A Review of Effort Estimation in Agile Software Development using Machine Learning Techniques

Nowadays organizations are heading towards the agile software development instead of traditional development models due to the flexibility and acceptance of change in the agile software development approach. Estimation of time, effort, and size of a project is a challenging task in agile software development due to the changing requirement during development. So to overcome the challenges in effort estimation different Machine Learning (ML) techniques such as Random Forest, Decision tree, SGB, and Neural Network models are used along with story points. In this paper, a literature review of all these ML techniques is carried out for effort estimation of agile projects.