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.

本文探讨了一种新的用于软件项目生产力预测的集成构建机制，利用带权平均方法来汇聚基模型的预测结果，结果表明基模型准确性不一致时，使用集成方法是一种良好的替代方案。