Abstract

The essential part of abstract interpretation is to build a machine-representable abstract domain expressing interesting properties about the possible states reached by a program at run-time. Many techniques have been developed which assume that one knows in advance the class of properties that are of interest. There are cases however when there are no a priori indications about the best abstract properties to use. We introduce a new framework that enables nonunique representations of abstract program properties to be used, and expose a method, called dynamic partitioning, that allows the dynamic determination of interesting abstract domains using data structures built over simpler domains. Finally, we show how dynamic partitioning can be used to compute non-trivial approximations of functions over infinite domains and give an application to the computation of minimal function graphs.

Citations