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Software Engineering with Computational Intelligence

  • Taghi M. Khoshgoftaar

Table of contents

  1. Front Matter
    Pages i-xi
  2. Stephen G. MacDonell, Andrew R. Gray
    Pages 17-43
  3. Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl
    Pages 69-100
  4. Martin Neil, Paul Krause, Norman Fenton
    Pages 136-172
  5. Ali Mili, Bojan Cukic, Yan Liu, Rahma Ben Ayed
    Pages 173-203
  6. Lionel C. Briand, Jie Feng, Yvan Labiche
    Pages 204-234
  7. Mark Last, Abraham Kandel
    Pages 235-258
  8. Robert M. Patton, Annie S. Wu, Gwendolyn H. Walton
    Pages 259-286
  9. David C. Kung, Hitesh Bhambhani, Riken Shah, Gaurav Pancholi
    Pages 287-318
  10. Tim Menzies, Eliza Chiang, Martin Feather, Ying Hu, James D. Kiper
    Pages 319-361

About this book

Introduction

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 these and other continuously growing challenges by developing and implementing useful software engineering methodologies. Among the more recent advances are those made in the context of software portability, formal verification· techniques, software measurement, and software reuse. However, despite the introduction of some important and useful paradigms in the software engineering discipline, their technological transfer on a larger scale has been extremely gradual and limited. For example, many software development organizations may not have a well-defined software assurance team, which can be considered as a key ingredient in the development of a high-quality and dependable software product. Recently, the software engineering field has observed an increased integration or fusion with the computational intelligence (Cl) field, which is comprised of primarily the mature technologies of fuzzy logic, neural networks, genetic algorithms, genetic programming, and rough sets. Hybrid systems that combine two or more of these individual technologies are also categorized under the Cl umbrella. Software engineering is unlike the other well-founded engineering disciplines, primarily due to its human component (designers, developers, testers, etc. ) factor. The highly non-mechanical and intuitive nature of the human factor characterizes many of the problems associated with software engineering, including those observed in development effort estimation, software quality and reliability prediction, software design, and software testing.

Keywords

Bayesian network Computational Intelligence Evolution algorithms design expert system fuzzy logic genetic algorithms genetic programming intelligence modeling programming project management validation verification

Editors and affiliations

  • Taghi M. Khoshgoftaar
    • 1
  1. 1.Florida Atlantic UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-0429-0
  • Copyright Information Kluwer Academic Publishers 2003
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5072-9
  • Online ISBN 978-1-4615-0429-0
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site