面向大代码和自然性的机器学习研究综述
A Survey of Machine Learning for Big Code and Naturalness
摘要：
在机器学习、程序设计语言和软件工程的交叉点上的研究最近在提出利用代码丰富模式的源代码的可学习概率模型方面迈出了重要的一步。在这篇文章中，我们调查这项工作。我们对比程序设计语言和自然语言，并讨论这些相似性和差异如何驱动概率模型的设计。我们提出了一个分类的基础上，每个模型的基本设计原则，并使用它来浏览文献。然后，我们回顾了研究人员如何将这些模型应用于应用领域，并讨论了跨领域和特定应用的挑战和机遇。
英文摘要：
Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
