Using analytical programming for software effort estimation

Abstract

This paper evaluates the usage of analytical programming for software effort estimation. Analytical programming and differential evolution generate regression models. The new model was generated by analytical programming and it was tested and compared with Karner’s model to assess insight to its properties. Mean Magnitude of Relative Error and k-fold cross validation were used to assess the reliability to this experiment. The experimental results shows that the new model generated by analytical programming outperforms the Karner’s equation about 12% MMRE. Moreover, this work shows that analytical programming method is viable method for calibrating Use Case Points method. All results were evaluated by standard approach: visual inspection and statistical significance testing.