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A Method for Predicting the Severity of Software Defect Modules Based on Ordered Neural Network

A neural network model and severity technology, applied in the field of software quality assurance, can solve problems affecting the prediction of software defect severity and achieve high accuracy.

Active Publication Date: 2021-11-12
NANTONG UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] It can be seen that considering the order of software defect module severity level labels is also a problem that affects the prediction of software defect severity

Method used

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  • A Method for Predicting the Severity of Software Defect Modules Based on Ordered Neural Network
  • A Method for Predicting the Severity of Software Defect Modules Based on Ordered Neural Network
  • A Method for Predicting the Severity of Software Defect Modules Based on Ordered Neural Network

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Embodiment Construction

[0025] In order to further understand the present invention, the method for predicting the severity of software defect modules based on an ordered neural network provided by the present invention will be described in detail below in conjunction with the embodiments, and the protection scope of the present invention is not limited by the following embodiments.

[0026] Please refer to figure 1 , the invention provides a method for predicting the severity of a software defect module based on a neural network, comprising the following steps:

[0027] S110: measure the program module of the sample software project by using the measurement element, the granularity of the program module can be set as a file or class according to the project development language (that is, if the C / C++ programming language is analyzed, the granularity of the program module is set as a file, If you are analyzing the Java programming language, set Program Module Granularity to Class).

[0028] In the e...

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Abstract

The invention belongs to the field of software quality assurance, and discloses a software defect module severity prediction method based on an ordered neural network. The method provided by the present invention includes the following steps: mining the version control system and the defect tracking system where the sample software project is located, measuring the program modules of the sample software project and marking the severity of defects, and constructing a sample data set; based on the sample data set, using An ordered neural network model and a Bayesian hyperparameter optimization method are used to obtain a software defect prediction model, that is, an ordered neural network model with optimal hyperparameters; the software defect prediction model is used to predict the severity of defects in program modules in software projects. Compared with conventional software defect prediction methods, the software defect prediction model constructed by the present invention can not only predict software defects, but also predict the severity of defects, and the prediction accuracy is higher.

Description

technical field [0001] The invention belongs to the field of software quality assurance, in particular to a method for predicting the severity of software defect modules based on an ordered neural network. Background technique [0002] With the rapid development of information technology, the complexity of software is constantly increasing, and the scale of software is also increasing. The dependence on information technology makes the guarantee of software quality particularly important. Especially in some important fields, once the software system fails, it may bring huge losses. When there are some serious defects in the software system, it will lead to serious consequences such as software errors, function failures, and crashes. Therefore, realizing the detection of modules with higher severity in the software system in the early stage of software can reduce product production and maintenance costs, and enhance the competitiveness of enterprises. Therefore, the predicti...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36G06K9/62G06N3/04G06N3/08
CPCG06F11/3604G06N3/084G06N3/045G06F18/24155G06F18/214
Inventor 陈翔贾焱鑫李春明葛骅杨光林浩
Owner NANTONG UNIVERSITY