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

Software defect prediction method and device based on K-B, electronic equipment and medium

A software defect prediction, K-B technology, applied in software testing/debugging, computer parts, error detection/correction, etc., can solve problems such as long time and high machine performance requirements

Pending Publication Date: 2022-05-03
CHINA PETROLEUM & CHEM CORP +1
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the long time for training models based on complex neural networks, the requirements for machine performance are high

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
  • Software defect prediction method and device based on K-B, electronic equipment and medium
  • Software defect prediction method and device based on K-B, electronic equipment and medium
  • Software defect prediction method and device based on K-B, electronic equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0110] figure 1 A flowchart showing the steps of the K-B-based software defect prediction method according to an embodiment of the present invention.

[0111] Such as figure 1As shown, the software defect prediction method based on K-B includes: step 101, collecting software historical defect data, and dividing the software historical defect data into a training data set and a test data set; step 102, reducing the metric element in the training data set Dimension, obtain feature vector; Step 103, carry out Bayesian classification regression calculation training according to the training data set and feature vector after dimensionality reduction; Step 104, adjust dimensionality reduction parameter and Bayesian parameter, obtain optimal model; Step 105 , according to the optimal model, dimensionality reduction is performed on the metric elements in the test data set, and Bayesian classification and regression calculations are performed to predict the defects of the test data se...

Embodiment 2

[0121] image 3 A block diagram of a K-B-based software defect prediction device according to an embodiment of the present invention is shown.

[0122] Such as image 3 As shown, the K-B-based software defect prediction device includes:

[0123] The data set division module 201 collects software historical defect data, and divides the software historical defect data into a training data set and a test data set;

[0124] The dimensionality reduction module 202 performs dimensionality reduction on the metric elements in the training data set to obtain feature vectors;

[0125] The training module 203 performs Bayesian classification and regression calculation training according to the training data set and feature vector after dimensionality reduction;

[0126] Optimal model building module 204, adjusting dimensionality reduction parameters and Bayesian parameters to obtain an optimal model;

[0127] The prediction module 205 performs dimensionality reduction on the metric e...

Embodiment 3

[0141] The present disclosure provides an electronic device comprising: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above K-B-based software defect prediction method.

[0142] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0143] The memory is used to store non-transitory computer readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache). The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like.

[0144] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capabili...

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 K-B-based software defect prediction method and device, electronic equipment and a medium. The method comprises the following steps: collecting software historical defect data, and dividing the software historical defect data into a training data set and a test data set; dimensionality reduction is carried out on metric elements in the training data set, and feature vectors are obtained; performing Bayesian classification regression calculation training according to the training data set after dimension reduction and the feature vector; adjusting dimension reduction parameters and Bayesian parameters to obtain an optimal model; according to the optimal model, dimension reduction is carried out on metric elements in the test data set, Bayesian classification regression calculation is carried out, and the defects of the test data set are predicted. According to the method, the dimensionality problem of the metric element is solved, the accuracy of software defect prediction is improved, a new feasible method is provided for software defect prediction, the number of defects of a subsequent software system can be predicted, reference indexes are provided for making a software test plan, and manpower and time are better planned.

Description

technical field [0001] The present invention relates to the fields of software testing and data mining, and more specifically, to a K-B-based software defect prediction method, device, electronic equipment and media. Background technique [0002] Since 1970, software defect prediction technology has begun to develop; as the scale of software systems becomes larger and the logic becomes more complex, software defects are bound to increase, affecting software quality. Because software defect prediction helps testers understand the status and Quality, helping to set delivery standards, so the prediction of software defects has also become important. [0003] At present, software defect prediction is divided into static and dynamic two prediction methods. With the number of software iterative updates and the increase of similar software, it has become a practical method to predict the number, type, and distribution of defects based on historical software development data and th...

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): G06F11/36G06K9/62
CPCG06F11/3608G06F18/2135G06F18/214G06F18/24155
Inventor 王婷婷
Owner CHINA PETROLEUM & CHEM CORP
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