Using improved analytical programming algorithm for effort estimation in software engineering

Abstract

This paper evaluates the usage of improved analytical programming algorithm for software effort estimation. The new model was generated by improved analytical programming and it was tested and compared with Karner's model to assess its properties. Least Absolute Deviation and random sub-sampling 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 40 %. Moreover, this work shows that improved analytical programming algorithm is feasible method for calibrating Use Case Points method. All results were evaluated by standard approach: visual inspection and statistical significance testing.