Synthetic Test Data Generation: Definition, Techniques and Benefits

Synthetic Test Data Generation for software testing has become a crucial step of the development process. QA teams need to test every aspect of the software, leaving no room for mistakes. Why? Because a minor bug is enough to damage a brand’s reputation.

For example, a bug in the checkout process will lead to payment failures for customers. And do you think customers will consider this platform reliable? Of course not!

For robust software testing, DevOps and testing teams need good production data. However, collecting production data is all about a time-consuming process. So, synthetic test data generation appears to be the best solution. Though, it’s a fake data creation process, it tests the software like real data in no time.

Read this article to learn about deep learning with synthetic data!

Difference between Production Data and Synthetic Data?

Before we start discussing the details, let’s first has a glance at what synthetic and production data are.

Production data

This is real data that comes from actual users or activities in a live system. Teams handle this data manually. It shows how the system is used in real life. Production data is important for testing how well the system works in everyday situations. Most important, this data contains private information, so it needs to be handled carefully.

Synthetic data

To get a clear meaning of synthetic data, consider it parallel to real data. That means it is an artificially generated dataset. However, it mimics the structure and properties of real data but isn’t from real users or activities. Synthetic data helps DevOps and testing teams make sure a system is performing in different situations with no risk of breaching personal identifiable information (PII).

Benefits of Synthetic Test Data

There are many benefits of synthetic data, but the major ones are:

1. It enhances data quality

In production data, there are more chances to get unreliable, inaccurate, and biased data.  These bottlenecks result in ineffective software testing.  That’s why using artificial data in this scenario becomes a game-changer. No chance of wrong data at all. It’s all about increasing data quality, variety, and balance.

On top of that, with synthetic data, you can automate various tasks to improve the quality of your test data, such as:

1. Label data in a standardized way
2. Remove the wrong records
3. Delete the duplicate data
4. Collate data from different places (even if they’re in different formats)

2. It increases scalability

The best benefit is that you can generate test data as much as you need. So, no matter how big or complex your testing needs are, you can easily scale up without worrying about running out of real data.

Let’s say you need real data of 100 customers, but the availability is only up to 10 customers.  Here, creating synthetic data shines. You can fill gaps by expanding synthetic datasets for analysis or testing purposes.

Also, most of the time, setting the criteria for generating fake data is easier than acquiring rule-based test data.

3. It protects the sensitive information of the data

The key consideration in the testing phase is to keep the sensitive information safe and secure. Therefore, teams use data masking technique in testing environments. There are multiple data masking tools, but they are not 100% reliable to keep the data intact.

On the other side, the tools that create synthetic data can keep sensitive information safe from leaks. This is just because they don’t use real, sensitive information during testing.  It’s all about having a security blanket for your testing process.

Types of Synthetic Test Data

There are four major types of synthetic data:

1. Valid Test Data

This is the data that matches with the intended data format.  Valid test data is used to verify that the system or application performs as per requirements.

For example:

• Date and time data
• Numeric data within specified ranges
• A valid email address

2. Invalid or Erroneous Test Data

Invalid is the data that mismatches with the intended data format. With this data, teams can check how the systems control errors.

For example:

• If a field expects a numerical value but receives a string instead,.
• An email without “.com”
• Providing a phone number in a format other than the expected one (e.g., using letters instead of numbers).

3. Huge test data

As the name suggests, huge test data is all about using data in bulk to check how your system works when handling load or stress.  Teams use this data to ensure that the system doesn’t crash when controlling large datasets.

For example:

• An e-commerce site containing customer information with millions of entries.
• There are thousands of gigabytes of images on the website

4. Boundary test data

This data type is used to check the ultimate capacity of the system. That means, teams give input data to the system beyond its capacity to ensure if it works correctly.

For example:

• If an app requires users to be between 18 and 65 years old, the boundary data would be either 17 or 66.
• Uploading files of sizes just below and above the maximum allowed size
• Providing the highest or lowest amount

Most common methods to Generate Synthetic Data

There are two common methods for synthetic test data generation in order to assess the performance and reliability of software:

· Deep generative models

Deep generative models such as Variationally Autoencoders (VAEs) and Generative Adversarial Networks (GANs) depend on Artificial Intelligence/Machine Learning algorithms. These algorithms are trained on real data to create synthetic data reflecting the realistic properties of the real data.

· Rules-based

On the other hand, in rules-based methods, data engineers and data analysts set some parameters to make synthetic data. This method works on specifically what you want to create. The best synthetic data example of rules-based fraud is creating fake credit card transactions to test a banking system. Here, the data scientists and engineers set some rules. For example, it is important to make sure transaction amounts are reasonable, dates fall within a certain timeframe, and transactions involve valid combinations of cardholders and merchants.

Synthetic Data Generation Techniques

Here are series of Synthetic data generation techniques

1. Determine software testing and compliance requirements

The first step is to ensure the information you have is crystal clear. You should determine the data type you need for a particular test. Also, provisional employees should follow the rules and regulations such as GDPR, HIPAA, or CCPA to avoid data breaches. Also, it is important to have test data management tools to generate synthetic data on the spot.

2. Choose the right synthetic test data generation model

Next, you should choose the data generation models that seem best fit for your requirements. There are different models to choose from, such as Variationally Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), and various diffusion models. Each one comes with distinct purposes and requires different level of technical expertise. So, you should ensure that your TDM platform supports the chosen model.

3. Create the initial dataset

Whatever technique you use to create synthetic data requires real data samples, right? That’s why you need to choose test data from the production dataset that is reliable and has quality data. The more reliable the sample you have, the more quality synthetic data results will it generate.

4. Build and train the algorithm

Then, you should implement the chosen model structure and fine-tune its parameters to train on the patterns of the production data sample.

5. Evaluate your synthetic data

Finally, you’ll have the synthetic data, but before employing it, you should evaluate its quality and effectiveness to produce similar results as the original data. Furthermore, you can go for manual inspection, statistical analysis, and training runs.

Final Words

Synthetic test data generation has become the best solution for testing the software. It is a process of creating fake data that mimics the realistic properties of the production data. DevOps and testing teams use this data to test different aspects of the software to improve its performance. Unlike production data, where the chances of data breaches are high, this artificial data ensures that sensitive information is safe. However, to generate it, provisions need to follow a series of steps. For example, first, they should find out the data type for a particular test, second, they should ensure compliance with rules and regulations, third, they should select the model to generate the data, fourth, they need to create an initial data set, fifth, they should build and train the chosen model, last, they should evaluate the data’s quality and implement it.