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

Xiaoqing Li, Qingquan Jiang, Maxwell K. Hsu and Qinglan Chen
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Xiaoqing Li: 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

Sustainability, 2019, vol. 11, issue 17, 1-12

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.

Keywords: Backpropagation neural network; risk assessment; rough set theory; software projects risk (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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