Hardback : £165.00
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.
Show moreThe 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.
Show more1. 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.
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