Computational Intelligence In Software Quality Assurance

By: Abraham Kandel (Editor), Scott Dick (Editor)

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Hardcover | 24 October 2005

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200 Pages

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23.5 x 16.2 x 1.8

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Software systems surround us. Software is a critical component in everything from the family car through electrical power systems to military equipment. As software plays an ever-increasing role in our lives and livelihoods, the quality of that software becomes more and more critical. However, our ability to deliver high-quality software has not kept up with those increasing demands. The economic fallout is enormous; the US economy alone is losing over USD50 billion per year due to software failures. This book presents new research into using advanced artificial intelligence techniques to guide software quality improvements. The techniques of chaos theory and data mining are brought to bear to provide new insights into the software development process. Written for researchers and practitioners in software engineering and computational intelligence, this book is a unique and important bridge between these two fields.

Industry Reviews

"Written for researchers and practitioners in software engineering and computational intelligence, this book is a unique and important bridge between these two fields." Zentralblatt MATH

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Dedication	p. v
Acknowledgements	p. vii
Foreword	p. ix
Preface	p. xiii
Software Engineering and Artificial Intelligence	p. 1
Introduction	p. 1
Overview of Software Engineering	p. 5
The Capability Maturity Model	p. 6
Software Life Cycle Models	p. 7
Modern Software Development	p. 12
Requirements Engineering	p. 13
Software Architecture	p. 16
OO Design	p. 19
Design Patterns	p. 20
Maintenance Cycle	p. 22
New Directions	p. 23
Artificial Intelligence in Software Engineering	p. 26
Computational Intelligence	p. 29
Fuzzy Sets and Fuzzy Logic	p. 30
Artificial Neural Networks	p. 32
Genetic Algorithms	p. 34
Fractal Sets and Chaotic Systems	p. 35
Combined CI Methods	p. 39
Case Based Reasoning	p. 40
Machine Learning	p. 42
Data Mining	p. 43
Computational Intelligence in Software Engineering	p. 44
Remarks	p. 44
Software Testing and Artificial Intelligence	p. 46
Introduction	p. 46
Software Quality	p. 46
Software Testing	p. 52
White-Box Testing	p. 53
Black-Box Testing	p. 57
Testing Graphical User Interfaces	p. 58
Artificial Intelligence in Software Testing	p. 59
Computational Intelligence in Software Testing	p. 61
Remarks	p. 62
Chaos Theory and Software Reliability	p. 65
Introduction	p. 65
Reliability Engineering for Software	p. 71
Reliability Engineering	p. 71
Reliability Analysis	p. 72
Reliability Testing	p. 77
Software Reliability Engineering	p. 79
Software Reliability Models	p. 82
Nonlinear Time Series Analysis	p. 87
Analytical Techniques	p. 87
Software Reliability Data	p. 93
Experimental Results	p. 94
State Space Reconstruction	p. 94
Test for Determinism	p. 96
Dimensions	p. 98
Remarks	p. 98
Data Mining and Software Metrics	p. 107
Introduction	p. 107
Review of Related Work	p. 109
Machine Learning for Software Quality	p. 109
Fuzzy Cluster Analysis	p. 111
Feature Space Reduction	p. 113
Software Change and Software Characteristic Datasets	p. 114
The MIS Dataset	p. 114
The OOSoft and ProcSoft Datasets	p. 117
Fuzzy Cluster Analysis	p. 119
Results for the MIS Dataset	p. 119
Results for the ProcSoft Dataset	p. 127
Results for OOSoft	p. 129
Conclusions from Fuzzy Clustering	p. 131
Data Mining	p. 133
The MIS Dataset	p. 133
The OOSoft Dataset	p. 135
The ProcSoft Dataset	p. 136
Remarks	p. 137
Skewness and Resampling	p. 139
Introduction	p. 139
Machine Learning in Skewed Datasets	p. 140
Experimental Results	p. 144
Proposed Usage	p. 149
Remarks	p. 152
Conclusion	p. 153
References	p. 157
About the Authors	p. 179
Table of Contents provided by Ingram. All Rights Reserved.

Computational Intelligence In Software Quality Assurance

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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