ASTSDL: predicting the functionality of incomplete programming code via an AST-sequence-based deep learning model

Code recommendation systems have been widely used in helping developers implement unfamiliar programming tasks. Many existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy, however, they cannot recommend any code outside similar ones. We propose an ap...

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Bibliographic Details

Published in:	Science China. Information sciences Vol. 67; no. 1; p. 112105
Main Authors:	Yu, Yaoshen, Huang, Zhiqiu, Shen, Guohua, Li, Weiwei, Shao, Yichao
Format:	Journal Article
Language:	English
Published:	Beijing Science China Press 01.01.2024 Springer Nature B.V
Subjects:	Computer Science Deep learning Information Systems and Communication Service Programming Recommender systems Research Paper Search engines Source code syntactical information deep learning algorithm functionality prediction incomplete programming code code representation model
ISSN:	1674-733X, 1869-1919
Online Access:	Get full text
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