南昌工程学院学报2006，Vol.25Issue(2)：102-106,5.
A Software Risk Analysis Model Using Bayesian Belief Network
A Software Risk Analysis Model Using Bayesian Belief Network
摘要
Abstract
The uncertainty during the period of software project development often brings huge risks to contractors and clients. Ifwe can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project, we can reduce the risk.Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table. In this paper, we built up network structure by Delphi method for conditional probability table learning, and learn update probability table and nodes' confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately. This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.关键词
software risk analysis/Bayesian Belief Network/EM algorithm/parameter learningKey words
software risk analysis/Bayesian Belief Network/EM algorithm/parameter learning分类
信息技术与安全科学引用本文复制引用
Yong Hu,Juhua Chen,Mei Liu ,Yang Yun,Junbiao Tang..A Software Risk Analysis Model Using Bayesian Belief Network[J].南昌工程学院学报,2006,25(2):102-106,5.基金项目
Guangdong Nature Science Foundation (04009863) （04009863）
