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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hu Yong Chen Juhua Rong Zhenbang Mei Liu Xie Kang |
| Copyright Year | 2006 |
| Description | Author affiliation: Sun Yat-sen Univ., Guangzhou (Hu Yong; Chen Juhua; Rong Zhenbang) |
| Abstract | Software project development has always been associated with high failure rate. In this paper, we identify the key software risk factors responsible in achieving successful outcome and use a neural network approach to establish a model for minimizing the risks attributed to failed projects. Input of the model is software risk factors that were obtained through interview, and output of the model describes the final outcome of the project. The data for analysis is from real software projects collected through questionnaires. In order to enhance model performance, principal component analysis and genetic algorithm are employed. The experimental result indicates that the software risk analysis can be improved through these methods and that the risk analysis model is effective |
| Sponsorship | IEEE CPS |
| Starting Page | 722 |
| Ending Page | 725 |
| File Size | 205919 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769527027 |
| DOI | 10.1109/ICDMW.2006.14 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data analysis Neural networks Project management Artificial neural networks Large-scale systems Risk analysis Risk management Sun Principal component analysis Software development management |
| Content Type | Text |
| Resource Type | Article |
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