The increase of communication scale and non-deterministic communication make the communication test of Message-Passing Interface (MPI) parallel programs more difficult. In order to solve the problems, a new method of reducing communication coverage based on dominant relation was proposed. Firstly, based on the correspondence between communications and communication statements, the reduction problem of communications was converted into a reduction problem of communication statements. Then, the dominant relation of statements was used to solve the reduction set of communication statement set. Finally, the communications related to the reduction set were selected as the targets to be covered, so that the test data covering these targets covered all the communications. The proposed method was applied to 7 typical programs under test. Experimental results show that, compared with the test data generation method with all communication as coverage targets, the proposed method can reduce the generation time of test data by up to 95% without reducing the coverage rate of communications, indicating that the proposed method can improve the generation efficiency of communication coverage test data.

In order to solve the problem of rescuing the maximum number of trapped men in limited time after disaster, the robots were used to take place of rescue workers to rescue the survivors after disaster, and the robots rescue path planning method was studied by considering the situation that the trapped men's life strengths were uncertain. Firstly, considering that each target has life strength and the values of life strengths were different due to different factors, the value of life strength was set as interval number in general. Secondly, taking life strength constraint into account, the rescued worker number was treated as the objective function, which is an interval function related to life strength. Then the modified Particle Swarm Optimization (PSO) algorithm was used to solve the established objective function, the particle's code and decode method and the global best solution update strategy were introduced. Finally, the effectiveness of the proposed method was verified by simulations of different scenarios.