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Empirical analysis of software quality prediction using a TRAINBFG algorithm
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| Zeitschriftentitel: | International Journal of Engineering & Technology |
|---|---|
| Personen und Körperschaften: | , |
| In: | International Journal of Engineering & Technology, 7, 2018, 2.6, S. 259 |
| Medientyp: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Science Publishing Corporation
|
| Schlagwörter: |
| author_facet |
Pattnaik, Saumendra Kumar Pattanayak, Binod Pattnaik, Saumendra Kumar Pattanayak, Binod |
|---|---|
| author |
Pattnaik, Saumendra Kumar Pattanayak, Binod |
| spellingShingle |
Pattnaik, Saumendra Kumar Pattanayak, Binod International Journal of Engineering & Technology Empirical analysis of software quality prediction using a TRAINBFG algorithm Hardware and Architecture General Engineering General Chemical Engineering Environmental Engineering Computer Science (miscellaneous) Biotechnology |
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pattnaik, saumendra |
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Pattnaik, Saumendra Kumar Pattanayak, Binod 2227-524X Science Publishing Corporation Hardware and Architecture General Engineering General Chemical Engineering Environmental Engineering Computer Science (miscellaneous) Biotechnology http://dx.doi.org/10.14419/ijet.v7i2.6.10780 <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> Empirical analysis of software quality prediction using a TRAINBFG algorithm International Journal of Engineering & Technology |
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International Journal of Engineering & Technology |
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| title |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_unstemmed |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_full |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_fullStr |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_full_unstemmed |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_short |
Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_sort |
empirical analysis of software quality prediction using a trainbfg algorithm |
| topic |
Hardware and Architecture General Engineering General Chemical Engineering Environmental Engineering Computer Science (miscellaneous) Biotechnology |
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http://dx.doi.org/10.14419/ijet.v7i2.6.10780 |
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2018 |
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259 |
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<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> |
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| author | Pattnaik, Saumendra, Kumar Pattanayak, Binod |
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| description | <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> |
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| spelling | Pattnaik, Saumendra Kumar Pattanayak, Binod 2227-524X Science Publishing Corporation Hardware and Architecture General Engineering General Chemical Engineering Environmental Engineering Computer Science (miscellaneous) Biotechnology http://dx.doi.org/10.14419/ijet.v7i2.6.10780 <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> Empirical analysis of software quality prediction using a TRAINBFG algorithm International Journal of Engineering & Technology |
| spellingShingle | Pattnaik, Saumendra, Kumar Pattanayak, Binod, International Journal of Engineering & Technology, Empirical analysis of software quality prediction using a TRAINBFG algorithm, Hardware and Architecture, General Engineering, General Chemical Engineering, Environmental Engineering, Computer Science (miscellaneous), Biotechnology |
| title | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_full | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_fullStr | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_full_unstemmed | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_short | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| title_sort | empirical analysis of software quality prediction using a trainbfg algorithm |
| title_unstemmed | Empirical analysis of software quality prediction using a TRAINBFG algorithm |
| topic | Hardware and Architecture, General Engineering, General Chemical Engineering, Environmental Engineering, Computer Science (miscellaneous), Biotechnology |
| url | http://dx.doi.org/10.14419/ijet.v7i2.6.10780 |