带格Datalog的增量全程序分析

Tamás Szabó, Sebastian Erdweg, Gábor Bergmann
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引用次数: 12

摘要

增量静态分析及时提供与代码更改大小成比例的最新分析结果,而不是整个代码库。这保证了在ide中以及检入提交时向程序员提供快速反馈。然而,现有的增量分析框架无法实现基于整个程序格的数据流分析的承诺。特别是,以前基于datalog的框架仅对过程内分析产生良好的增量性能。在本文中,我们首先提出了一种方法来实证检验计算是否适用于增量化。使用这种方法,我们发现增量全程序分析是可能的。其次,我们提出了一种新的增量数据表求解器LADDDER,以消除先前基于数据表的分析框架的缺点。我们的Datalog求解器使用一种非标准的聚合语义,这允许我们放松对分析的单调性要求,并大大提高了格聚合器的性能。我们对真实Java代码的评估证实,LADDDER以毫秒为单位提供了最新的指向点、恒定传播和间隔信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental whole-program analysis in Datalog with lattices
Incremental static analyses provide up-to-date analysis results in time proportional to the size of a code change, not the entire code base. This promises fast feedback to programmers in IDEs and when checking in commits. However, existing incremental analysis frameworks fail to deliver on this promise for whole-program lattice-based data-flow analyses. In particular, prior Datalog-based frameworks yield good incremental performance only for intra-procedural analyses. In this paper, we first present a methodology to empirically test if a computation is amenable to incrementalization. Using this methodology, we find that incremental whole-program analysis may be possible. Second, we present a new incremental Datalog solver called LADDDER to eliminate the shortcomings of prior Datalog-based analysis frameworks. Our Datalog solver uses a non-standard aggregation semantics which allows us to loosen monotonicity requirements on analyses and to improve the performance of lattice aggregators considerably. Our evaluation on real-world Java code confirms that LADDDER provides up-to-date points-to, constant propagation, and interval information in milliseconds.
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