Research On Software Reliability Measurement Technology Based On Deep Learning

With the rapid development of information technology and information industry,computer software systems have been widely used in many fields.In recent years,with the continuous improvement of the scale and complexity of computer software systems,the reliability of software systems is increasingly difficult to secure and measure.With the development of software reliability measurement technology for many years,many excellent software reliability and growth models have emerged to measure software reliability.Based on the theoretical research and practice of software reliability growth model,this paper proposes a deep CG-EGU(Efficient Gated Unit with Control Gate)neural network based on the deep GRU(Gated Recurrent Unit)neural network aiming at the problem of software reliability growth model based on GRU neural network.The paper uses the deep CG-EGU neural network to establish a software reliability growth model on the software failure data set to measure software reliability.The research of this paper is divided into the following three parts:(1)Aiming at the problem that the GRU unit has low learning efficiency due to insufficient GRU unit update learning ability,a highly efficient EGU(Efficient Gated Unit)is proposed.In order to make the GRU unit update gates more efficient,the EGU unit introduces a forgotten gate when the GRU unit updates the gate update,which increases the efficiency of updating the gate update,improves the training efficiency of the GRU unit,and reduces the training overhead.(2)For the problem that the information processing efficiency between multiple hidden layers in the deep GRU network is not high,this paper proposes a deep CG-EGU neural network based on deep EGU network.On the basis of the deep EGU network,the deep CG-EGU neural network enhances the information processing capability between the hidden layers by adding control gates between different hidden layers to improve the efficiency of feature extraction and further improve the performance of the model.(3)The performance of the EGU unit and the deep CG-EGU neural network proposed in this paper were compared and verified by experiments.Software reliability growth models based on deep BP neural network,deep GRU neural network,deep EGU neural network anddeep CG-EGU neural network are established in the recognized software defect dataset.Compare and analyze the prediction results of the software reliability growth model from the same and different perspectives of the hidden layer.Verify whether the performance of the EGU unit is higher than that of the GRU unit when the number of hidden layers is the same.Verify whether the performance of the deep CG-EGU network is improved compared to the deep EGU network when the number of hidden layers is different.