Towards an ensemble based system for predicting the number of software faults

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@Article{Rathore:2017:ESA,
  author =       "Santosh Singh Rathore and Sandeep Kumar",
  title =        "Towards an ensemble based system for predicting the
                 number of software faults",
  journal =      "Expert Systems with Applications",
  volume =       "82",
  pages =        "357--382",
  year =         "2017",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2017.04.014",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417417302506",
  abstract =     "Software fault prediction using different techniques
                 has been done by various researchers previously. It is
                 observed that the performance of these techniques
                 varied from dataset to dataset, which make them
                 inconsistent for fault prediction in the unknown
                 software project. On the other hand, use of ensemble
                 method for software fault prediction can be very
                 effective, as it takes the advantage of different
                 techniques for the given dataset to come up with better
                 prediction results compared to individual technique.
                 Many works are available on binary class software fault
                 prediction (faulty or non-faulty prediction) using
                 ensemble methods, but the use of ensemble methods for
                 the prediction of number of faults has not been
                 explored so far. The objective of this work is to
                 present a system using the ensemble of various learning
                 techniques for predicting the number of faults in given
                 software modules. We present a heterogeneous ensemble
                 method for the prediction of number of faults and use a
                 linear combination rule and a non-linear combination
                 rule based approaches for the ensemble. The study is
                 designed and conducted for different software fault
                 datasets accumulated from the publicly available data
                 repositories. The results indicate that the presented
                 system predicted number of faults with higher accuracy.
                 The results are consistent across all the datasets. We
                 also use prediction at level l (Pred(l)), and measure
                 of completeness to evaluate the results. Pred(l) shows
                 the number of modules in a dataset for which average
                 relative error value is less than or equal to a
                 threshold value l. The results of prediction at level l
                 analysis and measure of completeness analysis have also
                 confirmed the effectiveness of the presented system for
                 the prediction of number of faults. Compared to the
                 single fault prediction technique, ensemble methods
                 produced improved performance for the prediction of
                 number of software faults. Main impact of this work is
                 to allow better use of testing resources helping in
                 early and quick identification of most of the faults in
                 the software system.",
  keywords =     "genetic algorithms, genetic programming, Software
                 fault prediction techniques, Empirical study, Linear
                 regression, Gradient boosting, Promise repository",
}

Genetic Programming entries for Santosh S Rathore Sandeep Kumar

Citations