<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Computer network software prediction is a good way to improve software quality, and the predictions of the software prediction method are close to the characteristics of the data set. In order to solve the problem that the invisible size of special data set is too large for computer software prediction, the author proposed a computer network-based software prediction method with deep computer coding and power learning. deep exploration of data features. data features. This type of model first uses an unsupervised learning-based evaluation model to evaluate the data set of 6 open projects, which solves the problem of classification uncertainty in the data; Deep self-encoding network models were then investigated. The model reduces the size of the data set, which is used to connect our model at the end of the model, the model uses training sets of shortened length to train the workers, and finally it makes predictions using the benchmarks. Experiments show that this model's prediction is better than standard software defect prediction, better than existing model-based software prediction models for fabric processes with repeated data volumes, and can be used in different categories. algorithms.</p>
</abstract>

Computer network software prediction is a good way to improve software quality, and the predictions of the software prediction method are close to the characteristics of the data set. In order to solve the problem that the invisible size of special data set is too large for computer software prediction, the author proposed a computer network-based software prediction method with deep computer coding and power learning. deep exploration of data features. data features. This type of model first uses an unsupervised learning-based evaluation model to evaluate the data set of 6 open projects, which solves the problem of classification uncertainty in the data; Deep self-encoding network models were then investigated. The model reduces the size of the data set, which is used to connect our model at the end of the model, the model uses training sets of shortened length to train the workers, and finally it makes predictions using the benchmarks. Experiments show that this model's prediction is better than standard software defect prediction, better than existing model-based software prediction models for fabric processes with repeated data volumes, and can be used in different categories. algorithms.

Defect prediction of big data computer network based on deep learning model

School of Electronic Information of Chengdu Jincheng University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Defect prediction of big data computer network based on deep learning model"}

Computer network software prediction is a good way to improve software quality, and the predictions of the software prediction method are close to the...

Defect prediction of big data computer network based on deep learning model

Pubblicato online: 06 giu 2023

Pagine: 2141 - 2148

Ricevuto: 15 lug 2022

Accettato: 01 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00319

Parole chiave
Deep learning, big data, Network defects

© 2023 Lei Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Defect prediction of big data computer network based on deep learning model

Pubblicato online: 06 giu 2023

Pagine: 2141 - 2148

Ricevuto: 15 lug 2022

Accettato: 01 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00319

Parole chiaveDeep learning, big data, Network defects

© 2023 Lei Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Deep learning, big data, Network defects