Enhancing the performance of software effort estimation through boosting ensemble learning

Software effort estimation is a major component of the software development cycle, playing a crucial role in the outcome of a project. Comprehending the volume and effort of a software project in the early stages is not a trivial problem, but a necessary step since both over and underestimates can lead to client dissatisfaction, thus a low-quality product, and in some cases, even project failure. This paper investigates whether using a boosting ensemble learning approach for the problem at hand contributes to enhancing the performance of estimating the software development effort. An increase of about 18% in terms of Mean Squared Error and 88% in ${R}^{2}$ performance metrics has been achieved by the boosted model compared to the classic one, without boosting.