Volume 7 Number 2 (Feb. 2012)
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JSW 2012 Vol.7(2): 440-449 ISSN: 1796-217X
doi: 10.4304/jsw.7.2.440-449

Intelligent Analysis Model for Outsourced Software Project Risk Using Constraint-based Bayesian Network

Yong Hu, Xizhu Mo*, Xiangzhou Zhang*, Yuran Zeng, Jianfeng Du, and Kang Xie
Institute of Business Intelligence & Knowledge Discovery, Business School, Guangdong University of Foreign Studies, Sun Yat-Sen University, Guangzhou, 510006, PR China

Abstract—Software outsourcing is one of the leading methods in software development. However, it is also accompanied with higher risk than in-house software development. A risk intelligent analysis model based on Bayesian Network can effectively contribute to software project risk assessment. From the perspectives of both the customer and contractor, we propose a risk identification framework for outsourced software projects, and have collected real-life outsourced software project samples. Based on totally 154 valid samples, we established an intelligent analysis model for outsourced software project risk by incorporating expert knowledge as structural constraints into a Bayesian Network. Experimental results showed that the model has higher predictive accuracy than Decision Tree and Neural Network, and the derived management rules are consistent with the existing software engineering theory. The model would provide a great guideline for outsourced software project risk management in both theory and practice.

Index Terms—outsourced software, software project risk management, Bayesian network, structural constraint, risk prediction

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Cite: Yong Hu, Xizhu Mo*, Xiangzhou Zhang*, Yuran Zeng, Jianfeng Du, and Kang Xie, "Intelligent Analysis Model for Outsourced Software Project Risk Using Constraint-based Bayesian Network," Journal of Software vol. 7, no. 2, pp. 440-449, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly 
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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