Effort estimation of software project

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@Article{Merugu:2012:IJARCET,
  title =        "Effort estimation of software project",
  author =       "R Raja Ramesh Merugu and Venkat Ravi Kumar Dammu",
  journal =      "International Journal of Advanced Research in Computer
                 Engineering \& Technology",
  publisher =    "Shri Pannalal Research Institute of Technology",
  year =         "2012",
  keywords =     "genetic algorithms, genetic programming, SBSE, effort
                 estimation, fuzzy logic, particle swarm optimisation,
                 MMRE, neural networks",
  ISSN =         "22781323",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:65e54283cfc94cdfd7b789a43a65f1b0",
  URL =          "http://ijarcet.org/wp-content/uploads/IJARCET-VOL-1-ISSUE-10-33-41.pdf",
  URL =          "http://ijarcet.org/?p=1249",
  abstract =     "The effort invested in a software project is probably
                 one of the most important and most analysed variables
                 in recent years in the process of project management.
                 The limitation of algorithmic effort prediction models
                 is their inability to cope with uncertainties and
                 imprecision surrounding software projects at the early
                 development stage. More recently attention has turned
                 to a variety of machine learning methods, and soft
                 computing in particular to predict software development
                 effort. Soft computing is a consortium of methodologies
                 centering in fuzzy logic, artificial neural networks
                 and evolutionary computation. It is important to
                 mention here that these methodologies are complementary
                 and synergistic rather than competitive. They provide
                 in one form or another flexible information processing
                 capability for handling real life ambiguous situations.
                 These methodologies are currently used for reliable and
                 accurate estimate of software development effort which
                 has always been a challenge for both the software
                 industry and academia. The aim of this study is to
                 analyse soft computing techniques in the existing
                 models and to provide in depth review of software and
                 project estimation techniques existing in industry and
                 literature based on the different test datasets along
                 with their strength and weaknesses.",
}

Genetic Programming entries for R Raja Ramesh Merugu Venkat Ravi Kumar Dammu

Citations