Large-scale configurable static analysis

M. Naik
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引用次数: 1

Abstract

Program analyses developed over the last three decades have demonstrated the ability to prove non-trivial properties of real-world programs. This ability in turn has applications to emerging software challenges in security, software-defined networking, cyber-physical systems, and beyond. The diversity of such applications necessitates adapting the underlying program analyses to client needs, in aspects of scalability, applicability, and accuracy. Today's program analyses, however, do not provide useful tuning knobs. This talk presents a general computer-assisted approach to effectively adapt program analyses to diverse clients. The approach has three key ingredients. First, it poses optimization problems that expose a large set of choices to adapt various aspects of an analysis, such as its cost, the accuracy of its result, and the assumptions it makes about missing information. Second, it solves those optimization problems by new search algorithms that efficiently navigate large search spaces, reason in the presence of noise, interact with users, and learn across programs. Third, it comprises a program analysis platform that facilitates users to specify and compose analyses, enables search algorithms to reason about analyses, and allows using large-scale computing resources to parallelize analyses.
大规模可配置静态分析
在过去三十年中开发的程序分析已经证明了证明真实世界程序的非平凡属性的能力。这种能力反过来又应用于安全、软件定义网络、网络物理系统等新兴软件挑战。这类应用程序的多样性要求在可伸缩性、适用性和准确性方面调整底层程序分析以满足客户需求。然而,今天的程序分析并没有提供有用的调优旋钮。本讲座介绍了一种通用的计算机辅助方法,以有效地使程序分析适应不同的客户。这种方法有三个关键要素。首先,它提出了优化问题,暴露了大量的选择,以适应分析的各个方面,例如成本、结果的准确性以及对缺失信息的假设。其次,它通过新的搜索算法解决了这些优化问题,这些算法可以有效地导航大型搜索空间,在存在噪声的情况下进行推理,与用户交互,并跨程序学习。第三,它包含一个程序分析平台,方便用户指定和编写分析,使搜索算法能够对分析进行推理,并允许使用大规模计算资源并行化分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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