國立虎尾科技大學 |

Fault Prediction Modeling for the Prediction of Number of Software Faults

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Fault Prediction Modeling for the Prediction of Number of Software Faults/ by Santosh Singh Rathore, Sandeep Kumar.
Author:	Rathore, Santosh Singh.
other author:	Kumar, Sandeep.
Description:	XIII, 78 p. 8 illus., 1 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Software engineering. -
Online resource:	https://doi.org/10.1007/978-981-13-7131-8
ISBN:	9789811371318

Fault Prediction Modeling for the Prediction of Number of Software Faults
Rathore, Santosh Singh.

Fault Prediction Modeling for the Prediction of Number of Software Faults[electronic resource] /by Santosh Singh Rathore, Sandeep Kumar. - 1st ed. 2019. - XIII, 78 p. 8 illus., 1 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

Introduction -- Techniques used for the Prediction of Number of Faults -- Homogeneous Ensemble Methods for the Prediction of Number of Faults -- Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Non-Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Conclusions.

This book addresses software faults—a critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults. A must-read for readers seeking a “one-stop” source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments. .

ISBN: 9789811371318

Standard No.: 10.1007/978-981-13-7131-8doiSubjects--Topical Terms:

562952
Software engineering.

LC Class. No.: QA76.758

Dewey Class. No.: 005.1

Fault Prediction Modeling for the Prediction of Number of Software Faults
LDR:02717nam a22004095i 4500 001 1012540
003 DE-He213
005 20200701050846.0
007 cr nn 008mamaa
008 210106s2019 si | s |||| 0|eng d
020 $a 9789811371318 $9 978-981-13-7131-8
024 7 $a 10.1007/978-981-13-7131-8 $2 doi
035 $a 978-981-13-7131-8
050 4 $a QA76.758
072 7 $a UMZ $2 bicssc
072 7 $a COM051230 $2 bisacsh
072 7 $a UMZ $2 thema
082 0 4 $a 005.1 $2 23
100 1 $a Rathore, Santosh Singh. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1205894
245 1 0 $a Fault Prediction Modeling for the Prediction of Number of Software Faults $h [electronic resource] / $c by Santosh Singh Rathore, Sandeep Kumar.
250 $a 1st ed. 2019.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a XIII, 78 p. 8 illus., 1 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 SpringerBriefs in Computer Science, $x 2191-5768
505 0 $a Introduction -- Techniques used for the Prediction of Number of Faults -- Homogeneous Ensemble Methods for the Prediction of Number of Faults -- Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Non-Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Conclusions.
520 $a This book addresses software faults—a critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults. A must-read for readers seeking a “one-stop” source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments. .
650 0 $a Software engineering. $3 562952
650 0 $a Computer industry. $3 563946
650 1 4 $a Software Engineering. $3 669632
650 2 4 $a The Computer Industry. $3 669952
700 1 $a Kumar, Sandeep. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 883974
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811371301
776 0 8 $i Printed edition: $z 9789811371325
776 0 8 $i Printed edition: $z 9789811371332
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-981-13-7131-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)