Abstract:
"
Story Points are the number one estimation metric for the modern age software
development industry. Proper estimations produce a proper sprint, Proper sprints
produce a proper and fruitful software development project. Estimates hold a great key
factor in an effective sprint. Each sprint planned in a project which is following Agile
methodology, is crucial to develop a successful software project. A story point is not
“just a number”. It contains the amount of work to do, the complexity and the risk
factors associated with the backlog item or the user requirement. However, in the
modern software development industry teams are using individual thinking to predict
story point. But this leads to many problems including under and over predictions which
are not accurate enough in the long run of the project.
Machine learning provides the ability for the machines to learn about data and carry on
tasks without obvious programming. It is one of the most inspired revolutions in the
history of the information technology industry. This artificial intelligence component
helps to solve many complex and sophisticated real world problems. Therefore,
Machine learning could be used to predict the story points in an effective way. This
research gives an overview of how Machine Learning techniques can be used to predict
story points accurately.
Using the new and advanced deep neural network BERT, several models were built and
tested their performance. The analysis and prediction is performed using just the text
description of the development items. The prediction model is inspired by the SkLearn
and Keras learning platforms. The results achieved in the development and testing are
quite exceptional and the system was evaluated by industry experts with positive and
promising feedback."
