Machine Learning Models for Predicting Software Project Delays in Large Development Teams

Abstract

Software development projects often experience schedule delays due to increasing system complexity, distributed teams, and evolving requirements, making accurate project planning difficult. Despite the availability of large volumes of development data from software repositories and project management systems, predicting schedule delays in large development teams remains a challenging problem. This study proposes a machine learning framework that analyzes development indicators such as team size, code churn rate, bug density, sprint backlog size, and requirement changes to predict project delay risks. Multiple machine learning models are trained using historical development data to identify patterns associated with schedule deviations. The results demonstrate that ensemble learning models provide reliable predictions of delay risks by capturing relationships between development indicators and project outcomes. The proposed approach enables early detection of scheduling issues and supports data-driven project planning in large software development environments.