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.

中文翻译：

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用于源代码建模和生成的深度学习

用于自然语言处理的深度学习 (DL) 技术发展迅速。最近，DL 在语言建模、机器翻译和段落理解方面的进步如此突出，以至于 DL 在软件工程中的潜力不可忽视，尤其是在程序学习领域。为了促进 DL 在该领域的进一步研究和应用，我们提供了一个全面的回顾来分类和研究用于源代码建模和生成的现有 DL 方法。为了解决传统源代码模型的局限性，我们在编码器-解码器框架下制定了常见的程序学习任务。之后，我们介绍了适合解决此类问题的最新 DL 机制。然后，