Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Updated Data Mocks using AI page #475

Merged
merged 2 commits into from
Nov 11, 2024
Merged

Updated Data Mocks using AI page #475

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
699b8c3
Updated Data Mocks using AI page
Swpn0neel Nov 3, 2024
8c13ad2
updated data mocks definition
Swpn0neel Nov 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions versioned_docs/version-2.0.0/concepts/reference/glossary/mocks.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,14 @@ keywords:

## What are data mocks?

Mocks or Data mocks are fake data that is used to simulate real data in a controlled environment. They are often used in software development to test code that interacts with data, such as APIs and databases. Data mocking can be used to:
Data mocks refer to the synthetic data that mimics real-world datasets, which is useful for testing, training machine learning models, and simulating data flows without relying on actual production data. They can also be generated with the help of AI. Data mocks are often used in Software Development to test code that interacts with data, such as APIs and databases. Data mocking can be used to:

- Test code that is not yet connected to a real data source.
- Test code that is expected to handle errors or unexpected data.
- Test code that is expected to work with different types of data.
- Speed up the testing process by avoiding the need to wait for real data to be loaded.

## Here are some of the benefits of data mocking:
## What are the benefits of data mocking?

- **Increased test coverage**: Data mocks can be used to test code that is not yet connected to a real data source. This can help to increase the test coverage of the code, and it can help to identify bugs that would not be found if the code was only tested with real data.
- **Improved testing speed**: Data mocking can be used to speed up the testing process by avoiding the need to wait for real data to be loaded. This can be especially beneficial for large data sets or for tests that need to be run repeatedly.
Expand Down
Loading