Realistic data can help capture application errors that are often overlooked with fake test values. Realistic data is also helpful for validating that an app accepts only valid input.

However, accessing real production data can be difficult or expensive. And using copies of real data may expose personal information (PII). Row Gen enables testers to use realistic test data without the need for access to the data source.

Realistic Data

Whether you’re Testing an existing, or stress-testing a new, system, the quality of your test data is critical. It must represent good and bad production data, and conform technically and functionally to the characteristics of your production environment.

Using real data for tests is often called “production cloning.” However, if you’re copying production data, it is important to ensure that personally identifiable information (PII) such as credit card and social security numbers is masked and not exposed during the test process.

To avoid these concerns, it is often better to use realistic synthetic data that has been generated solely for testing purposes. This type of data is more suited to the test environment and allows for more accurate testing than real data.

Mock Data

For example, a unit test might require a mock database to simulate a function that writes data to the database. It allows the test to be decoupled from the implementation details of that database such as storing data on disk or establishing connections over network protocols. This reduces testing dependencies and makes it easier to change and maintain the code.

However, mocks can be difficult to manage and can create a testing environment that doesn’t replicate production. They can also add complexity and increase maintenance costs. Over-reliance on them can lead to tests that aren’t as accurate and can expose bugs that wouldn’t have been found otherwise.

To avoid these issues, it is recommended to use Synthetic data. This is a subset of big original production datasets that have been artificially generated and cleaned without any privacy-sensitive information. It works best for integration testing but can be used as a source of test data in UI testing to provide realism and expose bugs that dummy data couldn’t.

Artificial Intelligence (AI)-generated Synthetic Data

It’s hard to find and use realistic test data that mimics production. That’s why, Veeramachaneni explains, “AI-generated synthetic data is becoming a critical tool for testing.”

It can help developers get the results they want without having to move real customer data from one team to another. It also allows them to test new features at a faster pace, without putting sensitive data at risk.

In addition, AI-generated synthetic data can be used to test for human bias in a way that real-world data sets cannot. It can be a valuable supplement to existing data sets for training and testing AI algorithms, especially in situations where collecting new data may not be feasible, such as in self-driving cars or image recognition systems.

It’s important to choose a synthetic data generator that retains the data structures and referential integrity of the sample database while generating realistic, privacy-safe data. Luckily, there are several available options, including MOSTLY AI’s easy-to-use platform.

Privacy-safe Synthetic Data

Privacy regulations and security protocols often make it difficult to use real-world data for testing software. This can be a big problem, as it can delay the speed at which companies can test their software.

However, using synthetic data generation can help speed up the process and allow organizations to get valuable insights faster. In addition, using synthetic data also eliminates the need to follow lengthy compliance processes.

Synthetic data can be created in a way that preserves the properties and statistical information of the original real-world dataset. The most important property is that synthetic data cannot be used to learn information about a particular individual in any way. This is achieved by using generative models with differential privacy.

This approach has been shown to work for many different uses, including upsampling under-represented classes, training fraud/anomaly detection models, and evaluating outliers. In addition, re-identification risks for these types of synthetic data are well below the widely established standards.