Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming

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@Article{Aljahdali:2013:IJARAI,

• author = "Sultan Aljahdali and Alaa Sheta",
• title = "Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming",
• journal = "International Journal of Advanced Research in Artificial Intelligence",
• year = "2013",
• number = "12",
• volume = "2",
• pages = "52--57",
• keywords = "genetic algorithms, genetic programming, SBSE",
• publisher = "The Science and Information (SAI) Organization",
• bibsource = "OAI-PMH server at thesai.org",
• language = "eng",
• oai = "oai:thesai.org:10.14569/IJARAI.2013.021207",
• URL = "http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf",
• URL = "http://dx.doi.org/10.14569/IJARAI.2013.021207",
• size = "6 pages",
• abstract = "Software has played an essential role in engineering, economic development, stock market growth and military applications. Mature software industry count on highly predictive software effort estimation models. Correct estimation of software effort lead to correct estimation of budget and development time. It also allows companies to develop appropriate time plan for marketing campaign. Now a day it became a great challenge to get these estimates due to the increasing number of attributes which affect the software development life cycle. Software cost estimation models should be able to provide sufficient confidence on its prediction capabilities. Recently, Computational Intelligence (CI) paradigms were explored to handle the software effort estimation problem with promising results. In this paper we evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming (GP). One model uses the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model uses the Inputs, Outputs, Files, and User Enquiries to estimate the Function Point (FP). The proposed GP models show better estimation capabilities compared to other reported models in the literature. The validation results are accepted based Albrecht data set.",

}

Genetic Programming entries for Sultan Aljahdali Alaa Sheta

Citations