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Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points
lib:98a49a417ea561bb (v1.0.0)
Authors: Mohammad Azzeh,Ali Bou Nassif,Shadi Banitaan,Cuauhtemoc Lopez-Martin
ArXiv: 1812.06459
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1812.06459v1
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.
ArXiv: 1812.06459
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1812.06459v1
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.
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