EAGER: Exploiting the "Naturalness" of Software

EAGER：利用软件的“自然性”

• 批准号: 1247328
• 负责人: Mark Gabel
• 金额: $ 10万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247328&HistoricalAwards=false
• 关键词: EAGER; Exploiting; Naturalness; Software

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.

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一样（如果不是更自然的话）“自然”（例如，英语）中的代码是高度可预测和重复的;统计自然语言技术可以非常胜任地应用于某些软件工程任务。 例如，N-gram在代码中的建议和完成任务方面可能非常有效。 这些证据支持进一步探索统计NLP技术和工具对软件开发活动和过程的适用性。 该项目探讨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-包括自然语言摘要，代码检索，软件问题回答，自动代码完成，以及残疾开发人员支持软件工程的辅助工具，不仅形成了NLP进一步研究的新的重要领域，也是一种全新的软件开发方法。