Software development effort prediction of industrial projects applying a general regression neural network

  • Authors:

  • Cuauhtemoc Lopez-Martin;Claudia Isaza;Arturo Chavoya

  • Affiliations:

  • Information Systems Department CUCEA, Guadalajara University, Jalisco, Mexico;Department of Electronic Engineering-GEPAR Research Group, Universidad de Antioquia, Medellín, Colombia;Information Systems Department CUCEA, Guadalajara University, Jalisco, Mexico

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2012

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Abstract

An important factor for planning, budgeting and bidding a software project is prediction of the development effort required to complete it. This prediction can be obtained from models related to neural networks. The hypothesis of this research was the following: effort prediction accuracy of a general regression neural network (GRNN) model is statistically equal or better than that obtained by a statistical regression model, using data obtained from industrial environments. Each model was generated from a separate dataset obtained from the International Software Benchmarking Standards Group (ISBSG) software projects repository. Each of the two models was then validated using a new dataset from the same ISBSG repository. Results obtained from a variance analysis of accuracies of the models suggest that a GRNN could be an alternative for predicting development effort of software projects that have been developed in industrial environments.