Software effort estimation as a multiobjective learning problem

Ensembles of learning machines are promising for software effort estimation (SEE), but need to be tailored for this task to have their potential exploited. A key issue when creating ensembles is to produce diverse and accurate base models. Depending on how differently different performance measures behave for SEE, they could be used as a natural way of creating SEE ensembles. We propose to view SEE model creation as a multiobjective learning problem. A multiobjective evolutionary algorithm (MOEA) is used to better understand the tradeoff among different performance measures by creating SEE models through the simultaneous optimisation of these measures. We show that the performance measures behave very differently, presenting sometimes even opposite trends. They are then used as a source of diversity for creating SEE ensembles. A good tradeoff among different measures can be obtained by using an ensemble of MOEA solutions. This ensemble performs similarly or better than a model that does not consider these measures explicitly. Besides, MOEA is also flexible, allowing emphasis of a particular measure if desired. In conclusion, MOEA can be used to better understand the relationship among performance measures and has shown to be very effective in creating SEE models.