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Software defect module severity prediction method 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: 2020-10-30
NANTONG UNIVERSITY
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  • Abstract
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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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  • Software defect module severity prediction method based on ordered neural network
  • Software defect module severity prediction method based on ordered neural network
  • Software defect module severity prediction method 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 guarantee, and discloses a software defect module severity prediction method based on an ordered neural network. The method comprises the following steps that a version control system and a defect tracking system where a sample software project is located are mined, measurement and defect severity marking are conducted on a program module of the sample software project, and a sample data set is constructed; based on the sample data set, an ordered neural network model and a Bayesian hyper-parameter optimization method are adopted to obtaina software defect prediction model, namely, the ordered neural network model with optimal hyper-parameters; and the defect severity of the program module in the software project is predicted by adopting the software defect prediction model. Compared with a conventional software defect prediction method, the method for constructing the software defect prediction model has the advantages that software defect prediction can be performed, the severity of defects can be predicted, 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 Applications(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
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