Splitting User Stories Using Supervised Machine Learning

Abstract

User stories are a well-known tool for representing requirements. They define small fragments of the system and help in the developer’s daily work. When we talk about user stories, then splitting them into tasks is common. Many approaches can be used to split a user story into tasks but these all approaches are based on manual working. In this era, where everything is now becoming digitalized. User stories should move to the next phase as well. In this paper, we will implement a novel idea to split a user story into tasks atomically using machine learning. We have used four machine learning algorithms random forest, SVM, KNN, and decision tree (ctree) on three open-source projects from Jira. The dataset we have used for this thesis was imbalanced, so we have used ROSE (randomly over sampling examples) and SMOTE (Synthetic Minority Oversampling Technique) to make a balanced dataset. We have applied machine learning algorithms separately on each project and also all projects combined into one dataset and then made comparisons on the results.