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Abstract interpretation by dynamic partitioning

Part of: JFP Research Articles

Published online by Cambridge University Press:  07 November 2008

François Bourdoncle
François Bourdoncle
Affiliation:
DIGITAL Paris Research Laboratory, 85, avenue Victor Hugo, 92500 Rueil-Malmaison, France Centre de Mathématiques Appliquées, Ecole des Mines de Paris, Sophia-Antipolis, 06560 Valbonne, France(e-mail: bourdoncle@prl.dec.com)
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Abstract

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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 runtime. 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 non-unique 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.

Type
Articles
Information
Journal of Functional Programming , Volume 2 , Issue 4 , October 1992 , pp. 407 - 435
Copyright
Copyright © Cambridge University Press 1992

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