Test data generation refers to the creation of synthetic test data in order to test the software. There are two ways to create this data: manually and automatically.
Because synthetic data mimics real-life environments so, DevOps and testing teams use it to ensure their software apps perform as expected. The teams create different testing scenarios using this data and then test the apps accordingly. This makes it easy to identify where the software has bugs or lacks.
You’d be familiar with the test data masking that keeps the sensitive information of real individuals safe. Test data generation is parallel to that. By using different algorithms, patterns, and rules, this method generates fake data. As a result, teams test the software in edge cases and under boundary conditions with tons of data.
Also, this test data can be used for unit testing, integration testing, acceptance testing, and system testing.
The modern-day QA and DevOps teams should use an effective test data generation tool. This will reduce costs, improve software quality, and save time.
What are Test Data Generation Challenges?
Got the basics of what is test data in testing? As a quick recap, it is data used to test software’s functionalities under different scenarios. To generate it, we either intervene manually or use tools.
Test data management is very important when providing the test environment with top-quality and fresh test data. But when real-life production data becomes test data, it requires to be:
- Complete, fresh, and reliable
- Obscured, effectively protecting the sensitive information
- Populated, to fulfill the distinct requirement of the development project
- Synthesized, when there is a need for more test data
- Compliant, to follow the data privacy rules
Companies are using synthetic test data not just because the clean production data is not readily available. The actual reason is the insecurity of users’ private data. Since synthetic data is fake, there is no fear of breaching privacy rules. That’s what urges everyone to use this approach to generate test data.
The new updates in data privacy regulations strictly inform companies about users’ sensitive data during testing. That means if someone leaks private data, they are more likely to face lawsuits.
So, this is too important in industries like healthcare, telecommunications, and financial services.
What are Test Data Generation Solutions?
These days, testing teams have to deliver quality results in a short time, while following the data privacy rules and at minimal cost. Keeping that in mind, teams turn to finding a test data generation solution based on either production or synthetic data.
Production test data
In production test data, companies use the data that already exists in their production databases. Just because this data belongs to the real users so, they process it with an eagle eye. For example, they properly mask it, create its separate subset, and ensure compliance with the privacy rules. So, test data management tools come in handy for both data masking and test data management purposes.
Synthetic test data
The term ‘synthetic’ means artificial or fake. So, synthetic test data is the artificially generated data. Though it is fake, still it looks similar to the company’s real data. Synthetic data is used when there is no production data. There are different methods to generate synthetic data. They are business rules, generative AI, and data cloning.
What are the 7 key considerations when selecting a test data generation solution?
Before you select a data generation solution, it’s important to consider these 7 key factors:
Speed
In this case, think about the selected option as:
- Will the selected option allow you to provision the data quickly?
- How much of your time will be saved using it?
Provisioning the synthetic dataset takes a short time. It’s just because it doesn’t need to be connected with multiple systems in production. The best thing is that after using this data, you can discard it without any fear of data leaking.
Cost
Companies should choose the solution if it fits in their budget. Here, they should weigh the ROI of their selected technology. What goes beyond the budget isn’t effective, right? The best tool gives the best of both worlds: data preparation and data masking.
Quality
Considering if the tool generates data quickly and is cost-effective isn’t enough. Think about quality, too. Because quality speaks volumes, so don’t overlook that. To achieve that, you should pay attention not only to unbiased, high-quality, and realistic data but also to whether the data works perfectly throughout the systems. That means an effective tool will provide you with data that exactly covers every test case requirement.
Security
As we have mentioned, privacy rules have been more strict than ever. So, real-world data that is more likely to expose the PII can put the companies in danger. Ineffective tools where masking might be compromising can lead to hefty fines. To avoid that, it’s important to look for inflight data masking tools that ensure precise masking.
Versatility
Different testing environments require distinct data formats to perform action. But when it comes to test data generation solution, it adapts according to every format. This way, you don’t need to spend on different environments and, as a result, saves your time. Plus, this adaptability feature helps to match different testing requirements. For example, volumes, population, verticals, CI/CD, and more.
Simplicity
The more a data generation process is user-friendly, the more it will be easier for the companies to reach their testing goals. Hence, with the availability of a self-service data generation solution, DevOps and testing teams provision the test data without relying on a centralized system. This way, they provision the data independently.
Scale
Scalability is another plus point when using this data generation solution. While production data offers high accuracy, it takes a lot of time to be modified and adapted. On the flip side, synthetic data may come up with less accuracy, but it can be transformed to your required type of data format and type in no time.
Business Entities: An Innovative Method to Test Data Generation
An entity-based test data management method uses the data schema of a business entity such as (Customer, Loan, Order, or any other business item in the tested applications). The method then unites all the properties of the entity throughout the system. Finally, it acts as a template with all that real data to create new data. Generative AI and user-defined business rules leverage this template to generate test data synthetically.
This generated data goes for inflight data masking to shield the sensitive information. After that, it is taken to the testing environment.
Entity-based synthetic test data appears as:
- Specific and complete – generated to cover each test case
- Accurate – following the given business rules
- Consistent – with relational integrity
- Dividable – with subsets as per various parameters in order to meet real-time data provisioning
- Ready to use – with test data readily available, through API or self-service portal