A population is partitioned into several sub-populations

the genetic operations for each sub-population are executed

and parallel genetic algorithms are executed by cores to search the test paths to speed up generating test cases.The individualities for population are enriched by migrating thee individualities for sub-populations among cores

the optimization solutions are searched globally better

and more test cases for covering all the test paths are found.The experiment results show that compared with the sequential generating test cases method using genetic algorithm

the multi-core parallel generating test cases method using parallel genetic algorithm can obtain more generated test cases and require less execution time.