Demand-driven Alias Analysis : Formalizing Bidirectional Analyses for Soundness and Precision
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A demand-driven approach to program analysis have been viewed as efficient algorithms to compute only the information required to serve a target demand. In contrast, an exhaustive approach computes all information in anticipation of it being used later. However, for a given set of demands, demand-driven methods are believed to compute the same information that would be computed by the corresponding exhaustive methods. We investigate the precision and bidirectional nature of demand-driven methods and show that: (a) demand-driven methods can be formalized inherently as bidirectional data flow analysis because the demands are propagated against the control flow and the information to satisfy the demands is propagated along the control flow. We extend the formalization of the Meet Over Paths solution to bidirectional flows. This formalization helps us to prove the soundness and precision of our analysis, and (b) since a demand-driven method computes only the required information to meet a demand, it should be able to reduce the imprecision caused by data abstractions and hence should be more precise than an exhaustive method. We show that while this is indeed the case with Java, for C/C++, the precision critically hinges on how indirect assignments are handled. We use this insight and propose a demand-driven alias analysis that is more precise than an exhaustive analysis for C/C++ too. We have chosen devirtualization as an application. Our measurements show that our method is not only more efficient but more precise than the existing demand-driven method, as well as the corresponding exhaustive method.
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