期刊
COMPUTER STANDARDS & INTERFACES
卷 40, 期 -, 页码 15-22出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csi.2015.01.001
关键词
Risks; Software project risk management; Risky project; Neural network
资金
- National Science Council of Taiwan [NSC 100-2410-H-147-013-, NSC 102-3114-C-147-001-ES]
Early and accurate discrimination of risky software projects is critical to project success. Researchers have proposed many predictive approaches based on traditional modeling techniques, but the high misclassification rate of risky projects is common. To overcome this problem, this study proposes a typical three-layered neural network (NN) architecture with a back propagation algorithm that can learn the complex patterns of the OMRON dataset. This study uses four accuracy evaluation criteria and two performance charts to objectively quantify and visually illustrate the performance of the proposed approach. Experimental results indicate that the NN approach is useful for predicting whether a project is risky. Specifically, this approach improves accuracy and sensitivity by more than 12.5% and 33.3%, respectively, compared to a logistic regression model developed from the same database. These results imply that the proposed approach can be used for early planning of limited project/organization resources and appropriate action for risky projects that are likely to cause schedule slippage and cost overload. (C) 2015 Elsevier B.V. All rights reserved.
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