Software Engineering with Computational Intelligence

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April 2003



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.


Preface. Acknowledgment.
1. Applying Machine Learners to GUI Specifications in Formulating Early Life Cycle Project Estimations; G.D. Boetticher.
2. Applying Fuzzy Logic Modeling to Software Project Management; S.G. MacDonell, A.R. Gray.
3. Integrating Genetic Algorithms With Systems Dynamics To Optimize Quality Assurance Effort Allocation; B. Ramesh, T.K. Abdel-Hamid.
4. Improved Fault-Prone Detection Analysis of Software Modules Using an Evolutionary Neural Network Approach; R. Hochman, T.M. Khoshgoftaar, E.B. Allen, J.P. Hudepohl.
5. A Fuzzy Model and the AdeQuaS Fuzzy Tool: a theoretical and a practical view of the Software Quality Evaluation; K.R. Oliveira, A.D. Belchior.
6. Software Quality Prediction Using Bayesian Networks; M. Neil, P. Krause, N. Fenton.
7. Towards the Verification and Validation of Online Learning Adaptive Systems; A. Mili, B. Cukic, Yan Liu, R. Ben Ayed.
8. Experimenting with Genetic Algorithms to Devise Optimal Integration Test Orders; L.C. Briand, Jie Feng, Y. Labiche.
9. Automated Test Reduction Using an Info-Fuzzy Network; M. Last, A. Kandel.
10. A Genetic Algorithm Approach to Focused Software Usage Testing; R.M. Patton, A.S. Wu, G.H. Walton.
11. An Expert System for Suggesting Design Patterns - A Methodology and a Prototype; D.C. Kung, H. Bhambhani, R. Shah, G. Pancholi.
12. Condensing Uncertainty via Incremental Treatment Learning; T. Menzies, E. Chiang, M. Feather, Ying Hu, J.D. Kiper.
EAN: 9781402074271
ISBN: 1402074271
Untertitel: 2003. Auflage. Sprache: Englisch.
Erscheinungsdatum: April 2003
Seitenanzahl: 361 Seiten
Format: gebunden
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