软件故障预测的递归神经网络

M. Benaddy, B. El Habil, Othmane El Meslouhi, S. Krit
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引用次数: 4

摘要

当软件在操作配置文件中运行时,会发生软件故障。控制软件中的故障要求能够足够早地预测问题以采取预防措施。软件故障的预测是通过使用以前收集的历史故障来完成的。为了预测软件故障,研究人员提出了几种模型。本文提出了一种利用历史故障数据预测软件故障的递归神经网络(RNN)。使用从文献中收集的数据对所提出的RNN进行训练和测试;将得到的结果与其他模型进行了比较,结果表明我们提出的模型具有很好的预测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recurrent neural network for software failure prediction
Software failure occurs when the software runs in an operational profile. Controlling failures in software require that one can predict problems early enough to take preventive action. The prediction of software failures is done by using the historical failures collected previously when they occur. To predict software failures, several models are proposed by researchers. In this paper, we present a recurrent neural network (RNN) to predict software failure using historical failure data. The proposed RNN is trained and tested using collected data from the literature; the obtained results are compared with other models and show that our proposed model gives very attractive prediction rates.
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