Multi-task software test case evolution generation method

Abstract

The invention discloses a multi-task software test case evolution generation method, and aims to convert a variation test problem into a traditional coverage path test problem according to the executability of variation branches and program paths, and efficiently generate test cases with defect detection capability by adopting a multi-task parallel mode. The method comprises the following steps: firstly, statically analyzing execution correlation of variant branches and program paths, and dividing the variant branches with the same execution path into the same group; secondly, establishing a mutation test case generation multi-task optimization model based on path coverage for the multiple groups of mutation branches; and finally, solving the model by utilizing a multi-population genetic algorithm, and efficiently generating a test case with defect detection capability by adopting a multi-task parallel mode. According to the method, the variant branches are grouped according to the paths to which the variant branches belong, and a traditional mature path testing method is adopted, so that the software testing efficiency is improved, and the test case with high defect detection capability is generated.

Description

technical field [0001] The invention relates to the field of computer software testing, in particular to a method for evolutionary generation of multi-task software test cases. Background technique [0002] Software testing refers to the detection of defects in a software or software system by manual or automatic methods. Mutation testing is a powerful but expensive testing technique, especially for testing data that kills a large number of variants is very difficult to obtain. A variation branch is composed of the original statement and its variation statement. The true branch of the mutation branch is covered by a test data, indicating that the corresponding mutation is killed under the weak mutation test criterion. [0003] A program under test generally produces many variants. In order to kill these variants, a large number of test cases are needed; moreover, these test cases need to execute the original program and variants at the same time, so the efficiency of mutat...

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Problems solved by technology

[0006] In order to solve the problem of low efficiency in the generation of test cases for detecting numerous software defects in the prior art, the present invention proposes a method for evolutionary generation of multi-task software test cases. The method is different from the original method in that it combines The mutation branches are divided into the same group. For multiple mutation branch groups, a multi-task test case generation mathematical model is established, and test cases with high defect detection capabilities are generated in parallel.

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