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A Bayesian Networks-Based Risk Identification Approach for Software Process Risk: The Context of Chinese Trustworthy Software

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  • Jianping Li

    (College of Economics and Management, China Jiliang University, Hangzhou 310018, P. R. China†Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Minglu Li

    (#x2020;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P. R. China§Bureau of Planning, National Natural Science Foundation of China, Beijing 100085, P. R. China)

  • Dengsheng Wu

    (#x2020;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Qianzhi Dai

    (#x2020;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, P. R. China¶School of Economics, Hefei University of Technology, Hefei 230009, P. R. China)

  • Hao Song

    (#x2225;School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250014, P. R. China)

Abstract

For all of the many advantages and enormous benefits that information technology has brought to us, it has subjected to increasing risks that need enough attention. Bayesian network (BN) is an important probabilistic inference approach to support reasoning under uncertainty. This paper describes how BN is applied to quantify the occurrence probability of software process risk factors and the influence strength among the process risk factors in the context of Chinese trustworthy software. The information of 52 factors was obtained through a questionnaire survey of 93 project managers in five high Capability Maturity Model Integration (CMMI) level software companies. The focus of this paper is to present the key risk checklist and good timing for process risk control to improve software process risk management. Special effort has been put on the description of the experimental study, which provides the top 20 key risk factors in software process and critical software sub-processes for process risk management. The findings can provide the key risk checklist to software risk manager for risk identification and decision-making in process risk management. This is a general approach and, as such, it can be applied to a certain software project or some software enterprises with updated data.

Suggested Citation

  • Jianping Li & Minglu Li & Dengsheng Wu & Qianzhi Dai & Hao Song, 2016. "A Bayesian Networks-Based Risk Identification Approach for Software Process Risk: The Context of Chinese Trustworthy Software," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1391-1412, November.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:06:n:s0219622016500401
    DOI: 10.1142/S0219622016500401
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    References listed on IDEAS

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    Cited by:

    1. Dengsheng Wu & Xiaoqian Zhu & Jie Wan & Chunbing Bao & Jianping Li, 2019. "A Multiobjective Optimization Approach for Selecting Risk Response Strategies of Software Project: From the Perspective of Risk Correlations," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 339-364, January.

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