Heterogeneous software workload estimation method based on deep learning

A deep learning and workload technology, applied in neural learning methods, computing, biological neural network models, etc., can solve problems such as inaccurate and suboptimal prediction results

Active Publication Date: 2019-10-11
GUANGDONG UNIV OF PETROCHEMICAL TECH
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AI Technical Summary

Problems solved by technology

[0006] Although the above method solves the problem of heterogeneity between data, the method used has defects, which leads to inaccurate prediction results. The difference in order has different effects on the learned feature space. For example, the MCA method, due to the l

Method used

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  • Heterogeneous software workload estimation method based on deep learning
  • Heterogeneous software workload estimation method based on deep learning
  • Heterogeneous software workload estimation method based on deep learning

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Embodiment

[0068] This embodiment provides a heterogeneous software workload estimation method based on deep learning, including:

[0069] Step S1: Create a dataset, including the source dataset x 2 with the target dataset x 1 ;

[0070] Among them, the target data set x 1 A data set owned by the user;

[0071] Generally, it is the internal data of the enterprise. If the historical data of the newly established enterprise is zero, the target data set can also be formed by using the enterprise branch or external enterprise data similar to the enterprise's situation according to the actual situation x 1 ;

[0072] source data set x 2 with the target dataset x 1 There is heterogeneity among them, and the external enterprise data set is generally used, which is different from the target data set x 1 heterogeneous;

[0073] Step S2: Use the source dataset x 2 with the target dataset x 1 train the autoencoder;

[0074] The training process includes: the autoencoder passes the aggreg...

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Abstract

The invention relates to the technical field of computer software, in particular to a heterogeneous software workload estimation method based on deep learning, which comprises the following steps: S1,establishing data sets including a source data set and a target data set; wherein the target data set is a data set owned by the user; wherein the source data set is a data set having heterogeneity with the target data set; S2, training an auto-encoder by using the source data set and the target data set; and S3, extracting data features from the auto-encoder, inputting the data features into a convolutional neural network training predictor, and generating a predicted value of the software workload by using the predictor. The convolutional neural network can automatically extract the high-level meaning of the data, so that the software workload estimation work is more efficient, and resources are saved.

Description

technical field [0001] The present invention relates to the technical field of computer software, and more specifically, to a method for estimating workload of heterogeneous software based on deep learning. Background technique [0002] As the Internet enters thousands of households and computer technology changes with each passing day, software, as a main carrier of information technology, has become an indispensable part of human society. With the increasing demand for software, the codes of software development are becoming more and more complex, and the estimation of software workload is becoming more and more difficult. [0003] Software Effort Estimation (Software Effort Estimation, referred to as SEE) is an important activity in the development of enterprise software projects. Accurate software effort estimation can enable enterprises to make reasonable plans, reduce management costs, thereby enhancing operational efficiency and improving economic efficiency. benefit...

Claims

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Application Information

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IPC IPC(8): G06Q10/10G06Q10/06G06N3/04G06N3/08
CPCG06Q10/103G06Q10/0639G06N3/08G06N3/045
Inventor 荆晓远齐富民訾璐黄鹤姚永芳
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
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