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Computational Intelligence and Quantitative Software Engineering

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational Intelligence and Quantitative Software Engineering/ edited by Witold Pedrycz, Giancarlo Succi, Alberto Sillitti.
其他作者:	Pedrycz, Witold.
面頁冊數:	IX, 207 p. 41 illus., 15 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-25964-2
ISBN:	9783319259642

Computational Intelligence and Quantitative Software Engineering
Computational Intelligence and Quantitative Software Engineering[electronic resource] /edited by Witold Pedrycz, Giancarlo Succi, Alberto Sillitti. - 1st ed. 2016. - IX, 207 p. 41 illus., 15 illus. in color.online resource. - Studies in Computational Intelligence,6171860-949X ;. - Studies in Computational Intelligence,564.

In a down-to-the earth manner, the volume lucidly presents how the fundamental concepts, methodology, and algorithms of Computational Intelligence are efficiently exploited in Software Engineering and opens up a novel and promising avenue of a comprehensive analysis and advanced design of software artifacts. It shows how the paradigm and the best practices of Computational Intelligence can be creatively explored to carry out comprehensive software requirement analysis, support design, testing, and maintenance. Software Engineering is an intensive knowledge-based endeavor of inherent human-centric nature, which profoundly relies on acquiring semiformal knowledge and then processing it to produce a running system. The knowledge spans a wide variety of artifacts, from requirements, captured in the interaction with customers, to design practices, testing, and code management strategies, which rely on the knowledge of the running system. This volume consists of contributions written by widely acknowledged experts in the field who reveal how the Software Engineering benefits from the key foundations and synergistically existing technologies of Computational Intelligence being focused on knowledge representation, learning mechanisms, and population-based global optimization strategies. This book can serve as a highly useful reference material for researchers, software engineers and graduate students and senior undergraduate students in Software Engineering and its sub-disciplines, Internet engineering, Computational Intelligence, management, operations research, and knowledge-based systems.

ISBN: 9783319259642

Standard No.: 10.1007/978-3-319-25964-2doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Computational Intelligence and Quantitative Software Engineering
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