Collaborative Research: SHF: Small: A General Framework for Responsive Static Analysis

社会越来越依赖软件的可靠性和安全性。抽象解释是一种成熟的方法，用于证明软件没有特定类别的错误。然而，对于工业规模的软件，标准的抽象解释技术可能需要几个小时才能完成，这使得它们很难集成到现代软件开发实践中。这个项目开发了一个响应性静态分析框架，它保留了抽象解释的力量，同时对常见用例运行得更快。该项目的新颖性是用于响应地运行抽象解释的新算法，相应的数学证明，这些算法产生期望的、正确的结果，以及算法的工作实现。该项目的影响是验证软件正确性的强大抽象解释技术的更高性能和适用性，这反过来将产生更可靠和安全的软件。该项目建立在最近开发的需求抽象解释框架基础上，这是一种需求驱动的增量分析方法，基于将分析计算和依赖具体化在图结构中。通过对这一方法的概括，该项目将扩展框架，以处理对过程调用的有效分析至关重要的成分分析，以及基于细化的分析，以使分析能够结合不同级别的精度和可伸缩性。这一通用框架将促进对从头开始的一致性的可证明保证，这是响应性分析的关键特性。该项目还将实施通用框架，并用具有挑战性的分析问题来实例化它，解决使框架实用的研究挑战。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。

Society increasingly relies on the reliability and security of software. Abstract interpretation is a well-established methodology for proving that software is free of certain classes of bugs. However, for industrial-scale software, standard abstract interpretation techniques may take hours to complete, making them difficult to integrate into modern software development practices. This project develops a framework for responsive static analysis, which retains the power of abstract interpretation while running much more quickly for common use cases. The project's novelties are new algorithms for running abstract interpretation responsively, corresponding mathematical proofs that these algorithms produce the desired, correct results, and working implementations of the algorithms. The project's impacts are greater performance and applicability of powerful abstract interpretation techniques for verifying software correctness, which in turn will yield more reliable and secure software.The project builds on a recently-developed framework for demanded abstract interpretation, a demand-driven and incremental analysis approach based on reifying analysis computations and dependencies in a graph structure. Via generalizations of this approach, this project will extend the framework to handle compositional analysis, essential for efficient analysis of procedure calls, and refinement-based analysis, to enable combining analyses with varying levels of precision and scalability. This generalized framework will facilitate provable guarantees of from-scratch consistency, a crucial property for responsive analysis. The project will also implement the generalized framework and instantiate it with challenging analysis problems, addressing research challenges in making the framework practical.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.