Estimating Software Development Efforts: The Role of Machine Learning in Enhancing Predictive Accuracy

Accurate estimation of software development efforts is critical for effectively managing project timelines, budgets, and resources. Traditional estimation methods often rely on expert judgment or historical data and can be prone to biases and inaccuracies. However, the emergence of Machine Learning (ML) provides a more objective, data-driven approach to estimating development efforts. This paper explores how Machine Learning models can improve the precision and reliability of software effort estimation, offering a comparative analysis of these models, their strengths, and the challenges they pose.

Keywords:

Software development , effort estimation , Machine Learning , predictive models , regression analysis , neural networks , data-driven approaches