Comparative analysis of software reliability predictions using statistical and machine learning methods

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@Article{Kumar:2013:IJISTA,
  title =        "Comparative analysis of software reliability
                 predictions using statistical and machine learning
                 methods",
  author =       "Pradeep Kumar and Yogesh Singh",
  publisher =    "Inderscience Publishers",
  year =         "2013",
  month =        sep # "~25",
  volume =       "12",
  ISSN =         "1740-8873",
  journal =      "Int. J. of Intelligent Systems Technologies and
                 Applications",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  number =       "3/4",
  language =     "eng",
  pages =        "230--253",
  ISSN =         "1740-8873",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, software reliability, machine
                 learning, artificial neural networks, ANNs, support
                 vector machines, SVM, fuzzy inference systems, ANFIS,
                 group method of data handling, GMDH, fuzzy logic, GEP,
                 multivariate adaptive regression splines, reliability
                 prediction, failure datasets.",
  URL =          "http://www.inderscience.com/link.php?id=56529",
  DOI =          "DOI:10.1504/IJISTA.2013.056529",
  abstract =     "This paper examines the performance of statistical
                 (linear regression) and machine learning methods like
                 Radial Basis Function Network (RBFN), Generalised
                 Regression Neural Network (GRNN), Support Vector
                 Machine (SVM), Fuzzy Inference System (FIS), Adaptive
                 Neuro Fuzzy Inference System (ANFIS), Gene Expression
                 Programming (GEP), Group Method of Data Handling (GMDH)
                 and Multivariate Adaptive Regression Splines (MARS) for
                 predicting software reliability. The effectiveness of
                 LR and machine learning methods are illustrated with
                 the help of 16 failure datasets of real-life projects
                 taken from Data and Analysis Centre for Software
                 (DACS). Two performance measures, Root Mean Squared
                 Error (RMSE) and Mean Absolute Percentage Error (MAPE),
                 are compared quantitatively obtained from rigours
                 experiments. We empirically demonstrate that
                 performance of the SVM model is better than LR and
                 other machine learning techniques in all datasets.
                 Finally, we conclude that such methods can help in
                 reliability prediction using real-life failure
                 datasets.",
}

Genetic Programming entries for Pradeep Kumar Yogesh Singh

Citations