Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
It provides a practical guide for anyone, especially in software testing, to implement ML, but it is incremental as it adapts existing processes without introducing new methods.
The paper describes a group's journey in applying machine learning to enterprise software testing, detailing a workflow similar to CRISP-DM that includes steps like data gathering, cleaning, feature engineering, model training, and evaluation, aimed at enabling effective ML application in any project.
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.