Key points are not available for this paper at this time.
The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on the observation that the effort estimation problem can be formulated as an optimization problem. Indeed, among the possible models, we have to identify the one providing the most accurate estimates. To this end a suitable measure to evaluate and compare different models is needed. However, in the context of effort estimation there does not exist a unique measure that allows us to compare different models but several different criteria (e.g., MMRE, Pred(25), MdMRE) have been proposed. Aiming at getting an insight on the effects of using different measures as fitness function, in this paper we analyzed the performance of GP using each of the five most used evaluation criteria. Moreover, we designed a Multi-Objective Genetic Programming (MOGP) based on Pareto optimality to simultaneously optimize the five evaluation measures and analyzed whether MOGP is able to build estimation models more accurate than those obtained using GP. The results of the empirical analysis, carried out using three publicly available datasets, showed that the choice of the fitness function significantly affects the estimation accuracy of the models built with GP and the use of some fitness functions allowed GP to get estimation accuracy comparable with the ones provided by MOGP.
Sarro et al. (Mon,) studied this question.