PredictPP: A Rank‐Based Weighted Ensemble Model for Prediction of Software Project Productivity

ABSTRACT Software effort estimation (SEE) determines the effort necessary to develop software. The researchers have been tending to SEE issues since the 1960s, and several methods have been created until the formulation of the function point (FP) and constructive cost estimation (COCOMO) methods. However, these methods are only useful for procedurally developed software, not modern object‐oriented (OO) software. Because the use case is the widely used unit of an OO system, particularly in scenarios requiring structured and early‐stage effort estimation, using the use case point (UCP) approach will help get accurate results. The UCP approach consists of size estimation (in UCP) and effort estimation with calculated size. This study focuses on effort estimation when the size (in UCP) is already known. The productivity of a project is one of the main components for estimating effort from the given size. The classical SEE models based on UCP utilized a fixed number of productivity values. So, the validity of classical approaches is a subject of disapproval because of static productivity values. Purposefully, we proposed a rank‐based weighted ensemble model for productivity prediction that allows us to use flexible productivity values. We used learning techniques such as simple linear regression (SLR), Least Absolute Shrinkage and Selection Operator Regression (LR), ridge regression (RR), elastic net regression (ER), K‐nearest neighbor (KNN), decision tree (DT), support vector regression (SVR), multilayer perceptron (MLP), bagging, and adaptive boosting for productivity prediction and compared them with the proposed model. Further, we used existing UCP prediction models and compared the proposed approach with them.