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Optimal Matching for Sharing and Linearity Analysis

Published online by Cambridge University Press:  22 November 2024

GIANLUCA AMATO
Affiliation:
University of Chieti–Pescara, Pescara, Italy (e-mails: gianluca.amato@unich.it, francesca.scozzari@unich.it)
FRANCESCA SCOZZARI
Affiliation:
University of Chieti–Pescara, Pescara, Italy (e-mails: gianluca.amato@unich.it, francesca.scozzari@unich.it)
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Abstract

Static analysis of logic programs by abstract interpretation requires designing abstract operators which mimic the concrete ones, such as unification, renaming, and projection. In the case of goal-driven analysis, where goal-dependent semantics are used, we also need a backward-unification operator, typically implemented through matching. In this paper, we study the problem of deriving optimal abstract matching operators for sharing and linearity properties. We provide an optimal operator for matching in the domain $\mathtt{ShLin}^{\omega }$, which can be easily instantiated to derive optimal operators for the domains $\mathtt{ShLin}^2$ by Andy King and the reduced product $\mathtt{Sharing} \times \mathtt{Lin}$.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press
Figure 0

Fig. 1. The role of forward and backward unification in goal-dependent analysis.