This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation
