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

A prediction model and neural network technology, applied in the field of software reliability prediction model, can solve the problems of poor model applicability, inability to model time series, poor prediction accuracy, etc., and achieve the effect of improving accuracy

Active Publication Date: 2018-01-05
HARBIN ENG UNIV
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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 reliab

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

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Embodiment Construction

[0059] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate explanation of the present invention, and are not intended to limit the present invention.

[0060] The invention proposes a software reliability prediction model based on CG-LSTM neural network. Such as figure 1 As shown, the model includes two parts: model training and model prediction.

[0061] Model training part:

[0062] Step A1: Perform data normalization processing on the software failure data set;

[0063] The software failure data set comes from a software data acquisition system, and the data set includes software failure time X i , the software failure time is normalized to M i .

[0064] Data normalization processing includes the following steps:

[0065] Step A11: Extract the maximum software failure time X in the software failure data set max ;

[0066] Step A12: Ex...

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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...

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Application Information

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IPC IPC(8): G06F11/36
Inventor 徐东王磊孟宇龙张子迎姬少培张玲玲王岩俊张朦朦李贤王杰
Owner HARBIN ENG UNIV
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