Constraint-Based Design Recovery for Software Reengineering: Theory and Experiments

204 Pages·1998·4.228 MB·English

by  Steven G. Woods, Alexander E. Quilici, Qiang Yang (auth.)

CONSTRAINT-BASED
DESIGN RECOVERY FOR
SOFTWARE REENGINEERING:
Theory and Experiments
THE KLUWER INTERNATIONAL SERIES IN
SOFTWARE ENGINEERING
Series Editor
Victor R. Basili
University ofM aryland
College Park, MD 20742
Also in the Series: .
FORMAL SPECIFICA nON TECHNIQUES FOR ENGINEERING MODULAR C
PROGRAMS
by TAN Yang Meng
ISBN: 0-7923-9653-7
TOOLS AND ENVIRONMENTS FOR PARALLEL AND DISTRIBUTED SYSTEMS
by Amr Zaky and Ted Lewis
ISBN: 0-7923-9675-8
The Kluwer International Series in Software Engineering addresses the
following goals:
• To coherently and consistently present important research topics and
their application(s).
• To present evolved concepts in one place as a coherent whole,
updating early versions of the ideas and notations.
• To provide publications which will be used as the ultimate
reference on the topic by experts in the area.
With the dynamic growth evident in this field and the need to communicate
findings, this series provides a forum for information targeted toward
Software Engineers.
CONSTRAINT- BASED
DESIGN RECOVERY FOR
SOFTWARE REENGINEERING:
Theory and Experiments
by
Steven G. Woods
Carnegie Mellon University
Pittsburgh, PA, USA
Alexander E. Quilici
University ofH awaii at Manoa
Honolulu, Hawaii, USA
Qiang Yang
Simon Fraser University
Burnaby, British Columbia, CANADA
SPRINGER-SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-7494-7 ISBN 978-1-4615-5461-5 (eBook)
DOI 10.1007/978-1-4615-5461-5
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright © 1998 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1998
Softcover reprint of the hardcover 1s t edition 1998
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission of the
publisher, Springer-Science+Business Media, LLC.
Printed on acid-free paper.
This book is dedicated to our
family and friends.
CONTENTS
LIST OF FIGURES XI
PREFACE xv
ACKNOWLEDGMENTS XXI
1. INTRODUCTION 1
1.1 PROGRAM UNDERSTANDING 1
Why Program Understanding Is So Important 2
Why Program Understanding Is Difficult 7
1.2 THE STATE OF THE PRACTICE 9
Tools For Forming And Accessing An Information Store 9
Tools That Apply The Information Store 11
1.3 CONCEPTUAL PROGRAM UNDERSTANDING 12
How People Understand Programs 12
Automated Program Understanding 13
Assisted Program Understanding 14
Interactive Progra m Understanding 16
1.4 THE REMAINDER OF THIS BOOK 17
2. PLAN-BASED PROGRAM UNDERSTANDING 19
2.1 OVERVIEW 19
2.2 AST-BASED APPROACHES 23
The Concept Recognizer: A Library-Driven Approach 23
DECODE: An Indexed, Code-Driven Approach 26
2.3 GRAPH-BASED APPROACHES 32
GRASPR: An Approach Based On Graph Parsing 32
Unprog: An Approach Based On Graph-Matching 36
2.4 ANALYSIS OF PREVIOUS APPROACHES 40
Scalability Issues 40
Comparing Approaches 41
Vll
Vlll CONSTRAINT-BASED DESIGN RECOVERY FOR SOFTWARE REENGINEERING
Summary: The State Of The Field 41
3. PROGRAM UNDERSTANDING AND CONSTRAINT SATISFACTION 43
3.1 INTRODUCTION 43
3.2 CONSTRAINT SATISFACTION PROBLEMS 44
Consistency Propagation 47
Backtrack-Based Search Algorithms 50
Other Methods 52
3.3 PROGRAM UNDERSTANDING AS CONSTRAINT-SATISFACTION 54
An Example Of Plan Matching 54
Plan Matching As MAP-CSP 54
Plan Ordering Through Layered MAP-CSP 59
3.4 MODELING EXISTING PROGRAM UNDERSTANDING ALGORITHMS 61
The Concept Recognizer 61
DECODE 63
Modeling Program Understanding As A Single CSP 64
4. INITIAL EXPERIMENTS WITH CONCEPT RECOVERY 67
4.1 INTRODUCTION 67
4.2 EXPERIMENTAL FRAMEWORK 68
The Actual Plan Representation 68
Generating Test Programs 69
Experimental Output 71
Measuring Efficiency 71
4.3 EXPERIMENTS WITH CSP SOLVERS 72
Exploring Different Strategies 73
Exploring Larger, More Complex Programs 77
Exploring Different Statement Distributions 79
Exploring Larger Plans 81
4.4 COMPARING EXISTING APPROACHES 83
The Concept Recognizer 83
DECODE 84
Experimental Methodology 85
Experimental Results 85
Experimental Analysis 86
4.5 SUMMARY OF RESULTS 88
5. ADDITIONAL EXPERIMENTS WITH CONCEPT RECOVERY 91
5.1 INTRODUCTION 91
5.2 EXPERIMENTAL FRAMEWORK 92
The Actual Plan Representation 92
