CCRI: ENS: Collaborative Research: Enabling Automated Language Support for the srcML Infrastructure

CCRI：ENS：协作研究：为 srcML 基础设施提供自动化语言支持

• 批准号: 2016465
• 负责人: Jonathan Maletic
• 金额: $ 39.78万
• 依托单位: Kent State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016465&HistoricalAwards=false
• 关键词: CCRI; ENS; Collaborative; Research; Enabling

srcML is an infrastructure for the exploration, analysis, and manipulation of source code. The infrastructure currently supports the translation of C, C++, C#, and Java source code to the srcML format. The srcML format contains all the original source code plus grammatical information from the specific programming language used. The self-contained parsing technology is very robust and highly scalable both in time and memory. Researchers and practitioners are able to construct source code analysis tools very easily by using the infrastructure. srcML has been leveraged to construct tools for such things as software quality assessment, error detection, and security risk assessment of software systems. The freely available srcML parser is used by a wide variety of researchers and practitioners in the fields of software engineering and programming languages, as well as computer science education. srcML has been used in the dissertation/thesis research of dozens (and counting) computer science graduate students at a number of institutions across the country. The proposed enhancements to the srcML infrastructure will extend the parsing and markup to a broad variety of widely used programming languages for example, Python, JavaScript, Go, Ruby, etc. The proposed enhancement to the srcML infrastructure will drastically reduce the entry cost for individuals to conduct research by enabling them to explore, analyze, and manipulate software in an easy and flexible manner, thus allowing them more time to pursue novel and transformative research on software, software engineering, and programming languages. Furthermore, it provides practical tools for engineers to improve the quality and lower the cost of software applications we all use daily.The proposed enhancements to the srcML infrastructure extend it to a wider variety of popular programming languages. These extensions will be accomplished by developing a parser generator for the srcML format. The input is a programming language grammar, and the output is a parser that takes source code in that programming language and inserts the srcML markup into the code. This basic approach is similar to those taken by parser generators such as yacc or ANTLR. This grammar-based approach will significantly broaden the audience for the srcML infrastructure. It will not only allow for new languages to be easily added but also the ability to support dialects, legacy languages, and domain-specific languages. Many current research tools and techniques do not work on mixed/multi-language systems or are not validated on such real-world systems due to the lack of tools that can be applied. The enhancement to srcML will represent one of the only mixed language, source code analysis tool that is open source and freely available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

srcML是用于探索、分析和操作源代码的基础设施。该基础设施目前支持将C、c++、c#和Java源代码转换为srcML格式。srcML格式包含所有原始源代码以及所使用的特定编程语言的语法信息。自包含解析技术非常健壮，并且在时间和内存方面具有高度可伸缩性。研究人员和实践者能够通过使用基础结构非常容易地构建源代码分析工具。srcML已被用于构建诸如软件质量评估、错误检测和软件系统安全风险评估等工具。免费提供的srcML解析器被软件工程和编程语言领域以及计算机科学教育领域的各种研究人员和实践者广泛使用。srcML已在全国许多机构的数十名计算机科学研究生的学位论文/论文研究中使用。对srcML基础结构提出的增强将把解析和标记扩展到各种广泛使用的编程语言，例如Python、JavaScript、Go、Ruby等。提出的对srcML基础结构的增强将极大地降低个人进行研究的入门成本，使他们能够以一种简单而灵活的方式探索、分析和操作软件，从而使他们有更多的时间从事软件、软件工程和编程语言方面的新颖和变革性研究。此外，它为工程师提供了实用的工具，以提高我们日常使用的软件应用程序的质量和降低成本。对srcML基础结构提出的增强将其扩展到更广泛的流行编程语言。这些扩展将通过为srcML格式开发解析器生成器来完成。输入是一种编程语言语法，输出是一个解析器，它接受该编程语言的源代码，并将srcML标记插入到代码中。这种基本方法类似于解析器生成器（如yacc或ANTLR）所采用的方法。这种基于语法的方法将极大地扩大srcML基础结构的受众。它不仅允许轻松添加新语言，还支持方言、遗留语言和特定于领域的语言。由于缺乏可应用的工具，许多当前的研究工具和技术不能在混合/多语言系统上工作，或者不能在这样的实际系统上验证。对srcML的增强将代表一个唯一的混合语言，源代码分析工具，是开放源代码和免费提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。