Test case set generation method based on combination chaotic sequence

Abstract

The invention discloses a test case set generation method based on a combination chaotic sequence; the method comprises the following steps in sequence: a, using Chebyshev mapping and Logistic mapping to combine, thus forming the chaotic sequence; b, using the chaotic sequence to initialize population; c, setting an individual fitness function; d, defining a heredity operator; e, adding chaotic disturbance. The method uses the combination chaotic mapping to generate the initial population, each population individual comprises n genes, the population individual is a possible test case selection scheme, the combination chaotic sequence with uniform distribution characteristic is introduced to heredity algorithmic selection, intersect and variation operation, thus effectively preventing immature convergence, improving algorithmic global search capability, and calculating efficiency chaotic system generation initial population genes, adding chaotic small disturbance to each chaotic variable so as to carry out population optimization, and restraining to a proper individual through continuously evolution; test case minimization is converted into a method in which the individual with least genes is searched in the population, thus covering all test needs.

Description

【Technical field】 [0001] The invention relates to the field of computer software performance testing, in particular to a method for generating test case sets used in computer software performance testing. 【Background technique】 [0002] Computer systems have been applied to all aspects of social life, and the software forms are more complex and diverse, and the scale is getting larger and larger. In such a background, it is particularly important to ensure the quality of software so that it can accurately complete the expected goals. Software testing is an essential part of software quality assurance. In order to meet a certain test coverage rate, the number of test cases generated is often extremely large; on the other hand, the iteration of the software system development process requires frequent regression testing, and redundant testing serious. In order to improve test efficiency, reduce test cost, and reduce the overhead of test case execution, management and mainten...

AI Technical Summary

Problems solved by technology

[0004] Among these methods, the genetic algorithm has achieved good application in test case optimization due to its good global search ability and fast search efficiency, but its local search ability is weak, and it is prone to problems such as premature convergence.

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