Constraint-Based Design Recovery for Software Reengineering

Theory and Experiments

Inbunden, Engelska, 1997

AvSteven G. Woods,Alexander E. Quilici,Qiang Yang,Qiang Yang

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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.

Produktinformation

Utgivningsdatum1997-11-30
Mått155 x 235 x 18 mm
Vikt492 g
FormatInbunden
SpråkEngelska
SerieInternational Series in Software Engineering
Antal sidor189
Upplaga1998
FörlagKluwer Academic Publishers
ISBN9780792380672

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1. Introduction.- 1.1 Program Understanding.- 1.2 The State of The Practice.- 1.3 Conceptual Program Understanding.- 1.4 The Remainder of This Book.- 2. Plan-Based Program Understanding.- 2.1 Overview.- 2.2 Ast-Based Approaches.- 2.3 Graph-Based Approaches.- 2.4 Analysis of Previous Approaches.- 3. Program Understanding and Constraint Satisfaction.- 3.1 Introduction.- 3.2 Constraint Satisfaction Problems.- 3.3 Program Understanding as Constraint-Satisfaction.- 3.4 Modeling Existing Program Understanding Algorithms.- 4. Initial Experiments with Concept Recovery.- 4.1 Introduction.- 4.2 Experimental Framework.- 4.3 Experiments With Csp Solvers.- 4.4 Comparing Existing Approaches.- 4.5 Summary of Results.- 5. Additional Experiments with Concept Recovery.- 5.1 Introduction.- 5.2 Experimental Framework.- 5.3 Some Experiments With “Real” Programs.- 5.4 Experiments with Domain-Value Pre-Filtering.- 5.5 Experiments With A New Constraint-Based Algorithm.- 5.6 Summary.- 6. Program Understanding and AI Plan Recognition.- 6.1 Introduction.- 6.2 An Ai Approach to Plan Recognition.- 6.3 Problems with Applying AI Plan Recognition.- 6.4 Layered Map-CSP Versus the AI Approach.- 7. Improving our Constraint-Based Approach.- 7.1 Introduction.- 7.2 Addressing Problems with Layered Map-Csp.- 7.3 Integrated Understanding: PU-CSP.- 7.4 Solving Strategies for PU-CSP.- 7.5 PU-CSP Versus Layered MAP-CSP.- 8. Conclusions.- 8.1 Summary.- 8.2 Future Experiments.- 8.3 Future Modeling and Algorithm Development.- 8.4 Future Tool Development.- 8.5 Applying Plan Recognition to the Year 2000 Problem.- 8.6 Contributions to Artificial Intelligence.- 8.7 Final Remarks.- References.