Prediction accuracy measurements as a fitness function for software effort estimation

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. 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.