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

Software defect prediction method and system

A software defect prediction and algorithm technology, applied in the field of software security, can solve problems such as low prediction accuracy

Active Publication Date: 2014-05-21
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF2 Cites 28 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a software defect prediction method and a software defect prediction system, which are used to solve the problem that the accuracy of the existing software defect prediction is not 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 system
  • Software defect prediction method and system
  • Software defect prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0041] The technical idea of ​​the present invention is aimed at the limitations of the existing dimensionality reduction methods, that is, the result after dimensionality reduction cannot guarantee the integrity of the data, nor is it the best embodiment of the intrinsic dimensionality. The embodiment of the present invention adopts the locally linear embedding (LLE for short) algorithm to reduce the dimensionality of the software defect data set. The idea of ​​the algorithm is to start from the spatial structure of the sample data and to ensure the geometric structure of the data sample after dimensionality reduction. Unchanged, so that the data after dimensionality reduction can more completely reflect the various characteristics of ...

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 provides a software defect prediction method and system. The software defect prediction method and system are used for resolving the problem that existing software defect prediction is low in accuracy. The software defect prediction system comprises a dimension reduction processing unit, an SVM training unit and a defect prediction unit. The software defect prediction method comprises the steps that firstly, dimension reduction processing is conducted on a first training dataset according to the LLE, a lower-dimension vector, mapped into a low-dimension space, of each sample point in the first training dataset is obtained, and a second training dataset composed of the lower-dimension vectors is obtained; secondly, training is conducted on an SVM classifier according to the second training dataset, an optimal classification hyperplane function of the SVM classifier is obtained, and the trained SVM classifier is further obtained; thirdly, detect prediction is conducted on software to be predicted according to the trained SVM classifier.

Description

technical field [0001] The invention relates to the field of software security, in particular to a software defect prediction method and a software defect prediction system. Background technique [0002] Software defect prediction technology was born in the 1970s, and its main function is to guide quality assurance work and provide high-value reference for balancing software costs. Software defect prediction is mainly divided into dynamic prediction and static prediction. At present, main researches are concentrated on static prediction. The present invention belongs to distribution prediction technology in static prediction. Support Vector Machine (SVM for short) is a new machine learning method developed on the basis of statistical learning theory. It has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. The existing Software defect prediction mainly utilizes the support vector machine (SVM) tool to establish a prediction ...

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): G06F11/36
Inventor 胡昌振单纯陈博洋马锐王勇
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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