In order to improve the generation efficiency of multipath coverage test data

a novel method is proposed based on ant colony algorithm (ACO). Firstly

an improved ACO is developed. The importance of an ant to generate test data is considered as a factor for ant state transfer and path mutation. As a result

more ants are guided to traverse small probabilities node and the efficiency of test data generation is improved. Secondly

according to the improved ACO

test data generation of multipath coverage based on single pheromone table and multiple pheromone tables are proposed. In a multiple pheromones table based approach

the pheromone table of each target path is also used to generate test data for other target path

and the test data of multiple paths are generated by running ACO only once. Finally

the effectiveness and complexity of the proposed method are analyzed theoretically. Experimental results show that test data generation based on multi-pheromone tables can effectively generate multipath coverage test data compared with other methods.