# Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone

arXiv: https://arxiv.org/abs/2507.22064 · HTML: https://mlsanity.com/paper/2507.22064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Abstract (arXiv)

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.