國立虎尾科技大學 |

Computational Intelligence and Quantitative Software Engineering

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational Intelligence and Quantitative Software Engineering/ edited by Witold Pedrycz, Giancarlo Succi, Alberto Sillitti.
other author:	Pedrycz, Witold.
Description:	IX, 207 p. 41 illus., 15 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	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
LDR:03043nam a22003975i 4500 001 972397
003 DE-He213
005 20200704015008.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319259642 $9 978-3-319-25964-2
024 7 $a 10.1007/978-3-319-25964-2 $2 doi
035 $a 978-3-319-25964-2
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Computational Intelligence and Quantitative Software Engineering $h [electronic resource] / $c edited by Witold Pedrycz, Giancarlo Succi, Alberto Sillitti.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a IX, 207 p. 41 illus., 15 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 617
520 $a 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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Software engineering. $3 562952
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Software Engineering. $3 669632
700 1 $a Pedrycz, Witold. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 678017
700 1 $a Succi, Giancarlo. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1104772
700 1 $a Sillitti, Alberto. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 787818
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319259628
776 0 8 $i Printed edition: $z 9783319259635
776 0 8 $i Printed edition: $z 9783319798660
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-319-25964-2
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)