This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project: gather the data; clean the data; perform feature engineering on the data; splitting the data into two sets, one for training and one for testing; choosing a machine learning model; training the model; testing the model and evaluating the model performance. By following this workflow, anyone can effectively apply ML to any project that they are doing.
@article{arxiv.2507.22064,
title = {Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone},
author = {Michael Cohoon and Debbie Furman},
journal= {arXiv preprint arXiv:2507.22064},
year = {2025}
}