An AI-Driven Framework for Software Effort Estimation Based on Developer Performance Metrics

Effort estimations, including budgets, hiring people, and project timelines, in the Agile methodology, are determined by tools like COCOMO and Function-Point analysis. This study presents a framework driven by artificial intelligence (AI) that uses almost real-time signals from Git platforms that track issues, and tools to determine code quality, convert them into vectors, and trains four different regressors on them: ordinary least-squares regression, a random-forest ensemble, gradient-boosted trees, and a long short-term memory network. Hold-out evaluation together with five-fold cross-validation supplies mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination, complemented by feature-importance charts from the tree-based learners. A CI/CD-integrated retraining schedule keeps the estimator aligned with evolving team dynamics. Analyzing multi-developer projects over successive sprints reveals where predictions remain accurate and where unpredictable behavior emerges, pointing to chances for improved data gathering, enhanced governance, and more intentional feature development.