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Widening as Abstract Domain

机译:扩展为抽象域

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Verification using static analysis often hinges on precise numeric invariants. Numeric domains of infinite height can infer these invariants, but require wideningarrowing which complicates the fixpoint computation and is often too imprecise. As a consequence, several strategies have been proposed to prevent a precision loss during widening or to narrow in a smarter way. Most of these strategies are difficult to retrofit into an existing analysis as they either require a pre-analysis, an on-the-fly modification of the CFG, or modifications to the fixpoint algorithm. We propose to encode widening and its various refinements from the literature as cofibered abstract domains that wrap standard numeric domains, thereby providing a modular way to add numeric analysis to any static analysis, that is, without modifying the fixpoint engine. Since these domains cannot make any assumptions about the structure of the program, our approach is suitable to the analysis of executables, where the (potentially irreducible) CFG is re-constructed on-the-fly. Moreover, our domain-based approach not only mirrors the precision of more intrusive approaches in the literature but also requires fewer iterations to find a fixpoint of loops than many heuristics that merely aim for precision.
机译:使用静态分析的验证通常取决于精确的数字不变性。无限高的数值域可以推断出这些不变量,但需要加宽/变窄,这会使定点计算复杂化,并且往往过于不精确。结果,已经提出了几种策略来防止扩宽期间的精度损失或以更智能的方式缩小精度。这些策略中的大多数很难改型为现有分析,因为它们需要进行预分析,对CFG进行即时修改或对定点算法进行修改。我们建议将文献中的拓宽及其各种改进编码为包裹标准数值域的同纤抽象域,从而提供一种将数值分析添加到任何静态分析的模块化方法,也就是说,无需修改定点引擎。由于这些域无法对程序的结构进行任何假设,因此我们的方法适用于对可执行文件进行分析,在这种情况下,即时重构CFG(可能无法还原)。而且,我们的基于域的方法不仅反映了文献中更多侵入式方法的精度,而且与许多仅以精度为目标的启发式方法相比,只需较少的迭代次数即可找到循环的固定点。

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