Fault Localization of Statement Coverage Based on DNN
Ling Peng, Chunqiao Mi · 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA) · 2021
In view of the inconvenience of parameter setting in the existing fault location method based on deep neural network, this paper combines the global random search ability of Genetic Algorithm, L2 regularization to prevent model overfitting and deep neural network to learn complex nonlinear ability, a fault location algorithm based on G-DNN is proposed. The optimal number of hidden layer neurons, learning rate, training period and L2 regularization coefficient of deep neural network are obtained by Genetic Algorithm; Input the statement coverage information and state values into the deep neural network to calculate the suspiciousness value of each executable statement; Locate defects from high to low according to the suspiciousness values. The Siemens Suite is used as the experimental sample, and the G-DNN is compared with the five defect location algorithms. The results show that the defect of the G-DNN can locate defects more accurately, and the positioning efficiency is improved.