Research On Reduction Method Of Test Cases Based On Mutation Association Analysis

With the rapid development of the software industry,software applications have been integrated into people’s daily lives,how to effectively ensure the quality of software has become crucial.For this reason,efficient,applicable,scalable and effective software testing technology for defect detection has received wide attention.Mutation testing is considered as a defect-based software testing technology,which provides specific testing objectives and strict test validity standards in program analysis,defect detection and test case generation.However,the resulting mutants contain many unnecessary and operable mutants,which makes the execution of mutation testing inefficient.At the same time,in the process of regression testing,because each software modification needs to redefine test requirements,test cases need to be constantly adjusted and executed,so there are some test cases with the same defect detection capabilities,which makes the testing cost increasing.Therefore,in order to improve the overall testing efficiency and reduce its testing cost,this paper combines the mutant containment method and the improved Apriori algorithm to study test case reduction,the main contents are as follows:(1)A mutant reduction method based on variance containment is proposed to solve the problem that the number of mutants generated is large and the execution efficiency of variance testing is low.First,perform a mutation analysis on the original program to create all possible variants.The kill matrix is then generated by the detection relationship between the mutant and the test case,and the values in the matrix represent whether the mutant was killed or not.Next,the kill matrix is analysed to obtain the dynamic mutation containment relationship between mutants.Finally,based on this relationship,redundant mutants were found and eliminated,resulting in an average mutant reduction rate of69.83%,reduced mutant size generated,and improved overall performance of mutation testing.(2)A test case reduction method based on improved Apriori is proposed to solve the problem of redundancy in test case set,which leads to high calculation cost and increased test cost.First,the mutation test is executed,then the mutation transaction set matrix is constructed,and then the matrix is compressed continuously to obtain frequent itemsets at all levels.Finally,the association rules between test cases are found based on the frequent itemsets and their corresponding confidence levels,and the test cases are simplified.The results show that the average test case reduction rate of this method is 45%.Compared with the other two algorithms,the average reduction rate is increased by 7% and 5%,and the time required to run the test is decreased by 46.1% and38.4%,respectively.To sum up,the methods presented in this paper are effective in reducing test cases to a certain extent,reducing test costs and improving test efficiency.