Data-intensive applications often require hyper-realistic test data to perform correctly. However, real production data is not always available or usable for testing purposes due to security and quality factors.
Synthetic data allows you to protect sensitive information while still meeting your application development goals. It is also ideal for new applications where no production data exists.
The ability to generate realistic data can be much more than rows in a database. The technology can create anything from dinosaurs listening to NPR and a legal affairs correspondent dunking a basketball in space, to realistic images that mimic natural lighting. This type of data generation is called generative model training, and it’s used by artificial intelligence and natural language processing systems to generate new, realistic content for use in machine learning models.
Realistic, high-quality test data is a key component for system testing. It’s critical to ensure maximum test coverage for any complex interconnected system and eliminate the risk of data leaks. This is especially important when real production data has usage constraints due to privacy rules or regulations.
This type of synthetic data generation is also a powerful tool for security testing. It allows testers to practice the correct responses to security tests without exposing real client data. GenRocket provisions synthetic test data by business entity, allowing testing and DevOps teams to create fake data for any application or legacy database.
Many application scenarios require large volumes of diverse and realistic data. And that’s not easy to find in real production data sets. That’s where artificial data becomes a powerful ally for testing.
But creating test data on the fly takes time and resources. And masking data to make it usable can introduce new biases and artifacts.
Fortunately, AI-powered synthetic data generation solutions are available that can address these challenges. The solution Mostly AI enables developers to generate scalable and diverse test data using production data while keeping sensitive information private.
Mostly AI’s privacy-preserving technologies automatically obfuscate or anonymize any information that could compromise security and compliance. Moreover, the tool can create a dynamic and stateful synthetic data set that intelligently replaces sensitive data for different workflow scenarios.
Watch the solutions video Testing a Bank ATM Web Services Workflow with GenRocket to see this in action. Whether you’re testing under heavy load or high traffic, it’s vital to have accurate test data that accurately simulates the complexity of production scenarios.
Often, real production data cannot be used for testing software due to privacy, security, or lack of availability. Using synthetic data is a way to overcome these limitations without compromising privacy or risking noncompliance.
This process entails creating data that matches the statistical aspects of actual datasets, such as patterns and distributions. This is especially helpful for test cases that require a specific data look-alike. For example, generating demographic data is useful for testing new apps and eliminating the risks associated with using real customer data.
GenRocket allows testers to intelligently replace sensitive production data with controlled synthetic data for all of their testing scenarios. This process is flexible and dynamic to support complex workflows. Watch this solutions video to see how GenRocket supports the entire process of defining data MODELs and generating the precise, modeled data that is needed for each testing situation. GenRocket is also able to generate large, high-quality data sets for a variety of formats such as SQL and XML.
Privacy-friendly synthetic data is critical to ensure that sensitive or personal information is not exposed during testing or analysis. It also helps prevent data leaks or breaches and is an essential safeguard for regulated industries such as healthcare, financial services, and insurance.
VARIETY: Some test cases require specific data patterns that can’t be found in production data (e.g. a null value, illegal password character, or incorrect computation). Whether for the purposes of UI testing or scalability testing, synthetic data can easily be designed to meet these needs.
ACCURACY: Other times the test data must accurately represent a value that is central to the software being tested. For example, medical procedure codes need to be correctly modeled to test a claims processing application.
The GenRocket self-service synthetic data design platform makes it fast and easy to generate unique, randomized, or sampled data for these tests. This allows Agile teams to rapidly design and automate their test data as they develop test cases during each sprint and then schedule them for automated execution in the CI/CD pipeline.