The Complete Guide to Synthetic Test Data Generation

Complete Guide to Synthetic Test Data Generation

Have you ever used an app that crashed or froze unexpectedly? If so, then you will be familiar with the frustration you may face. Many users expect apps to work flawlessly and deliver a great customer experience. If they don’t, the customers might switch to a competitor or leave a bad review.

That’s why app developers need good-quality test data. Test data helps to check if the app works as expected. It can reveal bugs, errors, and glitches that might ruin the user experience. But creating test data is not easy. It takes a lot of time. Testers spend almost 50% of their time in data testing.

But don’t worry, here is a green flag, too. Because we’re living in an AI era so soon, there will be AI-powered testing tools to see. These tools will help increase the security, productivity, velocity, and quality of the software. According to a research report from 2021, 75% of QA experts said they will use AI to generate test data.

Sounds amazing? Tools like synthetic test data generators are now available.  These tools will be helpful not only in synthetic test data generation but also in saving time. Moreover, according to Gartner, by 2025, around 20% of all test data will be synthetically generated.

If you’re feeling curious, then read this article to end. We’ll discuss the complete guide to this synthetic data generation.

What is test data?

Test data is the input that we use to test a software application. However, the definition of test data varies as per test type.  Application testing has many parts to test. For example, we test the functionality, performance, security, usability, and more.

Unit testing is the first stage in the software development process. It’s about testing a small piece of code. Test data in unit tests consists of simple and small samples of test data. It covers all possible scenarios and edge cases.

Performance testing or load testing is about testing how fast and reliable an application is. Performance testing needs a lot of test data. While talking about all these stages, you must consider an important thing: never use production data in test environments. Why? Because it is the real data that users generate. Using it in test environments can cause many problems. It can expose sensitive information, break laws, and corrupt data.

Data masking is a technique to hide production data. But it is not enough. It can still leak data or affect the test results.

Mock data and AI-generated synthetic data are two options for test data generation. Both options can protect privacy and comply with regulations. So, choosing the right test data generation method is important. It depends on the type of test and the test objectives.

AI-Generated Synthetic Data Meaning

AI-generated synthetic data is generated by a powerful AI engine that can learn all the aspects of the dataset it is trained on and provide 100% test coverage. It has the same structure, patterns, and rules as real data. It is not fake or mock data. Plus, it shows how customers use products or services in real life as production data.

Moreover, it doesn’t require human input or effort. A good synthetic data generator can automate test data generation quickly and easily. It also doesn’t breach privacy policies.

A good synthetic data example is synthetic transaction data, which is based on real customer transactions and can be used to test mobile banking apps.  This data can show how customers use the app, what they buy, how much they spend, and so on.

What is synthetic test data generator?

Synthetic test data generator helps you test your software in different scenarios and industries. For example, you can use it for mobile banking, insurance, retail, and more. It also helps you improve the quality of your software and find bugs.

Again, as a reminder, synthetic test data is not the same as mock or fake data. Mock data is just random data that you use for testing. Mock data generators can be used in unit tests, but they are beneficial only in this case. They are not as helpful for testing other parts of the software.

You can find many AI-powered synthetic test data generators for online, in the cloud, or on-premise. When choosing a generator for synthetic datasets, consider these features:

  • It truly uses AI to create realistic and meaningful test data
  • It keeps the data structures and referential integrity of the real data
  • It has built-in privacy checks

What are test data types, challenges, and their synthetic test data solutions?

Here are some test data types, challenges, and solutions to help you get deep learning with synthetic data:

Valid Test Data

This is the data that is the mixture of all inputs.

Applications: Include integration, interface, system and regression testing.

Challenges: It’s difficult to cover all aspects of manual data generation. Keeping test data is also tough.

Solution: Generate synthetic data on the basis of production data.

Invalid test data

This is the data that is not allowed or possible for the software to process.

Applications: Include unit, integration, interface, system testing, and security testing

Challenges: It is not always simple to find error conditions to test because they are already unknown to you. Reaching out to production errors is important but will also not show previously unknown error conditions.

Solution: Create more different test cases with synthetic data on the basis of production data.

Huge Test Data

This is the big volume test data used for load and stress testing.

  1. Applications: Include performance testing and stress testing.
  2. Challenges: Big and different batches of data are insufficient. If you opt to multiply production data, it will not simulate all the parts of architecture properly.  It is not easy to create scripts that reproduce what real users do.
  3. Solution: Utilize synthetization to up sample production data.

Boundary test data

This is the data that exists at the upper or lower boundaries of expectations

Applications: Include reliability testing.

Challenges: The extreme data is not enough. We cannot tell what is possible and what is not for values within lower and upper boundaries. For example, prices or transaction amounts.

Solutions: Generate synthetic data by using creative mode or by using contextual generation.

What are the primary use cases for synthetic data?

Primarily, there are 2 use cases for synthetic data:

Software testing

Synthetic data is useful for testing software applications. It can help developers find bugs, errors, and vulnerabilities. Synthetic data can also simulate different scenarios and user behaviors. This way, developers can improve the quality and performance of their software as expected.

Machine Learning (ML) model training

Furthermore, synthetic data is helpful for training ML models. It can provide more data and diversity for the models. Synthetic data can also overcome the challenges of real data, such as privacy, cost, and availability. With synthetic data, ML models can learn better and faster.

Also, there are many other synthetic data use cases, such as:

Removes bias from real-world data

Synthetic data can help you avoid bias in your data. Bias can make your data unfair or inaccurate. For example, if your data only has people from one country, it may not represent other countries well. But synthetic data can create more diverse and balanced data. This can improve your data quality and fairness.

Product design

In addition, you can use synthetic data to test your products in different scenarios. For example, if you are making a car, you can use synthetic data to simulate how it drives in different weather, traffic, and road conditions. It can help you find and fix problems before you launch your product.

What are the advantages of synthetic test data generation?

There are many benefits of synthetic data, such as:


It lets you create data that matches your needs – few bugs and high reliability. You can test different scenarios and outcomes with synthetic data. It helps you improve the customer experience.


Moreover, it saves you time and money, which makes it important for agile software development. You don’t have to collect real data from various sources. AI can help you generate data within a few minutes. So, this factor can speed up your development and testing cycles.


Also, there is a built-in privacy mechanism that protects your customers’ private information. You can anonymize or mask your data with synthetic data. It reduces your risk of data breaches and compliance issues.


It gives you more control and options. You can customize your data to fit your specifications. You can scale your data to any size or volume. Synthetic data adapts to your changing requirements and goals.


Additionally, this data simplifies your data management and maintenance. You don’t have to clean, process, or update your data. You can automate your data generation and delivery with synthetic data. It frees up your resources and bandwidth.

Final Words

Testing is very important for software development. It helps you find and fix errors, improve quality, and meet user needs. But testing can also take a lot of time and resources. You need to create and manage a lot of test data for different scenarios. That’s why you should use AI-powered synthetic test data generation. It is a smart and fast way to create realistic and diverse test data. You don’t need to use real data or write complex scripts. All you need is to define your data model and let the AI do the rest. Using synthetic data generators, you can save time and money, protect user privacy, comply with regulations, and increase test coverage. Its use cases cover software testing and machine model learning.

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