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Outcome prediction of software projects for information technology vendors
- Source :
- 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Several studies indicate that roughly 70% of the projects based on the software development have resulted in failure, thereby researchers and practitioners have been tried to develop solutions that will improve project success rates. It is insisted that to raise success rates, support should be provided by the organization to which the projects belong. With the aid of predictions that incorporate project outcomes for various information technology (IT) vendors, this study aims at identifying projects that should be preferentially supported by an organization. The data of 332 projects of various Japanese IT vendors were collected using an Internet survey, and a success/failure prediction algorithm is created by employing the Bayes classifier technique on the collected data. A resultant algorithm with 77.3% prediction capability was obtained. It is expected that the success/failure prediction procedure, including the prediction algorithm, help significantly to specify projects that an organization needs to participate in as priority.
- Subjects :
- business.industry
Computer science
05 social sciences
Software development
Information technology
02 engineering and technology
Bayes classifier
Machine learning
computer.software_genre
Statistical classification
Capability Maturity Model
Software
020204 information systems
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
The Internet
Artificial intelligence
business
computer
050203 business & management
Risk management
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
- Accession number :
- edsair.doi...........79cb97f01b2e32de879d0507649aa8f7
- Full Text :
- https://doi.org/10.1109/ieem.2017.8290188