Software reliability prediction model based on relevance vector machine

Relevance vector machines have been successfully used in many domains, while their application in software reliability prediction is still quite rare. We proposed an RVM-based model for software reliability prediction, the RVM learning scheme is applied to the failure time data, forcing the network to learn and recognize the inherent internal temporal property of software failure sequence in order to capture the most current feature hidden inside the software failure behavior. We also compare the prediction accuracy of software reliability prediction models based on RVM, SVM and ANN. Experimental results show that our proposed RVM-based software reliability prediction model could achieve a higher prediction accuracy compared with ANN-based and SVM-based models.

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