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Path coverage test data generation method based on a negative selection genetic algorithm

A technology of path coverage testing and negative selection, applied in the field of computer software testing, can solve the problems of short test time and premature convergence of genetic algorithm coverage

Active Publication Date: 2019-06-21
MUDANJIANG NORMAL UNIV
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Problems solved by technology

[0005] In view of the foregoing, the present invention proposes a new evolutionary generation method for software test data covering the target path, i.e. Negative Selection Genetic Algorithm (Negative Selection Genetic Algorithm, NSGA), which incorporates a negative selection strategy in the genetic algorithm, which can Solve the problem of premature convergence of the traditional genetic algorithm, and make better use of the advantages of the high coverage rate and short test time of the genetic algorithm

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  • Path coverage test data generation method based on a negative selection genetic algorithm
  • Path coverage test data generation method based on a negative selection genetic algorithm
  • Path coverage test data generation method based on a negative selection genetic algorithm

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Embodiment Construction

[0034] Embodiments of the present invention will be described in detail below in conjunction with specific drawings and example programs.

[0035] Step 1. Negative Selection Genetic Algorithm Method Design

[0036] 1.1 Negative selection strategy

[0037] The basic idea of ​​the negative selection strategy is to generate several detection data in the search space, and then apply these detection data to classify new data as self-set or non-self-set. The negative selection strategy is divided into two phases: the generation phase (also called the training phase) and the detection phase (also called the testing phase). First, in the generation phase, a stochastic process is used to generate detection data and the stochastic process is supervised. Candidates that match self-samples are discarded, and candidates that do not match are stored into the detection set. The generation phase terminates when a sufficient amount of detection data (detection set) is generated. In the det...

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Abstract

The invention discloses a path coverage test data generation method based on a negative selection genetic algorithm which aims to ensure that generated test data has a relatively high coverage rate and contain least redundant data, thereby improving the quality of the test data and the efficiency of software testing. The method comprises the following steps: firstly, generating an initial population of a negative selection genetic algorithm in a supervised manner according to a negative selection generation strategy. Then, according to a negative selection detection strategy, dynamically optimizing population data of a negative selection genetic algorithm, and evolving to generate test data covering the target path. The test data generated by the conventional method only guarantees the coverage of a target path. Compared with the prior art, the method has the advantages that the number of generated test data cannot be guaranteed to be minimum, a large number of redundant test data arecontained, the problems can be effectively solved, generation of redundant test data is reduced, premature convergence of an algorithm can be avoided, and the software test efficiency can be improvedto a great extent.

Description

technical field [0001] The invention relates to the field of computer software testing, and designs a new method for generating software testing data to realize the coverage of target paths. This method is different from the original method in that it can effectively reduce the generation of redundant test data on the basis of ensuring that the generated test data has a high coverage rate, thereby shortening the test time and improving the efficiency of software testing. Background technique [0002] Software testing is an important part of the software life cycle and an important means to ensure software quality and improve software reliability. Research data show that the software testing process accounts for more than half of the total cost of software development. Software testing is divided into white box testing (also known as structural testing), black box testing, and gray box testing in between according to the degree of visibility of the testing work on the softwa...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06N3/00G06N3/12
Inventor 夏春艳张岩肖楠
Owner MUDANJIANG NORMAL UNIV
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