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A novel framework for risk management of software projects by integrating a new COPRAS method under cloud model and machine learning algorithms

Author

Listed:
  • Mohammad Hossein Haghighi

    (Amirkabir University of Technology)

  • Maryam Ashrafi

    (Amirkabir University of Technology)

Abstract

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks’ values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others.

Suggested Citation

  • Mohammad Hossein Haghighi & Maryam Ashrafi, 2024. "A novel framework for risk management of software projects by integrating a new COPRAS method under cloud model and machine learning algorithms," Annals of Operations Research, Springer, vol. 338(1), pages 675-708, July.
    • Handle: RePEc:spr:annopr:v:338:y:2024:i:1:d:10.1007_s10479-023-05653-3
    DOI: 10.1007/s10479-023-05653-3
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    References listed on IDEAS

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    1. Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
    2. Ibrahim Yilmaz, 2023. "A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    3. Ke-Qin Wang & Hu-Chen Liu & Liping Liu & Jia Huang, 2017. "Green Supplier Evaluation and Selection Using Cloud Model Theory and the QUALIFLEX Method," Sustainability, MDPI, vol. 9(5), pages 1-17, April.
    4. Zhou, Shan & Yang, Pu, 2020. "Risk management in distributed wind energy implementing Analytic Hierarchy Process," Renewable Energy, Elsevier, vol. 150(C), pages 616-623.
    5. 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.
    6. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
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