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Software Engineering with Computational Intelligence by Taghi M. Khoshgoftaar
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N.º de artículo de eBay:113905899338
Última actualización el 03 ago 2024 17:29:55 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- ISBN
- 9781402074271
Acerca de este producto
Product Identifiers
Publisher
Springer
ISBN-10
1402074271
ISBN-13
9781402074271
eBay Product ID (ePID)
2462791
Product Key Features
Number of Pages
Xi, 361 Pages
Language
English
Publication Name
Software Engineering with Computational Intelligence
Subject
Software Development & Engineering / General, Intelligence (Ai) & Semantics, Enterprise Applications / General
Publication Year
2003
Type
Textbook
Subject Area
Computers
Series
The Springer International Series in Engineering and Computer Science Ser.
Format
Hardcover
Dimensions
Item Weight
54.7 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2003-040132
Dewey Edition
21
Series Volume Number
731
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
005.1
Table Of Content
1. Applying Machine Learners to GUI Specifications in Formulating Early Life Cycle Project Estimations.- 2. Applying Fuzzy Logic Modeling to Software Project Management.- 3. Integrating Genetic Algorithms with Systems Dynamics to Optimize Quality Assurance Effort Allocation.- 4. Improved Fault-Prone Detection Analysis of Software Modules Using an Evolutionary Neural Network Approach.- 5. A Fuzzy Model and the AdeQuaS Fuzzy Tool: a theoretical and a practical view of the Software Quality Evaluation.- 6. Software Quality Prediction Using Bayesian Networks.- 7. Towards the Verification and Validation of Online Learning Adaptive Systems.- 8. Experimenting with Genetic Algorithms to Devise Optimal Integration Test Orders.- 9. Automated Test Reduction Using an Info-Fuzzy Network.- 10. A Genetic Algorithm Approach to Focused Software Usage Testing.- 11. An Expert System for Suggesting Design Patterns - A Methodology and a Prototype.- 12. Condensing Uncertainty via Incremental Treatment Learning.
Synopsis
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., 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 softwaretesting., 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., 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 softwaretesting.
LC Classification Number
QA76.76.A65
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- h***i (959)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaUltra-lightening fast shipping, exactly as described, carefully packaged. Great value for price! Thank you!Frontiers of Alcoholism by Morris E. Chafetz and Howard T. Blane (#114157013780)
- s***s (81)- Votos emitidos por el comprador.Último añoCompra verificadaPrompt at answering questions regarding the item! Packaged book very well for shipping which was much appreciated! Arrived promptly and ask described! Thank you so much!The Shadow Children Paperback – 1994 by Steven Schnur (Author) (#113873567451)