Effective Software Effort Estimation enabling Digital Transformation

Software effort estimation is a necessary component of software development projects that belong to industrial systems and digital transformation initiatives. Digital refers the process integrating technology into various components company or organization in order improve operations, procedures, customer experiences, overall performance. Industrial are trained packages designed for use manufacturing processes. The paper deals with machine learning based create an effective robust model predicting effort. proposes Omni-Ensemble Learning (OEL) approach, which combination static ensemble selection along genetic algorithm dynamic selection. identifies impact system, works on these attributes implement model. proposed Selection (OES) provides better performance (in terms evaluation metrics) comparing multiple models over Finnish Maxwell datasets.