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Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Author

Listed:
  • Xiaoqing Li

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

  • Qingquan Jiang

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

  • Maxwell K. Hsu

    (Marketing, University of Wisconsin-Whitewater, Whitewater, WI 53190, USA)

  • Qinglan Chen

    (School of Economics & Management, Xiamen University of Technology, Xiamen 361024, China)

Abstract

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4513-:d:259403
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    References listed on IDEAS

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    1. Bimal Nepal & Om Prakash Yadav, 2015. "Bayesian belief network-based framework for sourcing risk analysis during supplier selection," International Journal of Production Research, Taylor & Francis Journals, vol. 53(20), pages 6114-6135, October.
    2. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
    3. Feng Zhang & Yuanyuan Wang & Minjie Cao & Xiaoxiao Sun & Zhenhong Du & Renyi Liu & Xinyue Ye, 2016. "Deep-Learning-Based Approach for Prediction of Algal Blooms," Sustainability, MDPI, vol. 8(10), pages 1-12, October.
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    Cited by:

    1. Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    2. 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.

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