A study on software reliability prediction based on support vector machines
A study on software reliability prediction based on support vector machines
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DOI：
10.1109/ieem.2007.4419377
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发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
Bo Yang;Xiang Li
中科院分区：
文献类型：
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作者：
Bo Yang;Xiang Li
支持向量机（SVMs）已成功应用于许多领域，但在软件可靠性预测方面的应用还相当少见。文献中已经提出了一些基于支持向量机的软件可靠性预测模型，但预测精度仍有待提高。在本文中，我们提出了一个基于支持向量机的软件可靠性预测模型，我们研究的问题，影响预测精度。这些问题包括：1.是否应使用所有历史故障数据; 2.就预测精度而言，哪种类型的故障数据更适合使用。比较了基于支持向量机和人工神经网络的软件可靠性预测模型的预测精度。实验结果表明，我们提出的基于SVM的软件可靠性预测模型可以实现更高的预测精度相比，基于人工神经网络和现有的基于SVM的模型。
Support vector machines (SVMs) have been successfully used in many domains, while their application in software reliability prediction is still quite rare. A few SVM- based software reliability prediction models have been proposed in the literature; however, the accuracy of prediction can still be improved. In this paper, we propose an SVM-based model for software reliability prediction and we study issues that affect the prediction accuracy. These issues include: 1. Whether all historical failure data should be used; 2. What type of failure data is more appropriate to use in terms of prediction accuracy. We also compare the prediction accuracy of software reliability prediction models based on SVM and artificial neural network (ANN). Experimental results show that our proposed SVM-based software reliability prediction model could achieve a higher prediction accuracy compared with ANN-based and existing SVM-based models.