International Journal for Innovative Engineering and Management Research

Software effort estimation is a crucial for development projects in both industrial software systems and digital transformation initiatives.Digital transformation involves integrating digital technologies to enhance operations and performance, while industrial software systems are specialized tools used in manufacturing processes.This paper presents a machine learning - based approach for accurate effort estimation, introducing on Omni-Ensemble Learning model.The OEL combines static ensemble selection, genetic algorithms, and dynamic ensemble selection to improve prediction accuracy.The study demonstrates that this model outperforms other machine learning methods when applied to Finnish and Maxwell datasets, delivering superior performance in software effort estimation.