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Deep Learning For Source Code Modeling And Generation: Models, Applications And Challenges

Triet H. M. Le, Hao Chen, M. Ali Babar . ACM Computing Surveys 2020 – 102 citations

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Applications Compositional Generalization Content Enrichment Image Text Integration Interactive Environments Interdisciplinary Approaches Llm For Code Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Survey Paper Tools Variational Autoencoders Visual Question Answering

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.

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