The great challenge of reverse engineering is recovering design information from legacy code: the concept-recovery problem. This monograph describes recent research which addresses this problem. It discusses a 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 the authors' claims of scalability. Models and experiments are presented in sufficient detail for easy replication. The book is intended for researchers and software developers concerned with reverse engineering or re-engineering legacy systems. It may also interest researchers who are interested in using plan-recognition techniques or constraint-based reasoning. The reader is assumed to be familiar with the basics of programming and algorithm analysis, but does not need to have a familiarity with the fields of reverse engineering or artificial intelligence. Designed as a reference for advanced undergraduate or graduate seminar courses in software engineering, reverse engineering, or reengineering, the book 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.