University of Birmingham > Talks@bham > Computer Science Lunch Time Talk Series > Software Effort Estimation as a Multi-objective Learning Problem

Software Effort Estimation as a Multi-objective Learning Problem

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Ensembles of learning machines have been showing to be promising for software effort estimation (SEE), but still 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. Different performance measures behave differently when analysing SEE models. Depending on how differently differently they behave, they could be used as a natural way of creating diverse SEE ensembles. We propose to view SEE model creation as a multi-objective learning problem. A multi-objective evolutionary algorithm (MOEA) is used to better understand the trade-off 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. So, we use them as a source of diversity for creating SEE ensembles. A good trade-off 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.

This talk is part of the Computer Science Lunch Time Talk Series series.

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