EAGER: Exploiting the Naturalness of Software

对软件代码的研究揭示了一个令人惊讶的结果：软件代码可能和自然语言本身（例如英语）一样（如果不是更）“自然”，因为代码是高度可预测和重复的；统计自然语言技术可以很好地应用于一些软件工程任务。例如，n -gram在建议和完成代码中的任务时可能非常有效。这些证据支持进一步探索统计NLP技术和工具在软件开发活动和过程中的适用性。该项目探索了为各种代码级软件工程功能建立科学基础和工具的可行性，包括自然语言总结、代码检索、软件问答、自动代码完成和残疾开发人员辅助工具，以支持软件工程，不仅形成了NLP进一步研究的一个新的重要领域，而且还形成了一个全新的软件开发方法。

A study of software code has revealed a surprising result: that software code may be just as (if not more) "natural" as natural language itself (e.g., English) in that code is highly predictable and repetitive; statistical natural language techniques may be applied quite competently for some software engineering tasks. For example, N-grams may be quite effective at suggestion and completion tasks in code. The evidence supports further exploration of the applicability of statistical NLP techniques and tools to software development activities and processes. The project explores the feasibility of establishing a scientific basis and tools for a variety of code-level software engineering functions -- including natural language summarization, code retrieval, software question answering, automated code completion, and assistive tools for disabled developers to support software engineering, forming not only a new and important domain for further research in NLP, but also a totally new approach to software development.