Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points

TL;DR

This paper introduces an ensemble method for predicting software project productivity using Use Case Points, demonstrating improved accuracy when base models are inconsistent across datasets.

Contribution

It proposes a novel weighted mean ensemble technique for software productivity prediction, enhancing accuracy over individual models.

Findings

Ensemble approach outperforms individual models in productivity prediction.

Weighted mean aggregation improves prediction accuracy.

Ensemble is effective when base models have diverse behaviors.

Abstract

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.