Generalized Strong Preservation by Abstract Interpretation

Standard abstract model checking relies on abstract Kripke structures which approximate concrete models by gluing together indistinguishable states, namely by a partition of the concrete state space. Strong preservation for a specification language L encodes the equivalence of concrete and abstract model checking of formulas in L. We show how abstract interpretation can be used to design abstract models that are more general than abstract Kripke structures. Accordingly, strong preservation is generalized to abstract interpretation-based models and precisely related to the concept of completeness in abstract interpretation. The problem of minimally refining an abstract model in order to make it strongly preserving for some language L can be formulated as a minimal domain refinement in abstract interpretation in order to get completeness w.r.t. the logical/temporal operators of L. It turns out that this refined strongly preserving abstract model always exists and can be characterized as a greatest fixed point. As a consequence, some well-known behavioural equivalences, like bisimulation, simulation and stuttering, and their corresponding partition refinement algorithms can be elegantly characterized in abstract interpretation as completeness properties and refinements

Abstract Program Slicing: an Abstract Interpretation-based approach to Program Slicing

In the present paper we formally define the notion of abstract program slicing, a general form of program slicing where properties of data are considered instead of their exact value. This approach is applied to a language with numeric and reference values, and relies on the notion of abstract dependencies between program components (statements). The different forms of (backward) abstract slicing are added to an existing formal framework where traditional, non-abstract forms of slicing could be compared. The extended framework allows us to appreciate that abstract slicing is a generalization of traditional slicing, since traditional slicing (dealing with syntactic dependencies) is generalized by (semantic) non-abstract forms of slicing, which are actually equivalent to an abstract form where the identity abstraction is performed on data. Sound algorithms for computing abstract dependencies and a systematic characterization of program slices are provided, which rely on the notion of agreement between program states

Abstract Program Slicing: An Abstract Interpretation-Based Approach to Program Slicing

n the present article, we formally define the notion of abstract program slicing, a general form of program slicing where properties of data are considered instead of their exact value. This approach is applied to a language with numeric and reference values and relies on the notion of abstract dependencies between program statements. The different forms of (backward) abstract slicing are added to an existing formal framework where traditional, nonabstract forms of slicing could be compared. The extended framework allows us to appreciate that abstract slicing is a generalization of traditional slicing, since each form of traditional slicing (dealing with syntactic dependencies) is generalized by a semantic (nonabstract) form of slicing, which is actually equivalent to an abstract form where the identity abstraction is performed on data. Sound algorithms for computing abstract dependencies and a systematic characterization of program slices are provided, which rely on the notion of agreement between program states

Making abstract model checking strongly preserving

Usually, abstract model checking is not strongly preserving: it may well exist a temporal specification which is not valid on the abstract model but which is instead satisfied by the concrete model. Starting from the standard notion of bisimulation, we introduce a notion of completeness for abstract models: completeness together with a so-called partitioning property for abstract models implies strong preservation for the past \u3bc-calculus. Within a rigorous abstract interpretation framework, we show that the least refinement of a given abstract model, for a suitable ordering on abstract models, which is complete and partitioning always exists, and it can be constructively characterized as a greatest fixpoint. This provides a systematic methodology for minimally refining an abstract model checking in order to get strong preservation