Program Understanding as Constraint Satisfaction
Steven Woods and
Qiang Yang.
Abstract
The process of understanding a source code in a high-level programming
language involves complex computation. Given a piece of legacy code and a
library of program plan templates, understanding the code corresponds to
building mappings from parts of the source code to particular program plans.
These mappings could be used to assist an expert in reverse
engineering legacy code, to facilitate software reuse, or to
assist in the translation of the source into another
programming language.
In this paper we present a model of program understanding using constraint
satisfaction. Within this model we intelligently compose a partial global
picture of the source program code by transforming knowledge about the problem
domain and the program itself into sets of constraints. We then systematically
study different search algorithms and empirically evaluate their performance.
One advantage of the constraint satisfaction model is its generality; many
previous attempts in program understanding could now be cast under the same
spectrum of heuristics, and thus be readily compared. Another advantage is
the improvement in search efficiency using various heuristic techniques in
constraint satisfaction.
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Copyright 1995 IEEE. Published in the Proceedings of the IEEE Seventh
International Workshop on Computer-Aided Software Engineering
(CASE-7), July, 1995, Toronto, Canada. Personal use of this material is
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