Software Engineering with Computational Intelligence
The constantly evolving technological infrastructure of the modem world presents a great challenge of developing software systems with increasing size, complexity, and functionality. The software engineering field has seen changes and innovations to meet
- PDF / 35,142,696 Bytes
- 373 Pages / 439 x 666 pts Page_size
- 59 Downloads / 304 Views
THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE
SOFTWARE ENGINEERING WITH COMPUTATIONAL INTELLIGENCE
edited by
Taghi M. Khoshgoftaar Florida Atlantic University, US.A.
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress CataIogiog-in-PubHcation
Title: Editor:
Software Engineering with Computational Intelligence Taghi M. Khoshgoftaar
ISBN 978-1-4613-5072-9 ISBN 978-1-4615-0429-0 (eBook) DOI 10.1007/978-1-4615-0429-0 Copyright © 2003 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2003 Softcover reprint of the hardcover 1st edition 2003 Ali rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photo-copying, microftlming, recording, or otherwise, without the prior written permission of the publisher, with the exception of any material supplied specifica1ly for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Permissions for books published in the USA: perm i ş şi an ş®wkap corn Permissions for books published in Europe: [email protected]
Printed on acid-free paper.
Table of Contents
1.
Preface
vii
Acknowledgment
xi
Applying Machine Learners to GUI Specifications in Formulating Early Life Cycle Project Estimations
1
Gary D. Boetticher.
2.
.
. .
Applying Fuzzy Logic Modeling to Software Project Management Stephen G. MacDonell and Andrew R. Gray .
3.
17
..
Integrating Genetic Algorithms With Systems Dynamics To Optimize Quality Assurance Effort Allocation
44
Balasubramaniam Ramesh and Tarek K. Abdel-Hamid .
4.
Improved Fault-Prone Detection Analysis of Software Modules Using an Evolutionary Neural Network Approach
69
Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen and John P. Hudepohl.. .. ..
5.
A Fuzzy Model and the AdeQuaS Fuzzy Tool: a theoretical and a practical view of the Software Quality Evaluation Kelly R. Oliveira and Arnaldo D. Belchior . .
6.
.
101
. ..
Software Quality Prediction Using Bayesian Networks
136
Martin Neil, Paul Krause and Norman Fenton . .
7.
Towards the Verification and Validation of Online Learning Adaptive Systems
173
Ali Mili, Bojan Cukic, Van Liu and Rahma Ben Ayed
8.
Experimenting with Genetic Algorithms to Devise Optimal Integration Test Orders Lionel C. Briand, Jie Feng and Yvan Labiche .
204
vi
9.
Automated Test Reduction Using an Info-Fuzzy Network 235 Mark Last and Abraham Kandel . . . . . . . . . .
10. A Genetic Algorithm Approach to Focused Software Usage Testing
259
Robert M. Patton, Annie S. Wu, and Gwendolyn H. Walton
11. An Expert System for Suggesting Design Patterns - A Methodology and a Prototype
287
David C. Kung, Hitesh Bhambhani, Riken Shah and Gaurav Pancholi . . . . . . . . . . . . . . . . . . ..
12. Condensing Uncertainty via Incremental Treatment Learning Tim Menzies, Eliza Chiang, Martin Feather, Ying Hu, James D. Kiper . . . . . . . . . . . . . . . . . .
