A Neoteric Review of Story Point Estimation in Agile-Scrum Projects Using Machine and Deep Learning Algorithms
Shivali Chopra, Arun Malik · 2024
In recent years, machine and deep learning approaches have become increasingly popular for assisting with software effort estimation, and many organizations are beginning to implement this practice in their projects. In this paper, we have articulated various research questions and reviewed the most recent studies in the field of scrum estimation. The review’s most striking finding is that moving from waterfall to agile software development necessitates switching from a traditional to a continuous estimation approach. Due to various people and project-related factors, it’s clear that there is no “one size fits all” strategy for making estimates. Recent advancements in estimation models include “end-to-end trainable” and “Explainable AI-driven estimation. The transition from point-based estimation to category-based estimation has also been seen in practice, and software development estimation is not intended to anticipate how long a task will take but rather its complexity.