Empirical analysis of software quality prediction using a TRAINBFG algorithm

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In: International Journal of Engineering & Technology, 7(2018), 2.6, S. 259
Format: E-Article
Sprache: Unbestimmt
veröffentlicht: Science Publishing Corporation
ISSN: 2227-524X
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finc.format ElectronicArticle
finc.mega_collection Science Publishing Corporation (CrossRef)
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ris.type EJOUR
rft.atitle Empirical analysis of software quality prediction using a TRAINBFG algorithm
rft.epage 0
rft.genre article
rft.issn 2227-524X
rft.issue 2.6
rft.jtitle International Journal of Engineering & Technology
rft.tpages 0
rft.pages 259
rft.pub Science Publishing Corporation
rft.date 2018-03-11
x.date 2018-03-11T00:00:00Z
rft.spage 0
rft.volume 7
abstract <jats:p>Software quality plays a major role in software fault proneness. That’s why prediction of software quality is essential for measuring the anticipated faults present in the software. In this paper we have proposed a Neuro-Fuzzy model for prediction of probable values for a predefined set of software characteristics by virtue of using a rule base. In course of it, we have used several training algorithms among which TRAINBFG algorithm is observed to be the best one for the purpose. There are various training algorithm available in MATLAB for training the neural network input data set. The prediction using fuzzy logic and neural network provides better result in comparison with only neural network. We find out from our implementation that TRAINBFG algorithm can provide better predicted value as compared to other algorithm in MATLAB. We have validated this result using the tools like SPSS and MATLAB. </jats:p>
authors Pattnaik Saumendra
Kumar Pattanayak Binod
doi 10.14419/ijet.v7i2.6.10780
languages und
url http://dx.doi.org/10.14419/ijet.v7i2.6.10780
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