SHF: Large: Collaborative Research: Exploiting the Naturalness of Software

SHF：大型：协作研究：利用软件的自然性

• 批准号: 1723215
• 负责人: Tien Nguyen
• 金额: $ 26.07万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723215&HistoricalAwards=false
• 关键词: SHF; Large; Collaborative; Research; Exploiting

This inter-disciplinary project has its roots in Natural Language (NL) processing. Languages such as English allow intricate, lovely and complex constructions; yet, everyday, ``natural? speech and writing is simple, prosaic, and repetitive, and thus amenable to statistical modeling. Once large NL corpora became available, computational muscle and algorithmic insight led to rapid advances in the statistical modeling of natural utterances, and revolutionized tasks such as translation, speech recognition, text summarization, etc. While programming languages, like NL, are flexible and powerful, in theory allowing a great variety of complex programs to be written, we find that ``natural? programs that people actually write are regular, repetitive and predictable. This project will use statistical models to capture and exploit this regularity to create a new generation of software engineering tools to achieve transformative improvements in software quality and productivity. The project will exploit language modeling techniques to capture the regularity in natural programs at the lexical, syntactic, and semantic levels. Statistical modeling will also be used to capture alignment regularities in ``bilingual? corpora such as code with comments, or explanatory text (e.g., Stackoverflow) and in systems developed on two platforms such as Java and C#. These statistical models will help drive novel, data-driven approaches for applications such as code suggestion and completion, and assistive devices for programmers with movement or visual challenges. These models will also be exploited to correct simple errors in programs. Models of bilingual data will used to build code summarization and code retrieval tools, as well as tools for porting across platforms. Finally, this project will create a large, curated corpus of software, and code analysis products, as well as a corpus of alignments within software bilingual corpora, to help create and nurture a research community in this area.

这个跨学科的项目起源于自然语言（NL）处理。诸如英语之类的语言允许复杂，可爱而复杂的结构；但是，每天，``自然吗？语音和写作简单，平淡且重复，因此可以适合统计建模。一旦大型NL Corpora获得可用，计算肌肉和算法洞察力就会导致自然话语的统计建模迅速发展，并彻底改变了诸如翻译，语音识别，文本摘要等的任务，而NL（如NL）具有灵活性和强大的作用，在理论上允许写出各种复杂的程序，我们会发现自然的自然风格？人们实际写作的程序是常规的，重复的和可预测的。该项目将使用统计模型来捕获和利用这种规律性，以创建新一代的软件工程工具，以实现软件质量和生产率的变革性提高。 该项目将利用语言建模技术来捕获词汇，句法和语义级别的自然程序的规律性。统计建模还将用于捕获“双语？诸如带有评论的代码或解释性文本（例如stackoverflow）以及在两个平台上开发的系统（例如Java和C＃）中的CORDA。 这些统计模型将有助于推动针对代码建议和完成等应用程序的新颖，数据驱动的方法，以及针对具有运动或视觉挑战的程序员的辅助设备。这些模型还将被利用以纠正程序中的简单错误。双语数据模型将用于构建代码摘要和代码检索工具，以及用于跨平台移植的工具。最后，该项目将创建一个大型，精心策划的软件和代码分析产品，以及软件双语语料库中的一系列对齐，以帮助创建和培养该领域的研究社区。