On the Complexity of Flow-Sensitive Dataflow Analyses
Robert Muth Saumya Debray
Department of Computer Science
University of Arizona
Tucson, AZ 85721, U.S.A.
Abstract
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.