论文标题
源代码建模和发电的深度学习：模型，应用和挑战
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
论文作者
论文摘要
自然语言处理的深度学习（DL）技术的发展非常快。最近，DL在语言建模，机器翻译和段落理解方面的进步非常突出，以至于软件工程中DL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，我们提供了全面的审查，以对现有的DL方法进行分类和调查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，我们在编码器框架下制定了通用程序学习任务。之后，我们介绍了最新的DL机制来解决此类问题。然后，我们介绍了最先进的实践，并通过对从业者和研究人员的一些建议讨论了他们的挑战。
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.
