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Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering

Author

Listed:
  • Kyawt Kyawt San

    (Department of Computer Science and Engineering, Waseda University, Shinjuku-ku, Tokyo 169-8555, Japan)

  • Hironori Washizaki

    (Department of Computer Science and Engineering, Waseda University, Shinjuku-ku, Tokyo 169-8555, Japan)

  • Yoshiaki Fukazawa

    (Department of Computer Science and Engineering, Waseda University, Shinjuku-ku, Tokyo 169-8555, Japan)

  • Kiyoshi Honda

    (Department of Information Systems, Osaka Institute of Technology, Hirakata City, Osaka 573-0196, Japan)

  • Masahiro Taga

    (e-Seikatsu Co., Ltd., Minato-ku, Tokyo 106-0047, Japan)

  • Akira Matsuzaki

    (e-Seikatsu Co., Ltd., Minato-ku, Tokyo 106-0047, Japan)

Abstract

Software reliability is an essential characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein, we propose a new software reliability modeling method called a deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for the training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

Suggested Citation

  • Kyawt Kyawt San & Hironori Washizaki & Yoshiaki Fukazawa & Kiyoshi Honda & Masahiro Taga & Akira Matsuzaki, 2021. "Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering," Mathematics, MDPI, vol. 9(22), pages 1-22, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2945-:d:682035
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    References listed on IDEAS

    as
    1. Wang, Jinyong & Zhang, Ce, 2018. "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 73-82.
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