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Improved effort estimation of heterogeneous ensembles using filter feature selection

Abstract:

Estimating the amount of effort required to develop a new software system remains the main activity in software project management. Thus, providing an accurate estimate is essential to adequately manage the software lifecycle. For that purpose, many paradigms have been proposed in the literature, among them Ensemble Effort Estimation (EEE). EEE consists of predicting the effort of the new project using more than one single predictor. This paper aims at improving the prediction accuracy of heterogeneous ensembles whose members use filter feature selection. Three groups of ensembles were constructed and evaluated: ensembles without feature selection, ensembles with one filter, and ensembles with different filters. The overall results suggest that the use of different filters lead to generate more accurate heterogeneous ensembles, and that the ensembles whose members use one filter were the worst ones.