An improved sorting learning method for software defect prediction

A technology for software defect prediction and sorting learning, which is applied in software testing/debugging, error detection/correction, instrumentation, etc., and can solve problems such as module sorting is not necessarily good

Inactive Publication Date: 2019-06-28
SUN YAT SEN UNIV
View PDF10 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problem with constructing a software defect prediction model for ranking tasks in this way is that the module ranking given by the model with good fitting degree is not necessarily good.

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
  • An improved sorting learning method for software defect prediction
  • An improved sorting learning method for software defect prediction
  • An improved sorting learning method for software defect prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0023] The invention belongs to the software defect prediction based on metric element in the static defect prediction technology, and mainly solves the software defect prediction problem of sorting tasks. The present invention will be further described in detail below with reference to the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0024] In this embodiment, the improved ranking learning method for software defect prediction mainly includes the following steps:

[0025] Step 1: Collect training data, extract metric elements and corresponding defect numbers from source code files with known defect numbers as training data.

[0026] When extracting metrics, it can be extracted from the following aspects: process metrics, past defects, source code metrics, change entropy, changes in source code metrics, and entropy of source code metrics. Each module corresponds to a metric element vector, defining x i Rep...

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 an improved sorting learning method for software defect prediction, which comprises the following steps of: extracting a metric element and a corresponding defect number froma source code file with a known defect number as training data; preprocessing the training data; using the preprocessed training data to construct a prediction model; using a multi-objective optimization algorithm to optimize the sorting performance of the model at the same time; obtaining a group of model parameters according to the model parameters, the regression performance and the model complexity, then selecting appropriate model parameters according to actual needs, or giving corresponding weights to the sorting performance, the regression performance and the model complexity of the model according to the actual needs, and then obtaining the model parameters by using a single-objective optimization algorithm; and analyzing the test data by using the trained model to obtain defect information of the corresponding software module. The sorting performance, the regression performance and the model complexity of the model can be optimized, and different requirements under different application scenes can be better met.

Description

technical field [0001] The invention relates to the field of software analysis and defect prediction in software engineering, in particular to an improved sorting and learning method for software defect prediction. Background technique [0002] Software defect prediction began in the 1970s. It refers to the use of statistical learning techniques to predict the number and types of defects in software systems based on software measurement data such as historical data and discovered defects. The purpose of defect prediction technology is to count and calculate the number of defects in the software system and the number of defects that have not been discovered but may still exist, so as to determine whether the system can be delivered. Defect prediction technology has promoted the improvement of software quality, and at the same time, it has also made software engineering technology develop a step forward. [0003] The invention patent "Cost-sensitive semi-supervised software d...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06F11/00
Inventor 杨晓杏温武少李鑫
Owner SUN YAT SEN 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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap