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Natural-semantics-based abstract interpretation (preliminary version)

  • David A. Schmidt
Invited Talks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 983)

Abstract

The original formulation of abstract interpretation (a.i.) [5] demonstrated clearly that a.i. is a formal-semantics-based methodology for deriving a provably correct, convergent, canonical iterative data flow analysis from a standard semantics of a programming language. But subsequent research in a.i. has obscured the methodology of the topic. For example, the recent slew of papers on closures analysis [2, 3, 17, 18, 21, 37, 39, 40, 41, 42, 43] mix implementation optimizations with specifications and leave unclear exactly what closures analysis is. In this paper, we reexamine the principles of a.i. and reformulate the topic on a foundation of coinductively defined natural semantics. We aim to demonstrate that the intensional and compositional aspects of natural semantics make it an ideal vehicle for formulating abstract interpretations of problems while preserving the essential characteristics of the subject.

Keywords

Closure Analysis Abstract Interpretation Denotational Semantic Galois Connection Standard Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • David A. Schmidt
    • 1
  1. 1.Department of Computing and Information SciencesKansas State UniversityManhattanUSA

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