Evaluation of GP Model for Software Reliability

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@InProceedings{Paramasivam:2009:ICSPS,
  author =       "S. Paramasivam and M. Kumaran",
  title =        "Evaluation of GP Model for Software Reliability",
  booktitle =    "2009 International Conference on Signal Processing
                 Systems",
  year =         "2009",
  month =        may,
  pages =        "758--761",
  keywords =     "genetic algorithms, genetic programming, SBSE, GP
                 model, fault count data prediction, industrial project,
                 software metrics, software quality, software
                 reliability growth model, software metrics, software
                 quality, software reliability",
  DOI =          "doi:10.1109/ICSPS.2009.104",
  abstract =     "There has been a number of software reliability growth
                 models (SRGMs) proposed in literature. Due to several
                 reasons, such as violation of models' assumptions and
                 complexity of models, the practitioners face
                 difficulties in knowing which models to apply in
                 practice. This paper presents a comparative evaluation
                 of traditional models and use of genetic programming
                 (GP) for modeling software reliability growth based on
                 weekly fault count data of three different industrial
                 projects. The motivation of using a GP approach is its
                 ability to evolve a model based entirely on prior data
                 without the need of making underlying assumptions. The
                 results show the strengths of using GP for predicting
                 fault count.",
  notes =        "Also known as \cite{5166890}",
}

Genetic Programming entries for S Paramasivam M Kumaran

Citations