Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
This is an incremental review paper that synthesizes existing research to guide practitioners and researchers in applying deep learning to software engineering tasks like program learning.
The paper provides a comprehensive review of deep learning methods for source code modeling and generation, categorizing existing approaches and formulating program learning tasks under an encoder-decoder framework to address limitations of traditional models.
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.