Comparative analysis of neural network and genetic programming for number of software faults prediction

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@InProceedings{Rathore:2015:RAECE,
  author =       "Santosh Singh Rathore and Sandeep Kuamr",
  booktitle =    "2015 National Conference on Recent Advances in
                 Electronics Computer Engineering (RAECE)",
  title =        "Comparative analysis of neural network and genetic
                 programming for number of software faults prediction",
  year =         "2015",
  pages =        "328--332",
  abstract =     "Software fault prediction can be more useful if,
                 besides predicting software modules being faulty or
                 non-faulty, number of faults can also be predicted
                 accurately. In this paper, we present an approach to
                 predict the number of faults in the software system. We
                 develop fault prediction model using neural network and
                 genetic programming and compare the effectiveness of
                 these techniques over ten project fault datasets
                 collected from the PROMISE data repository. The results
                 of the prediction are evaluated using error rate,
                 recall and completeness parameters. Our results found
                 that for small datasets, neural network produced better
                 results, while for large datasets genetic programming
                 produced better results. In terms of error values,
                 neural network outperformed genetic programming, while
                 for recall and completeness analysis, genetic
                 programming produced the result better than neural
                 network.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/RAECE.2015.7510216",
  month =        feb,
  notes =        "Also known as \cite{7510216}",
}

Genetic Programming entries for Santosh S Rathore Sandeep Kumar

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