Estimation Approaches of Machine Learning in Scrum Projects

It is impossible for a prosperous IT sector to avoid underestimating the amount of work, the amount of money, and the amount of time their projects would take. The introduction of Agile, (PBPM)principle-based process models, such as Scrum, brought about a substantial shift in the industry. This shift in culture proves to be extremely beneficial in terms of enhancing the collaboration that exists between the customer and the developer. In Agile development, estimation has always been difficult since requirements are subject to change. This motivates researchers to work on developing more accurate methods of effort estimation. The disparity between the amount of work that was estimated and the amount that was actually done. In this publication, a review was conducted of the work done by a large number of authors and potential researchers who were striving to close the gap between the real and estimated amount of effort. A thorough literature analysis led to the conclusion that machine learning models outperformed traditional estimation methods and methods that did not incorporate machine learning.