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On the Complexity of Flow-Sensitive Dataflow Analyses

机译:流量敏感数据流分析的复杂性

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This paper attempts to address the question of why certain dataflow analysis problems can be solved efficiently, but not others. We focus on flow-sensitive analyses, and give a simple and general result that shows that analyses that require the use of relational attributes for precision must be PSPACE-hard in general. We then show that if the language constructs are slightly strengthened to allow a computation to maintain a very limited summary of what happens along an execution path, inter-procedural analyses become EXPTIME-hard. We discuss applications of our results to a variety of analyses discussed in the literature. Our work elucidates the reasons behind the complexity results given by a number of authors, improves on a number of such complexity results, and exposes conceptual commonalities underlying such results that are not readily apparent otherwise.
机译:本文试图解决为什么某些数据流分析问题可以有效解决而其他问题无法解决的问题。我们关注于流量敏感的分析,并给出了一个简单而通用的结果,该结果表明,要求使用关系属性以提高精度的分析通常必须是PSPACE-hard。然后,我们表明,如果略微增强语言结构以允许计算维护沿执行路径发生的情况的非常有限的摘要,则过程间分析将变得难以使用EXPTIME。我们讨论了我们的结果在文献中讨论的各种分析中的应用。我们的工作阐明了许多作者给出的复杂性结果背后的原因,对许多此类复杂性结果进行了改进,并揭示了这种结果背后的概念共性,而这些共性在其他情况下并不容易理解。

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