Linear classifier-oriented software testing method based on metamorphic test

A software testing method and linear classifier technology, applied in software testing/debugging, nuclear methods, instruments, etc., can solve problems such as misjudgment of program bugs, immaturity, and prone to problems in the model, and achieve the effect of improving the detection rate

Pending Publication Date: 2022-01-07
NANJING UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the technology related to the quality assurance of machine learning software is not mature enough, because the software quality assurance of machine learning faces two major difficulties. logical analysis
2. Machine learning programs often do not have test predictions. Even if the model trained by the machine learning program can achieve a prediction accuracy rate of more than 90% on a certain data set, it does not mean that there are no bugs in the program.
The complete machine learning software includes three parts: machine learning training program, machine learning model and data set. Although it is very important for the quality assurance of the machine learning model, the model is trained by the training program. If there is a problem with the training program, the

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
  • Linear classifier-oriented software testing method based on metamorphic test
  • Linear classifier-oriented software testing method based on metamorphic test
  • Linear classifier-oriented software testing method based on metamorphic test

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The present invention will now be described in further detail with reference to the accompanying drawings.

[0041] like figure 1 As shown, the linear classifier-oriented software testing method based on transformation test provided by the embodiment of the present invention can firstly use the existing random data generator tool to randomly generate a data set, including a training set and a test set. The dataset is the source dataset. The source data set is input to the program to be tested for training, and the linear classifier model obtained by training is called the source model. The hyperplane model is a type of machine learning model, and the source model is a hyperplane model. The source dataset and the source model are input to the new dataset generation module, and the new dataset generation module generates a new dataset. A set of new models is obtained by training the program to be tested on the new dataset, and the new model is also a hyperplane model. ...

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 discloses a linear classifier-oriented software testing method based on metamorphic testing, which comprises the following steps of: 1, randomly generating a plurality of groups of training data sets and testing data sets as source data sets, and testing a to-be-tested program by utilizing the source data sets to obtain a training result; 2, generating a new data set according to a mode in a metamorphic relation according to the hyperplane model obtained by the source data set, and training on the new data set to obtain a group of new hyperplane models; and 3, calculating whether the hyperplane model obtained by training the hyperplane model and the new data set meets a metamorphic relation, and if the metamorphic relation is not met on any training data set, judging that the program has bug. According to the stability of the linear classifier, two new metamorphic relations are provided, the test result is more accurate, the training result on the source data set is applied when the source data set generates the new data set, and the test effect is improved.

Description

technical field [0001] Invention patents relate to technical fields such as software engineering, testing of machine learning programs and software testing. Background technique [0002] Machine learning technology is widely used in various fields of daily life, such as image processing, object recognition, autonomous driving, etc. However, the technology related to the quality assurance of machine learning software is not mature enough, because the software quality assurance of machine learning faces two difficulties. First, the statistical characteristics of machine learning cannot be carried out according to the effect of the model trained by the machine learning program. logical analysis. Second, machine learning programs often do not have test predictions. Even if the model trained by the machine learning program can achieve a prediction accuracy rate of more than 90% on a certain data set, it does not mean that there are no bugs in the program. Therefore, research on...

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): G06F11/36G06K9/62G06N20/10
CPCG06F11/3684G06F11/3688G06N20/10G06F18/2411G06F18/214
Inventor 许畅杨英卓李泽南王慧妍马晓星
Owner NANJING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products