Hybrid intelligent systems for predicting software reliability

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Mohanty:2013:ASC,
  author =       "Ramakanta Mohanty and V. Ravi and M. R. Patra",
  title =        "Hybrid intelligent systems for predicting software
                 reliability",
  journal =      "Applied Soft Computing",
  volume =       "13",
  number =       "1",
  month =        jan,
  pages =        "189--200",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Software reliability, Multiple Linear Regression (MLR),
                 Multivariate Adaptive Regression Splines (MARS), Back
                 Propagation Neural Network (BPNN), Counter Propagation
                 Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy
                 Inference System (DENFIS), TreeNet, Group Method of
                 Data Handling (GMDH), Recurrent architecture and
                 ensemble model",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2012.08.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494612003626",
  abstract =     "In this paper, we propose novel recurrent
                 architectures for Genetic Programming (GP) and Group
                 Method of Data Handling (GMDH) to predict software
                 reliability. The effectiveness of the models is
                 compared with that of well-known machine learning
                 techniques viz. Multiple Linear Regression (MLR),
                 Multivariate Adaptive Regression Splines (MARS),
                 Backpropagation Neural Network (BPNN), Counter
                 Propagation Neural Network (CPNN), Dynamic Evolving
                 Neuro-Fuzzy Inference System (DENFIS), TreeNet, GMDH
                 and GP on three datasets taken from literature.
                 Further, we extended our research by developing GP and
                 GMDH based ensemble models to predict software
                 reliability. In the ensemble models, we considered GP
                 and GMDH as constituent models and chose GP, GMDH, BPNN
                 and Average as arbitrators. The results obtained from
                 our experiments indicate that the new recurrent
                 architecture for GP and the ensemble based on GP
                 outperformed all other techniques.",
}

Genetic Programming entries for Ramakanta Mohanty Vadlamani Ravi Manas Ranjan Patra

Citations