Constructing Test Programs 93
5.3 SOME EXPERIMENTS WITH "REAL" PROGRAMS 94
CONTENTS IX
Real-World Versus Artificial Programs 95
Scaling And Plan Size 98
5.4 EXPERIMENTS WITH DOMAIN-VALUE PRE-FILTERING 99
An Initial Experiment 101
Scaling With Plan Size 102
Hierarchical Decomposition 103
Constraint Ordering 106
5.5 EXPERIMENTS WITH A NEW CONSTRAINT-BASED ALGORITHM 108
Redoing Our Earlier Experiments 110
Recognizing A Year 2000 Plan 111
5.6 SUMMARY 115
6. PROGRAM UNDERSTANDING AND AI PLAN RECOGNITION 117
6.1 INTRODUCTION 117
6.2 AN AI APPROACH TO PLAN RECOGNITION 118
An Action Hierarchy 119
Using The Action Hierarchy 119
A Detailed Example 121
6.3 PROBLEMS WITH APPLYING AI PLAN RECOGNITION 124
Incorrect Plan Recognition 127
Inefficient Plan Recognition 129
6.4 LAYERED MAP-CSP VERSUS THE AI APPROACH 131
7. IMPROVING OUR CONSTRAINT-BASED APPROACH 133
7.1 INTRODUCTION 133
Improving Scalability With Library Size 134
Performing Imprecise Program Understanding 134
Interaction With The User 135
7.2 ADDRESSING PROBLEMS WITH LAYERED MAP-CSP 135
The Library Scalability Problem 135
Tackling Imprecise Program Understanding 137
Supporting User Interaction 137
An Extended Layered MAP-CSP Algorithm 138
7.3 INTEGRATED UNDERSTANDING: PU-CSP 140
The Elements Of A PU-CSP 140
Forming A PU-CSP 140
Structural Versus Knowledge Constraints 142
How PU-CSP Uses Constraint Propagation 144
The Computational Complexity Of PU-CSP 147
7.4 SOLVING STRATEGIES FOR PU-CSP 148
An Interactive Bottom-Up Algorithm 148
Using PU-CSP As A Top-Down Algorithm 149
Hierarchical Arc-Consistency 151
x CONSTRAINT-BASED DESIGN RECOVERY FOR SOFTWARE REENGINEERING
Key Differences From Discrete Arc-Consistency 151
Examples Of Hierarchical Consistency Propagation 153
7.5 PU-CSP VERSUS LAYERED MAP-CSP 156
8. CONCLUSIONS 159
8.1 SUMMARY 159
8.2 FUTURE EXPERIMENTS 161
Experiments With MAP-CSP And Plan-Size 161
Experiments With MAP-CSP And Real-World Programs 161
Experiments With MAP-CSP And Generated Programs 162
Experiments With Layered MAP-CSP 163
Experiments With PU-CSP 164
8.3 FUTURE MODELING AND ALGORITHM DEVELOPMENT 164
Improving Our Models Of Existing Algorithms 164
Forming Models Of Other Program Understanding Algorithms 165
Improving Our Algorithms 166
Design Pattern Recognition 167
8.4 FUTURE TOOL DEVELOPMENT 168
Improving Program Understanding Environments 168
Locating Code By Example 169
8.5 APPLYING PLAN RECOGNITION TO THE YEAR 2000 PROBLEM 170
Pattern-Based Tech niq ues 171
Rule-Based Techniques 171
A Year 2000 Plan Library 172
Benefits Of Integrating Plan Recognition 173
8.6 CONTRIBUTIONS TO ARTIFICIAL INTElliGENCE 173
A New Plan Recognition Algorithm 174
A New Domain For Constraint Satisfaction Research 174
8.7 FINAL REMARKS 175
REFERENCES 177
INDEX
185
LIST OF FIGURES
1.1 An example incorrect, leap-year related, COBOL code fragment. 3
1.2 A fixed version of the incorrect leap-year related COBOL code
fragment. 4
l.3 A C language source code fragment that displays a two-digit date. 5
1.4 A revised version of the earlier C code that now displays a four digit
date. 5
1.5 Some imperative C code that manipulates strings. 6
1.6 Our imperative C code translated to object-oriented C++. 6
1.7 The role of program understanding in software maintenance and
reengineering. 10
1.8 The architecture of most state-of-the-practice environments for soft-
ware maintenance and reengineering. 11
1.9 How programmers understand source code. 13
1.10 Our approach to automated program understanding. 15
1.11 An illustration of assisted program understanding. 16
2.1 The common architecture of plan recognition systems. 20
2.2 An example hierarchical code understanding. 21
2.3 An example of the Concept Recognizer's representation for plans. 24
2.4 An example of the DECODE's plan representation. 28
2.5 DECODE's plan recognition algorithm. 31
2.6 A Lisp function to test whether the three input sides form a right
triangle. 33
2.7 An attributed flow graph for the RIGHTP function. 34
2.8 An example flowgraph representation of one rule for recognizing
the Negate-If-Negative design concept. 35
2.9 GRASPR's chart-parsing plan recognition algorithm. 36
2.10 An example COBOL code fragment. 37
Xl