Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 large separation between each step, the output of the previous step may not be optimal for the current step during implementation. And the collection of workload data is usually a dynamic process, MCA only considers the estimation of heterogeneous workload in static scenarios

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com