When a large number of target paths in a sequential program need to be covered, we can divide similar target paths into the same group, and generate a test suite covering the same group of target paths at the same time, so as to reduce the testing cost. However, different communication edges may be run under a same test input when executing a Message-PassingInterface (MPI) program with non-determinism, which cause different code fragments may be traversed, indicating the difficulty of generating a test suite to cover each group of target paths. This paper proposes an approach to evolutionary generation of test suites for multi-path coverage of MPI programs with non-determinism, which can significantly reduce the testing cost and difficulty. We first design an indicator for evaluating each traversal set of communication edges, which is used to form a relation matrix between each target path and each traversal set of communication edges, so as to divide all the target paths into a certain amount of groups. Then, we construct an optimization model for test suite generation associated with each group. Finally, an evolutionary optimization algorithm is extended to solve each model, and used to generate a test suite covering each group of target paths. The proposed approach is utilized and compared with several state-of-the-art approaches to seven benchmark MPI programs, as well as the experimental results illustrate that the proposed approach can efficiently generate a test suite, thus supporting the superiority of the proposed approach.

Compared with traditional structural test criteria, test data generated based on mutation testing are proved more effective at detecting faults. However, not all test data have the same potence in detecting software faults. If test data are prioritized while generating for mutation testing, the defect detectability of the test suite can be further strengthened. In view of this, we propose a method of test data generation for weak mutation testing via sorting mutant branches based on their dominance degrees. First, the problem of weak mutation testing is transformed into that of covering mutant branches for a transformed program. Then, the dominance relation of mutant branches in the transformed program is analyzed to obtain the non-dominated mutant branches and their dominance degrees. Following that, we prioritize all non-dominated mutant branches in descending order by virtue of their dominance degrees. Finally, the test data are generated in an orderly manner by selecting the mutant branches sequentially. The experimental results on 15 programs show that compared with other methods, the proposed test data generation method can not only improve the error detectability of the test suite, but also has higher efficiency.