Static Evaluation Algorithm of Programming Problems Based on AST
Aiming at the problems of heavy manual reviewing labor and low running review security in the process of tradition-al computer program review,this paper proposes an algorithm for static review of programming questions based on AST.First,it seg-ments the answer source code by sentence and generates the AST of the corresponding sentence,and introduces the pre-training model based on the bidirectional encoder to realize node vectorization.Then,it extracts the vocabulary through the specific convolu-tional neural network and bidirectional tree convolutional neural network feature and structural features are combined to achieve fea-ture integration.Finally,the fusion features are input into the deep neural network for multi-category review.Experimental results show that compared with traditional static analysis algorithms,this algorithm has a 5.1%improvement in review accuracy,and it is a feasible method for reviewing programming questions.
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