Software effort estimation by tuning COOCMO model parameters using differential evolution

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@InProceedings{Aljahdali:2010:AICCSA,
  author =       "Sultan Aljahdali and Alaa F. Sheta",
  title =        "Software effort estimation by tuning COOCMO model
                 parameters using differential evolution",
  booktitle =    "2010 IEEE/ACS International Conference on Computer
                 Systems and Applications (AICCSA)",
  year =         "2010",
  month =        "16-19 " # may,
  address =      "Hammamet, Tunisia",
  abstract =     "Accurate estimation of software projects costs
                 represents a challenge for many government
                 organisations such as the Department of Defense (DOD)
                 and NASA. Statistical models considerably used to
                 assist in such a computation. There is still an urgent
                 need on finding a mathematical model which can provide
                 an accurate relationship between the software project
                 effort/cost and the cost drivers. A powerful algorithm
                 which can optimise such a relationship via tuning
                 mathematical model parameters is urgently needed. In
                 two new model structures to estimate the effort
                 required for software projects using Genetic Algorithms
                 (GAs) were proposed as a modification to the famous
                 Constructive Cost Model (COCOMO). In this paper, we
                 follow up on our previous work and present Differential
                 Evolution (DE) as an alternative technique to estimate
                 the COCOMO model parameters. The performance of the
                 developed models were tested on NASA software project
                 dataset provided in. The developed COCOMO-DE model was
                 able to provide good estimation capabilities.",
  keywords =     "genetic algorithms, genetic programming, sbse, COOCMO
                 model parameter tuning, NASA software project dataset,
                 constructive cost model, differential evolution,
                 mathematical model, optimisation algorithm, software
                 effort estimation, software projects cost estimation,
                 statistical model, optimisation, software cost
                 estimation",
  DOI =          "doi:10.1109/AICCSA.2010.5586985",
  notes =        "'We suggest the use of Genetic Programming (GP)
                 technique to build suitable model structure for the
                 software effort estimation.' Also known as
                 \cite{5586985}",
}

Genetic Programming entries for Sultan Aljahdali Alaa Sheta

Citations