Software reliability prediction model based on depth CG-LSTM neural network

Abstract

The invention discloses a software reliability prediction model based on a depth CG-LSTM neural network, and belongs to the technical field of computer software. The model comprises a model training part and a model prediction part, wherein the model training part is used for performing data normalization processing on a software failure data set; and the software reliability prediction model based on the depth CG-LSTM neural network is trained by using software failure data set has subjected to normalization processing to acquire a prediction model. The model prediction part is used for acquiring the current software failure data and performing data normalization processing, and then the acquired prediction model is input to predict future software failure to acquire a prediction result.According to the model provided by the invention, the problems of gradient vanishing and poor generalization ability of the software reliability prediction model based on the traditional neural network are overcome, the model is higher in prediction accuracy and wider in applicability.

Description

technical field [0001] The invention relates to a software reliability prediction model based on a deep CG-LSTM neural network, belonging to the technical field of computer software. Background technique [0002] With the rapid development of Internet technology, the scale of computer application software is getting larger and more complex, which makes it more and more difficult to guarantee the reliability of software systems. [0003] Software reliability refers to the probability that software will not fail under specified conditions and within a specified time. Software reliability prediction usually refers to using the failure data collected during software testing or operation as a data source to predict the future failure of the software, accurately predict the operating status of the software, and help to detect and deal with possible software failures early. problems and prevent software failures from occurring. [0004] The stochastic process reliability model an...

AI Technical Summary

Problems solved by technology

The stochastic process model needs to make many prior assumptions about the attributes of software failures and the software failure process, which leads to great differences in the prediction accuracy of each model in different projects, that is, the applicability of the model is poor

On the one hand, the traditional neural network reliability model is prone to problems such as gradient disappearance, gradient explosion, and overfitting due to its own structure, which makes the prediction of the model often have weak generalization ability; on the other hand, due to its It cannot model the changes in the time series, which leads to the problem of poor forecasting accuracy

Images