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

SrcML是探索、分析和操纵源代码的基础设施。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。自包含的解析技术在时间和内存上都非常健壮和高度可伸缩。研究人员和实践者能够通过使用该基础设施非常容易地构建源代码分析工具。SrcML已被用于构建工具，用于软件质量评估、错误检测和软件系统的安全风险评估。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从业者使用。SrCML已经被用于全国多个机构的数十名(并且还在增加)计算机科学研究生的学位论文/论文研究中。对srcML基础设施的拟议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。拟议的对srcML基础设施的增强将使个人能够以轻松灵活的方式探索、分析和操纵软件，从而极大地降低个人进行研究的入门成本，从而使他们有更多时间在软件、软件工程和编程语言方面进行新颖和变革性的研究。此外，它还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基础设施的拟议增强将其扩展到更广泛的流行编程语言。这些扩展将通过开发srcML格式的解析器生成器来实现。输入是编程语言语法，输出是解析器，该解析器获取该编程语言的源代码并将srcML标记插入代码中。这种基本方法类似于yacc或ANTLR等解析器生成器所采用的方法。这种基于语法的方法将极大地扩大srcML基础设施的受众。它不仅允许轻松添加新语言，而且还能够支持方言、传统语言和领域特定语言。目前的许多研究工具和技术在混合/多语言系统上不起作用，或者由于缺乏可应用的工具而没有在这样的真实世界系统上得到验证。对srcML的增强将代表着仅有的开源和免费获得的混合语言、源代码分析工具之一。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.