Story and Task Issue Analysis for Agile Machine Learning Projects

The usage of Agile methodology in planning and executing machine learning (ML) and data science related software engineering projects is increasing. However, there are very few studies using real data on how effective such planning is or guidelines on how to plan such projects. In this paper, we analyze data taken from several software projects using Scrum tools. We compare the data for data science/ML and non-ML projects, in an attempt to understand if data science and ML projects are planned or executed any differently compared to normal software engineering projects. We also perform a story classification task using machine learning to analyze story logs for agile tasks for several teams. We find there are differences in what makes a good ML story as opposed to a non ML story. After analyzing this data, we propose a few ways in which software projects, whether machine learning related or not, can be better logged and executed using Scrum tools like Jira.