IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2325-d854705.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2325/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2325/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Seyed Morteza Hatefi & Mohammad Ehsan Basiri & Jolanta Tamošaitienė, 2019. "An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
    4. Luyuan Chen & Zhen Li & Xinyang Deng, 2020. "Emergency alternative evaluation under group decision makers: a new method based on entropy weight and DEMATEL," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(3), pages 570-583, February.
    5. Xin Ruan & Zhiyi Yin & Dan M. Frangopol, 2015. "Risk Matrix Integrating Risk Attitudes Based on Utility Theory," Risk Analysis, John Wiley & Sons, vol. 35(8), pages 1437-1447, August.
    6. Fergus Bolger, 2018. "The Selection of Experts for (Probabilistic) Expert Knowledge Elicitation," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 393-443, Springer.
    7. Mehran Khalaj & Reza Tavakkoli-Moghaddam & Fereshteh Khalaj & Ali Siadat, 2020. "New definition of the cross entropy based on the Dempster-Shafer theory and its application in a decision-making process," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(4), pages 909-923, February.
    8. Xiaoqing Li & Qingquan Jiang & Maxwell K. Hsu & Qinglan Chen, 2019. "Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    9. Markos V. Koutras & Ioannis S. Triantafyllou & Serkan Eryilmaz, 2016. "Stochastic Comparisons Between Lifetimes of Reliability Systems with Exchangeable Components," Methodology and Computing in Applied Probability, Springer, vol. 18(4), pages 1081-1095, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Zihan & Deng, Yong, 2022. "Derive power law distribution with maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    2. Zeng, Ziyue & Xiao, Fuyuan, 2023. "A new complex belief entropy of χ2 divergence with its application in cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Yu, Zihan & Deng, Yong, 2022. "Derive power law distribution with maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    3. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Roger C. Jensen & Haley Hansen, 2020. "Selecting Appropriate Words for Naming the Rows and Columns of Risk Assessment Matrices," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    5. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & Small, Michael & Li, Man, 2023. "Improving resilience of high-speed train by optimizing repair strategies," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Tang, Daogui & Fang, Yi-Ping & Zio, Enrico, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Abdar, Moloud & Basiri, Mohammad Ehsan & Yin, Junjun & Habibnezhad, Mahmoud & Chi, Guangqing & Nemati, Shahla & Asadi, Somayeh, 2020. "Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    8. Sun, Qin & Li, Hongxu & Zhong, Yuanfu & Ren, Kezhou & Zhang, Yingchao, 2024. "Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Belton, Ian & Wright, George & Sissons, Aileen & Bolger, Fergus & Crawford, Megan M. & Hamlin, Iain & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina, 2021. "Delphi with feedback of rationales: How large can a Delphi group be such that participants are not overloaded, de-motivated, or disengaged?," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    10. Peide Liu & Xiaoxiao Liu & Guiying Ma & Zhaolong Liang & Changhai Wang & Fawaz E. Alsaadi, 2020. "A Multi-Attribute Group Decision-Making Method Based on Linguistic Intuitionistic Fuzzy Numbers and Dempster–Shafer Evidence Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 499-524, April.
    11. Sonal, & Ghosh, Debomita, 2022. "Impact of situational awareness attributes for resilience assessment of active distribution networks using hybrid dynamic Bayesian multi criteria decision-making approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Saad Muslet Albogami & Mohd Khairol Anuar Bin Mohd Ariffin & Eris Elianddy Bin Supeni & Kamarul Arifin Ahmad, 2021. "A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    13. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    14. Yanhua Chang & Yi Liang, 2023. "Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    15. Jolanta Tamošaitienė & Vahidreza Yousefi & Hamed Tabasi, 2021. "Project Portfolio Construction Using Extreme Value Theory," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    16. Yutong Chen & Yongchuan Tang, 2021. "An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
    17. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    18. Anna Kosovac & Brian Davidson & Hector Malano, 2019. "Are We Objective? A Study into the Effectiveness of Risk Measurement in the Water Industry," Sustainability, MDPI, vol. 11(5), pages 1-13, February.
    19. Bao, Han & Ding, Ruoyu & Chen, Bei & Xu, Quan & Bao, Bocheng, 2023. "Two-dimensional non-autonomous neuron model with parameter-controlled multi-scroll chaotic attractors," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    20. Vicki Bier, 2020. "The Role of Decision Analysis in Risk Analysis: A Retrospective," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2207-2217, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2325-:d:854705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.