Abstract

Recent research and studies focus on improving the accuracy of software project effort estimation by employing Agile methodology and machine learning and deep learning techniques, such as neural networks and convolutional networks. (CNN) and use of initial optimization techniques. Also relying on Story Points as a tool for estimating and estimating software effort, this research will utilize and study several recent studies related to the subject, and this study proposes an ensemble-based machine learning framework for accurately calculating the effort required to complete user stories in Agile software development. Effort estimation in Agile software development must not only focus on resource allocation but also on safeguarding sensitive data, ensuring system security throughout the software lifecycle. Textual data from user story titles are transformed into numerical vectors using advanced feature extraction techniques such as TF-IDF. The framework encompasses strong security practices, such as data encryption, access control, and identity management, to provide confidentiality and integrity of the data while the model is working. Multiple regression models (MLP, SVR, and Linear Regression) are used and combined through ensemble learning for improved prediction accuracy. Evaluation metrics (MAE, RMSE, SMAPE, and R²) are used to validate the usefulness of the model. Several security-enhanced metrics capitalize on contemporary machine learning methods to ensure privacy and trust of Agile teams with sensitive project data. The methodology allows Agile teams to automate story point estimation to improve sprint planning and resource allocation.