, Q.X. Zhang, et al., "Building covert timing channels by packet rearrangement over mobile networks", Information Sciences, Vol.445-446, pp.66-78, 2018.
    Z.T. Guan, G.L Si, X.S. Zhang, et al., "Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities", IEEE Communications Magazine, Vol.56, No.7, pp.82-88, 2018.
    Z.T. Guan, Y. Zhang, L.H. Zhu, et al., "EFFECT:An efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid", SCIENCE CHINA Information Sciences, Vol.62, Issue.3, pp.1-14, 2019.
    C. Liang, X.M. Wang, X.S. Zhang, et al., "A payloaddependent packet rearranging covert channel for mobile VoIP traffic", Information Sciences, Vol.465, pp.162-173, 2018.
    Y. Xue, Y.A. Tan, C. Liang, et al., "RootAgency:A digital signature-based root privilege management agency for cloud terminal devices", Information Sciences, Vol.444, pp.36-50, 2018.
    Y.A. Tan, Y. Xue, C. Liang, et al., "A root privilege management scheme with revocable authorization for Android devices", Journal of Network and Computer Applications, Vol.107, No.4, pp.69-82, 2018.
    X. Yu, Y.A. Tan, C.Y. Zhang, et al., "A high-performance hierarchical snapshot scheme for hybrid storage systems", Chinese Journal of Electronics, Vol.27, No.1, pp.76-85, 2018.
    A.E. Hassan, "Predicting faults using the complexity of code changes", Proc. of IEEE 31st International Conference on Software Engineering, Vancouver, BC, Canada, pp.78-88, 2009.
    Z.Z. Sun, Q.X. Zhang, Y.Z. Li, et al., "DPPDL:A dynamic partial-parallel data layout for green video surveillance storage", IEEE Transactions on Circuits and Systems for Video Technology, Vol.28, No.1, pp.193-205, 2018.
    Y. Jiang, B. Cuki, T. Menzies, et al., "Comparing design and code metrics for software quality prediction", Proc. of the 4th international workshop on Predictor models in software engineering, Leipzig, Germany, pp.11-18, 2008.
    R. Moser, W. Pedrycz and G. Succi, "A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction", Proc. of ACM/IEEE International Conference on Software Engineering, Edmonton, Alberta, Canada, pp.181-190, 2009.
    J. Sliwerski, T. Zimmermann and A. Zeller, "When do changes induce fixes", ACM SIGSOFT Software Engineering Notes, Vol.30, No.4, pp.1-5, 2005.
    Y.Z. Li, S.J. Yao, K. Yang, et al., "A high-imperceptibility and histogram-shifting data hiding scheme for JPEG images", IEEE Access, Vol.7, No.1, pp.73573-73582, 2019.
    X.S. Zhang, Y.A. Tan, C.Y. Zhang, et al., "A code protection scheme by process memory relocation for android devices", Multimedia Tools and Applications, Vol.77, No.9, pp.11137-11157, 2018.
    J. Eyolfson, L. Tan and P. Lam, "Do time of day and developer experience affect commit bugginess", International Working Conference on Mining Software Repository, Vol.282, No.42, pp.153-162, 2011.
    Z. Yin, D. Yuan, Y. Zhou, et al., "How do fixes become bugs?", Proc. of ACM Sigsoft Symposium on the Foundation of Software Engineering, Szeged, Hungary, pp.26-36, 2011.
    J.M. Zheng, Y.A. Tan, X.S. Zhang, et al., "Multi-domain lightweight asymmetric group key agreement", Chinese Journal of Electronics, Vol.27, No.5, pp.1085-1091, 2018.
    X.S. Zhang, Y.A. Tan, C. Liang, et al., "A covert channel over voLTE via adjusting silence periods", IEEE Access, Vol.6, No.2, pp.9292-9302, 2018.
    Z. Zhang and H. Zha, "Principal manifolds and nonlinear dimensionality reduction via tangent space alignment", Society for Industrial and Applied Mathematics, Vol.8, No.4, pp.406-424, 2005.
    S.S. Keerthi and C.J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel", Neural Computation, Vol.15, No.7, pp.1667-1674, 2003.
    Q.X. Zhang, H.X. Gong, X.S. Zhang, et al., "A sensitive network jitter measurement for covert timing channels over interactive traffic", Multimedia Tools and Applications, Vol.78, No.3, pp.3493-3509, 2019.
    X.F. Gao, Y.A. Tan, H.W. Jiang, et al., "Boosting targeted black-box attacks via ensemble substitute training and linear augmentation", Applied Sciences, Vol.9, No.11, pp.2286-2293, 2019.
    Y. Xue, X.S. Zhang, X. Yu, et al., "Isolating host environment by booting android from OTG devices", Chinese Journal of Electronics, Vol.27, No.3, pp.617-624, 2018.
    C. Liang, Y.A. Tan, X.S. Zhang, et al., "Building packet length covert channel over mobile VoIP traffics", Journal of Network and Computer Applications, Vol.118, No.1, pp.144-153, 2018.
    J.Y. Jang, A. Abeer and B. David, "ReDeBug:Finding unpatched code clones in entire OS distributions", Proc. of IEEE Symposium on Security and Privacy, Westin St.Francis, San Francisco, CA, USA, pp.48-62, 2012.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (136) PDF downloads(207) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return