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Dynamic optimization method of evolutionary testing based on catastrophe

A dynamic optimization and catastrophe technology, applied in the dynamic optimization field of catastrophe-based evolution test, can solve the problems of evolution test getting rid of the local optimal solution and failing to find the global optimal solution, so as to solve the problem of premature population degradation and improve performance Effect

Inactive Publication Date: 2012-10-24
SOUTHEAST UNIV
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Problems solved by technology

This method is only suitable for the prevention of evolutionary stagnation problems, but once the population matures prematurely and degenerates, the static optimization method cannot help the evolution test get rid of the local optimal solution, resulting in the failure to find the global optimal solution

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  • Dynamic optimization method of evolutionary testing based on catastrophe
  • Dynamic optimization method of evolutionary testing based on catastrophe
  • Dynamic optimization method of evolutionary testing based on catastrophe

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Embodiment Construction

[0026] Step 1). Analyze the specified program as the test object and construct the corresponding control flow graph, specify the test target in the test object and construct the fitness value function according to the specified test target. The test target is well-known statement coverage, branch coverage and path coverage.

[0027] An example of constructing a fitness value function is as follows: Take the branch condition if (a==b) as an example, where a and b are integer variables in the program, when the target branch is the "true" branch of the decision, for the test case The simplest fitness value function can be defined as fitness=1 / (|a-b|+k), when a=b, set fitness=1.0, where fitness represents the fitness value, k∈(0,0.01); another example , for the branch decision condition if(a>=b), where a and b are the same as the above example, when the target branch is a "false" branch, the fitness value function can be defined as fitness=1 / ((a-b)+k), where k ∈(0,0.01). Assume ...

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Abstract

The invention provides a dynamic optimization method of an evolutionary testing based on a catastrophe, which is mainly used for solving the phenomenon of population prematurity and degeneration appearing in the evolutionary process of the evolutionary testing. The invention relates to key operations as follows: (1) when an initial population is generated, the diversity of the initial population needs to be measured; if the diversity of the initial population is greater than a first threshold value, individuals in the population are shown to be excessively dispersed, and the search process ismore difficult to converge; at the moment, the initial population needs to be regenerated until the diversity of the initial population is less than the first threshold value; (2) the diversity of the population is periodically measured in the evolutionary process, and once the diversity of the population is less than a second threshold value and a globally optimal solution is not found, the population is premature and degenerated, and at the moment, a catastrophe operation is adopted so as to help the group recover the diversity.

Description

technical field [0001] The invention is a dynamic optimization method of catastrophe-based evolution test, which is mainly used to deal with the phenomenon of premature degeneration of population in the evolution process of evolution test. Background technique [0002] Typically, software testing consumes more than 50% of the resources in the entire software development process. In order to improve test efficiency and reduce test cost, researchers have done a lot of research on automated test technology. Among them, the random testing technology has been widely used. In addition to realizing a high degree of automation, this technology also has the advantages of simplicity and strong operability. However, due to the blind search method, random testing often produces a large and inefficient test case set, which seriously reduces the defect detection ability of testing. Therefore, in order to make up for the defects in random testing and further improve the efficiency of aut...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36
Inventor 王猛李必信王正山蒋玉婷张功源邱栋吉顺慧
Owner SOUTHEAST UNIV
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