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An Evolutionary Generation Method of Test Data Based on GA Algorithm

A technology of testing data and algorithms, applied in software testing/debugging, electrical digital data processing, computing, etc., can solve problems affecting efficiency, premature stagnation, no protection of test data, etc., and achieve the effect of improving efficiency and strengthening computing power

Active Publication Date: 2020-09-22
HANGZHOU HUICUI INTELLIGENT TECH CO LTD
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AI Technical Summary

Problems solved by technology

However, some inherent defects of genetic algorithm, such as premature stagnation, easy to fall into local optimum, low search efficiency in the later stage, etc., affect the efficiency of test generation
Moreover, the existing fitness value function design methods do not effectively use the comprehensive information reflected by the evolutionary population, and thus do not protect the generated test data well during the evolution process.

Method used

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  • An Evolutionary Generation Method of Test Data Based on GA Algorithm
  • An Evolutionary Generation Method of Test Data Based on GA Algorithm
  • An Evolutionary Generation Method of Test Data Based on GA Algorithm

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Embodiment Construction

[0049] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation, but not as a limitation of the present invention.

[0050] figure 1 It is a flowchart of a method for generating test data evolution of a GA algorithm implemented in the present invention. The concrete steps of this method are as follows:

[0051] 1) Transform the feasible solution of the problem from its solution space to the search space that can be processed by the genetic algorithm, initialize the subpopulation, assign values ​​to the parameters of the algorithm, and use bit string encoding to establish a pair between the individual chromosomes of the population and the binary string A mapping relationship; this binary string can be expressed as:

[0052]

[0053]2) Insert the program under test, input the test case into the program to execute the individual distance of the calculation population; calculate the node coverage and bra...

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Abstract

The invention provides an IMPE-GA algorithm-based test data evolution generation method. The method comprises steps of introducing a multi-population idea, considering individual similarities of the multi-populations and influences of variety of the populations to the test data, considering effects of branch distance and node coverage, and therefore adding factors of influence to the improved computational formulas, and adding weight factors, and conducting weight allocation to the factors of influences to facilitate test case dynamic self-adapting adjustment. With crossover rate and aberration rate dynamic self0adapting adjustment strategy, the algorithm can be enhanced in light of operational capability. Individual contribution degree can be calculated and further a traditional fitness function can be adjusted; and excellent individuals can be stored after the evolution, so test data generation efficiency can be improved.

Description

technical field [0001] The invention belongs to the field of software testing, and in particular relates to a test data evolution generation method based on GA algorithm. Background technique [0002] Software testing is a vital link in software system development to ensure software quality, and a large proportion of software development costs is spent on testing. If the testing process can be automated, it will greatly reduce the cost of software development and improve testing efficiency. The generation of test cases includes determining test requirements, determining input data, running the program under test and analyzing the corresponding output data. The design of automatic test case generation technology is an important problem in software automation testing. Solving this problem is very important to ensure software quality, and it is the guarantee to improve software quality and reliability. [0003] As a heuristic search algorithm, genetic algorithm has the advant...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36G06N3/12
CPCG06F11/3684G06N3/126
Inventor 包晓安郑腾飞张娜熊子健
Owner HANGZHOU HUICUI INTELLIGENT TECH CO LTD
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