Evolutionary Generation of Test Suites for Multi-Path Coverage of MPI Programs With Non-Determinism

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
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nterface (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.