  It is well recognized that the project productivity is a key driver in
estimating software project effort from Use Case Point size metric at early
software development stages. Although, there are few proposed models for
predicting productivity, there is no consistent conclusion regarding which
model is the superior. Therefore, instead of building a new productivity
prediction model, this paper presents a new ensemble construction mechanism
applied for software project productivity prediction. Ensemble is an effective
technique when performance of base models is poor. We proposed a weighted mean
method to aggregate predicted productivities based on average of errors
produced by training model. The obtained results show that the using ensemble
is a good alternative approach when accuracies of base models are not
consistently accurate over different datasets, and when models behave
diversely.
