CGA-based Test Suite Generation for Branch Coverage of MPI Programs
For the branch coverage testing of programs,meta-heuristic search techniques have been widely used in test data generation.However,current researches are mainly applicable to sequential programs.Therefore,to cover the branches in a Message Passing Interface(MPI)program,we propose a method of generating test suite based on the Co-evolutionary Genetic Algorithm(CGA),which has the advantage of being unaffected by infeasible branches.To fulfill this task,we firstly define the minimum normalized branch distance based on the probes that collect the coverage information,and design the corresponding fitness function.Then,we use CGA to generate evolutionary individuals and calculate the fitness of these individuals based on the designed fitness function.Finally,the representative in-dividuals in each subpopulation are selected to form a cooperative population based on the calculated fitness.The proposed method is applied to 7 benchmark MPI programs and compared with some state-of-the-art methods.The experimental results show that the coverage rate of the proposed method is usually higher than that of other methods.
message passing interface(MPI)programco-evolutionary genetic algorithmbranch coverage testingtest suite generationfitness function
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