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In order to find out the similar test cases in the software test case set and realize the optimal simplification of test cases, an adaptive Gaussian mixture model is proposed. The proposed model uses K-means to initialize EM, adaptively determines the number of clusters. In this process, the clustering results can be evaluated. At the same time, all the parameters of the Gaussian mixture model are given. These parameters are used as the parameters of each cluster for a new round of iterative calculation, and the final results tend to be the optimal solution. The experimental results show that, compared with the existing Gaussian mixture model and fuzzy K-means clustering model, the adaptive Gaussian mixture model algorithm proposed in this paper can minimize the software test case set, and the reduced cases cover a wider range. The software error rate is higher, and it has good adaptability to the variety of software test case set.