The invention discloses a
convolutional neural network-oriented
mutation coverage test method and a computer storage medium, and the method comprises the following steps: 1) setting n
mutation operators, and respectively injecting the n
mutation operators into a to-be-tested
convolutional neural network program P to obtain a mutation program set {P1, P2, P3,and the like, Pn}; 2) training the variation program set {P1, P2, P3, and the like, Pn} by using a training
data set D to obtain a variation
model set {M1, M2, M3, and the like, Mn}; 3) testing the original model M and the variation
model set {M1, M2, M3, and the like, Mn} by using a
test data set T; and 4) comparing the test accuracy of all the models, and selecting the model with the highest accuracy. According to the invention, the defect that the traditional test method is difficult to ensure the test sufficiency of the
convolutional neural network application program is solved. The test sufficiency of the convolutional neural network can be effectively improved, the method is more effective in neural
network model testing, the local optimal model can be found out according to the test accuracy, and the quality and safety ofthe convolutional neural
network application program are effectively guaranteed.