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Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

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Abstract

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.

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Abbreviations

NHPP:

Non Homogeneous Poisson Process

SRGM:

Software Reliability Growth Model

ANN:

Artificial Neural Network

NARX:

Nonlinear Autoregressive with eXogenous Inputs

TDNN:

Time Delay Neural Network

MAE:

Mean Absolute Error

RMSE:

Root Mean Square Error

SP:

Series Parallel mode

P:

Parallel Mode

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Authors and Affiliations

  1. Department of Applied Mathematics, ISM, Dhanbad, 826004, India

    S. Chatterjee, S. Nigam, J. B. Singh & L. N. Upadhyaya

Authors
  1. S. Chatterjee
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  2. S. Nigam
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  3. J. B. Singh
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  4. L. N. Upadhyaya
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Correspondence to S. Chatterjee.

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Chatterjee, S., Nigam, S., Singh, J.B. et al. Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network. Appl Intell 37, 121–129 (2012). https://doi.org/10.1007/s10489-011-0316-x

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