A Stacking Ensemble Learning Model for Software Development Cost Estimation

Abstract

Estimating software costs is a vital step in guaranteeing the successful completion of a software project. Given the signiffcant impact of Functional Size (FS) measurement on obtaining accurate estimates for enhancement and development projects efforts, this study aims to investigate the use of FS as the key independent variable for predicting software project development costs. This is accomplished by utilizing ensemble models. The dataset used in this study came from the International Software Benchmarking Standards Group (ISBSG) repository. This research compares various single Machine Learning (ML) models and ensemble models learning using Grid Search (GS) tuning techniques to demonstrate the efffcacy of our approach. Using the FS of a new development request, the following observations were made: (i) The suggested Stacking Software Development Cost Estimation (StackSDCE) model outperformed the three distinct ML algorithms applied independently. (ii) The application of GS for ffne-tuning and conffguring the individual ML methods led to improved precision of the StackSDCE outcomes. (iii) StackSDCEbased GS tuning resulted in more precise estimations.