A Path Coverage Test Data Generation Method for Weak Mutation Test

Abstract

The invention discloses a path coverage test data generation method used for a weak mutation test, and aims to convert a weak mutation test problem into a path coverage problem so as to effectively improve mutation test efficiency and generate test data which has a high defect detection capability. The path coverage test data generation method comprises the following steps: firstly, on the basis of weak mutation test criterion, constructing mutation branches, and adopting a certain method to generate a mutation branch correlogram on the basis of relevancy between the mutation branches and the coverage difficulty of each mutation branch; then, on the basis of the correlogram, according to a certain strategy, generating en executable path set which can be easily covered; thirdly, constructing a multi-target optimization mathematical model of a multi-path coverage test data generation problem; and finally, adopting a multi-population parallel genetic algorithm to generate the test data which covers the paths.

Description

technical field [0001] The invention relates to the field of computer software testing, and designs a method for generating path coverage test data for weak mutation testing. The difference between this method and the original method is that it converts the weak mutation test problem into a path coverage problem, and the generated target paths are easy to cover and the path set is relatively small. Covering these path test data has a strong error detection ability. Background technique [0002] Software testing is an important means to ensure software quality. Through testing, not only can detect possible defects in software, but also improve the reliability of software. Mutation testing is a defect-oriented software testing method. Through manual analysis, defects are inserted into the program to simulate real defects. These defects largely reflect the real defects of the software. [0003] Mutation testing refers to making a small grammatical change to a certain sentence ...

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to overcome the low efficiency of the existing weak mutation test, transform the defect-oriented weak mutation test problem into a mature path coverage problem; and use the genetic algorithm to generate test data, which can not only pass through the target path, and can find defects in the target path, improving the efficiency of software testing

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