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An Evidential Software Risk Evaluation Model

Author

Listed:
  • Xingyuan Chen

    (College of Information and Engineering, Kunming University, Kunming 650214, China
    Key Laboratory of Data Governance and Intelligent Decision, Universities of Yunnan, Kunming 650214, China)

  • Yong Deng

    (Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

Suggested Citation

  • Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2325-:d:854705
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    References listed on IDEAS

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    Cited by:

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    3. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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