Informative Information for the Uninformed  


Generalizing Data Flow Information
August, 2007 skape mmiller@hick.org
Abstract:
Generalizing information is a common method of reducing the quantity of data that must be considered during analysis. This fact has been plainly illustrated in relation to static data flow analysis where previous research has described algorithms that can be used to generalize data flow information. These generalizations have helped support more optimal data flow analysis in certain situations. In the same vein, this paper describes a process that can be employed to generalize and persist data flow information along multiple generalization tiers. Each generalization tier is meant to describe the data flow behaviors of a conceptual software element such as an instruction, a basic block, a procedure, a data type, and so on. This process makes use of algorithms described in previous literature to support the generalization of data flow information. To illustrate the usefulness of the generalization process, this paper also presents an algorithm that can be used to determine reachability at each generalization tier. The algorithm determines reachability starting from the least specific generalization tier and uses the set of reachable paths found to progressively qualify data flow information for each successive generalization tier. This helps to constrain the amount of data flow information that must be considered to a minimal subset.
