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

TL;DR

This paper narrates the journey of applying machine learning to enterprise software testing, emphasizing a clear, step-by-step workflow that enables anyone to implement ML techniques effectively.

Contribution

It presents a practical, easy-to-follow ML workflow tailored for software testing, making ML accessible for practitioners without extensive expertise.

Findings

The workflow is effective for applying ML in enterprise testing.

Anyone can adopt this workflow for their ML projects.

The process improves testing efficiency and accuracy.

Abstract

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.