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Software Quality Prediction Model with the Aid of Advanced Neural Network with HCS

机译:借助HCS的高级神经网络辅助软件质量预测模型

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Software quality is regarded as the highly important factors for assessing the global competitive position of any software product. To assure quality, and to assess the reliability of software products, many software quality prediction models have been proposed in the past decades. In this proposed method we have utilized a hybrid method for quality prediction. The prediction is done with the help of the Advanced Neural network which is incorporated with Hybrid Cuckoo search (HCS) optimization algorithm for better prediction accuracy. The application software is first subjected to test case generation and once the test cases are generated they are applied to advanced neural network for the prediction of quality. The neural network is improved by utilizing HCS which optimizes the weight factor for improving the prediction. The quality metrics like maintainability and reliability are estimated for predicting the software quality and the results are compared with other existing techniques to verify the effectiveness of our proposed method.
机译:软件质量被视为评估任何软件产品的全球竞争地位的重要因素。为了确保质量并评估软件产品的可靠性,在过去的几十年中已经提出了许多软件质量预测模型。在此提出的方法中,我们利用混合方法进行质量预测。借助Advanced Neural Network进行预测,该网络与Hybrid Cuckoo Search(HCS)优化算法相结合,可提高预测精度。首先对应用软件进行测试用例生成,一旦生成了测试用例,便将它们应用到高级神经网络以预测质量。利用HCS改进了神经网络,HCS优化了权重因子以改善预测。估计诸如维护性和可靠性之类的质量指标,以预测软件质量,并将结果与​​其他现有技术进行比较,以验证我们提出的方法的有效性。

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