Improved estimation of software project effort using multiple additive regression trees

  • Authors:

  • Mahmoud O. Elish

  • Affiliations:

  • Information and Computer Science Department, King Fahd University of Petroleum and Minerals, P.O. Box 1082, Dhahran 31261, Saudi Arabia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Accurate estimation of software project effort is crucial for successful management and control of a software project. Recently, multiple additive regression trees (MART) has been proposed as a novel advance in data mining that extends and improves the classification and regression trees (CART) model using stochastic gradient boosting. This paper empirically evaluates the potential of MART as a novel software effort estimation model when compared with recently published models, in terms of accuracy. The comparison is based on a well-known and respected NASA software project dataset. The results indicate that improved estimation accuracy of software project effort has been achieved using MART when compared with linear regression, radial basis function neural networks, and support vector regression models.