Revistas Institucionales

econdly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to guide future research directions and tool development toward the most accurate machine learning approaches for modelling software development effort, costs, and delivery schedules, ultimately contributing to more efficient and cost-effective software projects.