Software Effort Estimation as a Multiobjective Learning Problem

Abstract

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.