Composing dataflow analyses and transformations

14 years 4 months ago

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Dataflow analyses can have mutually beneficial interactions. Previous efforts to exploit these interactions have either (1) iteratively performed each individual analysis until no further improvements are discovered or (2) developed "superanalyses" that manually combine conceptually separate analyses. We have devised a new approach that allows analyses to be defined independently while still enabling them to be combined automatically and profitably. Our approach avoids the loss of precision associated with iterating individual analyses and the implementation difficulties of manually writing a super-analysis. The key to our approach is a novel method of implicit communication between the individual components of a super-analysis based on graph transformations. In this paper, we precisely define our approach; we demonstrate that it is sound and it terminates; finally we give experimental results showing that in practice (1) our framework produces results at least as precise as...

Sorin Lerner, David Grove, Craig Chambers

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