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Software test case multi-population evolution generation method based on variant grouping

A technology for software testing and mutation testing, applied in software testing/debugging, genetic laws, genetic models, etc., to solve problems such as low efficiency in generating test cases

Inactive Publication Date: 2021-04-23
XUZHOU UNIV OF TECH
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Problems solved by technology

[0007] In order to overcome the low efficiency of existing software test generation test cases, the present invention proposes a multi-population evolutionary generation method for software test cases based on variant grouping, based on weak mutation test criteria, dynamically determines the correlation between input variables and variants, and establishes Mutation test case multi-task optimization model; for multiple groups of tasks, the multi-population genetic algorithm is used to efficiently generate test cases with defect detection capabilities

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  • Software test case multi-population evolution generation method based on variant grouping
  • Software test case multi-population evolution generation method based on variant grouping
  • Software test case multi-population evolution generation method based on variant grouping

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Embodiment Construction

[0070] Such as figure 1 As shown, it is a general flow chart of a method for generating multi-population evolution of software test cases based on variant grouping proposed by the present invention. The method includes:

[0071] Step S1: Group variants based on the same input variables

[0072] S1.1 Design fitness function

[0073] 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. The variants transformed by these variants are inserted 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 ).

[0074] remember f i (X) is the objective function, which reflects whether the input vector X of the program can cover the mutant, that is, whether to kill...

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Abstract

The invention discloses a software test case multi-population evolution generation method based on variant grouping, and aims to dynamically determine the correlation between an input variable and a variant based on a weak variation test criterion and establish a variation test case multi-task optimization model. For multiple groups of tasks, a multi-population genetic algorithm is adopted to efficiently generate test cases with defect detection capability. Firstly, the correlation between variants and input variables is determined based on the change of adaptive values, and the variants are grouped according to the same input component; secondly, a mutation test case generation multi-task optimization model is constructed based on related input variables; and finally, in order to solve the model, a multi-population genetic algorithm is utilized to evolve and generate a test case in a parallel mode.

Description

technical field [0001] The invention relates to the field of computer software testing, in particular to a method for generating multi-population evolution of software test cases based on variant grouping. Background technique [0002] Software testing is one of the important processes in the software development life cycle. It ensures that the high quality system reaches the customer is reliable. In order to find defects in software programs, test case execution software needs to be designed. Software testing, which was previously underestimated, is now given the same importance as software development. Studies have found that 40-50% of cost, effort and time is spent on testing and 50% on software development, and this cost is said to be even higher for critical software. [0003] Mutation testing is an important and powerful white-box testing technique. The testing technique is to deliberately change some parts of the program, this change is in line with the requiremen...

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Application Information

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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徐玮玮陈磊厉丹包季楠袁偲朕
Owner XUZHOU UNIV OF TECH
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