Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915417092521984 |
|---|---|
| author | Cohoon, Michael Furman, Debbie |
| author_facet | Cohoon, Michael Furman, Debbie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone Cohoon, Michael Furman, Debbie Software Engineering Artificial Intelligence 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. |
| title | Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2507.22064 |