code recommendation systems have been widely used in helping developers implement unfamiliar programming *** existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy,howe...
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code recommendation systems have been widely used in helping developers implement unfamiliar programming *** existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy,however,they cannot recommend any code outside similar *** propose an approach to predict the functionality of incomplete programming code by using syntactical information,and providing a list of potential functionalities to guess what the developers want,in order to increase the diversity of *** this paper,we propose a deep learning model called ASTSDL,which uses a sequencebased representation of source code to predict *** extract syntactical information from the abstract syntax tree(AST) of the source code,apply a deep learning model to capture the syntactic and sequential information,and predict the functionality of the source code *** experimental results demonstrate that ASTSDL can effectively predict the functionality of incompletecode with an accuracy above 84% in the top-10 list,even if there is only half of the complete code.
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