Empirical analysis of software quality prediction using a TRAINBFG algorithm

  • Authors

    • Saumendra Pattnaik
    • Binod Kumar Pattanayak
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10780
  • MATLAB, Software quality metrics, Fuzzy Logic, SPSS, Neural Network.

  • Abstract

    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.

     

  • References

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  • How to Cite

    Pattnaik, S., & Kumar Pattanayak, B. (2018). Empirical analysis of software quality prediction using a TRAINBFG algorithm. International Journal of Engineering & Technology, 7(2.6), 259-268. https://doi.org/10.14419/ijet.v7i2.6.10780

    Received date: 2018-03-29

    Accepted date: 2018-03-29

    Published date: 2018-03-11