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The great challenge of reverse engineering is recovering design
information from legacy code: the concept recovery problem. This
monograph describes our research effort in attacking this problem.
It discusses our theory of how a constraint-based approach to
program plan recognition can efficiently extract design concepts
from source code, and it details experiments in concept recovery
that support our claims of scalability. Importantly, we present our
models and experiments in sufficient detail so that they can be
easily replicated. This book is intended for researchers or
software developers concerned with reverse engineering or
reengineering legacy systems. However, it may also interest those
researchers who are interested using plan recognition techniques or
constraint-based reasoning. We expect the reader to have a
reasonable computer science background (i.e., familiarity with the
basics of programming and algorithm analysis), but we do not
require familiarity with the fields of reverse engineering or
artificial intelligence (AI). To this end, we carefully explain all
the AI techniques we use. This book is designed as a reference for
advanced undergraduate or graduate seminar courses in software
engineering, reverse engineering, or reengineering. It can also
serve as a supplementary textbook for software engineering-related
courses, such as those on program understanding or design recovery,
for AI-related courses, such as those on plan recognition or
constraint satisfaction, and for courses that cover both topics,
such as those on AI applications to software engineering.
ORGANIZATION The book comprises eight chapters.
The great challenge of reverse engineering is recovering design
information from legacy code: the concept recovery problem. This
monograph describes our research effort in attacking this problem.
It discusses our theory of how a constraint-based approach to
program plan recognition can efficiently extract design concepts
from source code, and it details experiments in concept recovery
that support our claims of scalability. Importantly, we present our
models and experiments in sufficient detail so that they can be
easily replicated. This book is intended for researchers or
software developers concerned with reverse engineering or
reengineering legacy systems. However, it may also interest those
researchers who are interested using plan recognition techniques or
constraint-based reasoning. We expect the reader to have a
reasonable computer science background (i.e., familiarity with the
basics of programming and algorithm analysis), but we do not
require familiarity with the fields of reverse engineering or
artificial intelligence (AI). To this end, we carefully explain all
the AI techniques we use. This book is designed as a reference for
advanced undergraduate or graduate seminar courses in software
engineering, reverse engineering, or reengineering. It can also
serve as a supplementary textbook for software engineering-related
courses, such as those on program understanding or design recovery,
for AI-related courses, such as those on plan recognition or
constraint satisfaction, and for courses that cover both topics,
such as those on AI applications to software engineering.
ORGANIZATION The book comprises eight chapters.
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