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Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges by Triet H. M. Le; Hao Chen; Muhammad Ali Babar is a Computer Science article available to read on EtoBox.

What is Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges about?

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation, and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.

Who reads Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges?

Author

Triet H. M. Le; Hao Chen; Muhammad Ali Babar

Publisher

Association for Computing Machinery (ACM)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)