CAREER: Static Analysis for Dynamic Languages

职业：动态语言的静态分析

• 批准号: 1350690
• 负责人: Ben Hardekopf
• 金额: $ 51.29万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350690&HistoricalAwards=false
• 关键词: CAREER; Static; Analysis; Dynamic; Languages

Dynamic languages such as JavaScript, Python, and Ruby are ubiquitous; they are employed in critical infrastructure on clients, servers, and desktops, from browsers to the operating systems. The security, maintainability, correctness, and performance of programs written in these languages is becoming increasingly important. Static analysis is a valuable tool to help achieve these goals. However, static analysis of dynamic languages is a significant challenge due to their inherent dynamism, complicated semantics, and obscure corner cases, all of which conspire to make most static analyses incorrect, intractable, or both. The objective of this research is to extend the state of the art in terms of correctness, precision, and performance for static analysis of dynamic languages.The intellectual merits of this research involve novel contributions in three areas: (1) ensuring the correctness of a static analysis via analysis testing, enabling high correctness assurance with low cost; (2) exploring the tradeoffs between analysis performance and precision by a novel framework for tunable analysis sensitivity, enabling empirical investigation into the most productive forms of static analysis for dynamic languages; and (3) refinement and parallelization strategies to help optimize analysis performance and precision, combatting the challenges of dynamic languages outlined above. Beyond the technical contributions, the broader impacts of this research are in education, the research community, and in industry. The insights and artifacts resulting from this research will be used to create novel curricula for both undergraduate and graduate courses. All of the research artifacts will be made publicly available under an open license, and the PI will use his connections to industrial research labs to facilitate technology transfer to industry.

JavaScript、Python 和 Ruby 等动态语言无处不在；它们应用于客户端、服务器和桌面（从浏览器到操作系统）的关键基础设施中。用这些语言编写的程序的安全性、可维护性、正确性和性能变得越来越重要。静态分析是帮助实现这些目标的宝贵工具。然而，动态语言的静态分析是一个重大挑战，因为它们固有的动态性、复杂的语义和模糊的极端情况，所有这些共同导致大多数静态分析不正确、棘手或两者兼而有之。本研究的目的是扩展动态语言静态分析的正确性、精确性和性能方面的最新技术。本研究的智力优点涉及三个领域的新颖贡献：（1）确保通过分析测试进行静态分析，以低成本实现高正确性保证； (2) 通过可调分析灵敏度的新颖框架探索分析性能和精度之间的权衡，从而能够对动态语言静态分析最有效的形式进行实证研究； (3) 细化和并行化策略，帮助优化分析性能和精度，应对上述动态语言的挑战。除了技术贡献之外，这项研究还对教育、研究界和工业界产生更广泛的影响。这项研究产生的见解和成果将用于为本科生和研究生课程创建新颖的课程。所有研究成果都将在开放许可下公开提供，PI 将利用他与工业研究实验室的联系来促进技术向工业的转移。