Learning fast and precise numerical analysis

Jingxuan He, Gagandeep Singh, Markus Püschel, Martin T. Vechev
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引用次数: 17

Abstract

Numerical abstract domains are a key component of modern static analyzers. Despite recent advances, precise analysis with highly expressive domains remains too costly for many real-world programs. To address this challenge, we introduce a new data-driven method, called LAIT, that produces a faster and more scalable numerical analysis without significant loss of precision. Our approach is based on the key insight that sequences of abstract elements produced by the analyzer contain redundancy which can be exploited to increase performance without compromising precision significantly. Concretely, we present an iterative learning algorithm that learns a neural policy that identifies and removes redundant constraints at various points in the sequence. We believe that our method is generic and can be applied to various numerical domains. We instantiate LAIT for the widely used Polyhedra and Octagon domains. Our evaluation of LAIT on a range of real-world applications with both domains shows that while the approach is designed to be generic, it is orders of magnitude faster on the most costly benchmarks than a state-of-the-art numerical library while maintaining close-to-original analysis precision. Further, LAIT outperforms hand-crafted heuristics and a domain-specific learning approach in terms of both precision and speed.
学习快速和精确的数值分析
数值抽象域是现代静态分析器的重要组成部分。尽管最近取得了一些进展,但对于许多现实世界的程序来说,对高表达域进行精确分析的成本仍然太高。为了应对这一挑战,我们引入了一种新的数据驱动方法,称为LAIT,它可以产生更快、更可扩展的数值分析,而不会显著降低精度。我们的方法是基于关键的洞察力,即由分析仪产生的抽象元素序列包含冗余,可以利用它来提高性能,而不会显著影响精度。具体而言,我们提出了一种迭代学习算法,该算法学习一种神经策略,该策略可以识别和消除序列中不同点的冗余约束。我们相信我们的方法是通用的,可以应用于各种数值领域。我们对广泛使用的多面体和八边形域实例化了LAIT。我们对LAIT在两个领域的一系列实际应用中的评估表明,虽然该方法被设计为通用的,但在最昂贵的基准测试中,它比最先进的数值库要快几个数量级,同时保持接近原始的分析精度。此外,在精度和速度方面,LAIT优于手工制作的启发式和特定领域的学习方法。
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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