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Software test case evolution generation method based on search domain reduction

A software testing and search domain technology, applied in software testing/debugging, genetic rules, genetic models, etc., can solve the problem of low efficiency in generating test cases, achieve the effect of increasing speed, reducing search domain, and reducing cost

Inactive Publication Date: 2021-04-30
XUZHOU UNIV OF TECH
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Problems solved by technology

[0005] In order to solve the problem of low efficiency of software test generation test cases in the prior art, the present invention provides a software test case evolution generation method based on search domain reduction, which can quickly obtain variants by dynamically analyzing the correlation between adaptation value changes and input variables Correlation with input variables, and then select relevant input variables as decision variables, which is equivalent to reducing the search domain, and using genetic algorithm to efficiently generate mutation test cases

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  • Software test case evolution generation method based on search domain reduction
  • Software test case evolution generation method based on search domain reduction
  • Software test case evolution generation method based on search domain reduction

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Embodiment Construction

[0062] Such as figure 1 As shown, it is a general flowchart of a software test case evolution generation method based on search domain reduction proposed by the present invention. The method includes:

[0063] Step S1: Dynamically determine the correlation between variants and input variables

[0064] Let the program under test be G, implement mutations on the sentences it contains, and obtain the variant set as M={M 1 , M 2 ,...,M n}, n is the number of variants. Insert the variant branches transformed by these variants into G to obtain a new tested program G'. Let the input vector of the program be X=(x 1 ,x 2 ,...,x m ), m is the number of program input variables. The input domain D(X) is the cross product of each input variable domain, that is, D(X)=D(x 1 )×D(x 2 )×…×D(x m ).

[0065] 1.1 Build an optimization model for traditional mutation test case generation

[0066] remember f i (X) is the objective function, which reflects whether the input vector X of ...

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Abstract

The invention discloses a software test case evolution generation method based on search domain reduction, and aims to dynamically determine the correlation between an input variable and a variant, remove an irrelevant input variable equivalent to reduction of a search domain, and efficiently generate a variation test case by adopting a genetic algorithm. The method comprises the steps that firstly, a mutation test case generation optimization model is established, a fitness function is designed, and then the correlation between a variant and an input variable is dynamically determined based on fitness change; then, a mutation test case generation optimization model is improved, the decision variable is a related input variable, and the mutation test case generation optimization model based on the related input variable is established; and finally, for the new model, a test case is effectively generated by adopting a genetic algorithm in a search domain formed by related input variables.

Description

technical field [0001] The invention relates to the field of computer software testing, in particular to an evolutionary generation method for software testing cases based on search domain reduction. Background technique [0002] Software testing is an important means to ensure software quality. The cost of software testing is relatively high, mainly from the labor cost of designing good test cases and checking their output. Therefore, many techniques are designed to improve the efficiency of software testing. Mutation testing has proven to be a powerful technique for software testing. Mutation testing involves modifying programs by introducing simple syntactic changes and creating potentially erroneous versions, called variants. After a test case executes the original program and variants, check whether the output or state changes. A mutant is killed by a test case, so that the mutant and the original program produce different outputs, then the test case is considered v...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06N3/12
CPCG06F11/3684G06N3/126
Inventor 党向盈巩敦卫姚香娟鲍蓉姜代红阮少伟厉丹陈磊李子龙包季楠袁偲朕
Owner XUZHOU UNIV OF TECH
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