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Applications of Spiking Neural Network to Predict Software Reliability

机译:尖峰神经网络预测软件可靠性的应用

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In the period of software improvement, programming dependability expectation turned out to be exceptionally critical for creating nature of programming in the product business. Time to time, numerous product dependability models have been introduced for evaluating unwavering quality of programming in programming forecast models. However, building precise forecast model is hard because of intermittent changes in information in the space of programming designing. As needs be, we propose a novel procedure, i.e. spiking neural system to anticipate programming unwavering quality. The key goal of this paper is to exhibit another approach which upgrades the exactness of programming unwavering quality prescient models when utilized with the product disappointment dataset. The viability of quality of a product is exhibited on dataset taken from the literature, where execution is measured by utilizing normalized root mean square error (NRMSE) obtained in the test dataset.
机译:在软件改进期间,编程可靠性期望对创造产品业务的编程性质特别重要。 时间待时间,已经引入了许多产品可依赖性模型,用于评估编程预测模型中的编程的坚定性质量。 然而,由于编程设计空间中的信息中的间歇变化,构建精确的预测模型很难。 由于需要,我们提出了一种新颖的程序,即尖峰神经系统,以期望编程不懈的质量。 本文的关键目标是展示另一种方法,该方法升级了编程不推动质量有质量模型的准确性,当使用产品失望数据集时。 在从文献中的数据集上展示了产品质量的可行性,其中通过利用测试数据集中获得的标准化均方根误差(NRMSE)来测量执行。

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