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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuan Ju-mei |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Automation, Taiyuan Institute of Technology, Shanxi Province, China (Yuan Ju-mei) |
| Abstract | Different reliability model results in different reliability prediction for the same software, and failed process of the same software could not be described using a single model in software reliability project. Concerning these problems, an adaptive multi-model synthesis dynamic prediction method for software reliability based on particle swarm optimization (PSO) algorithm was proposed. This method can select the most adaptive history data and the best weight allocation for every sub-model to predict the next time software reliability automatically. It integrates the strong point of various models and makes in-homogeneity model compensating each other through dynamic changing their weights adaptively and discards outdated data automatically. This method can solve the practical problems in software engineering. A case study was done to demonstrate the feasibility of this method and compare with basic particle swarm optimization. |
| Starting Page | 2357 |
| Ending Page | 2362 |
| File Size | 1448186 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424426928 |
| DOI | 10.1109/ICMA.2009.5246216 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Software reliability Particle swarm optimization Predictive models Prediction methods Software algorithms Automation History Software measurement Mathematical model Time domain analysis dynamic prediction particle swarm optimization software reliability adaptive multi-model synthesis |
| Content Type | Text |
| Resource Type | Article |
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