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Version: 2.0.0

How to perform Data Mocking using AI​

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:

  • 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:​

  • 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.
  • Enhanced flexibility: They can be used to test code with different types of data. This can help to ensure that the code is robust and that it can handle unexpected data.
  • Simplified debugging: Data mocks can be used to simplify debugging by providing a controlled environment for testing. This can help to identify the source of bugs more easily.

Challenges of using data mocks:​

  • Data accuracy: It is important to ensure that the mock data is accurate and that it represents the real data as closely as possible. This can be challenging, especially for complex data sets.
  • Data consistency: It is important to ensure that the mock data is consistent. This means that the data should be generated in a way that ensures that it is always the same.
  • Data maintenance: Mock data can be time-consuming to create and maintain. This is especially true for large data sets or for data that needs to be updated frequently.

Overcoming the challenges​

Keploy can generate dependency mocks in addition to the testcases by recording your application network calls making your testing process not only faster than unit tests but also incredibly efficient.

Since these data mocks are generated based on the real-time capturing of API calls from your application, they will be from real-world scenarios. This can help to ensure that the data mocks are accurate and that it represents the real data as closely as possible. As well as, it makes the data maintenance process easier by providing a same environment for testing. This can help to identify the source of bugs more easily.