A test data generator is a software tool that automatically creates synthetic, safe, and realistic data for software development and testing.

Table of Contents

What is Test Data Generator?
Test Data Generator Challenges
Test Data Generator Solutions
7 Considerations for Choosing a Test Data Generator
Business Entities: A New Approach to Test Data Generation

What is a Test Data Generator?

A test data generator is a program that automatically creates realistic but synthetic test data for software development and testing purposes. DevOps and testing teams use the generator to simulate lifelike scenarios to make sure that the software application performs as expected under different conditions.

Unlike test data masking, which obscures the Personally Identifiable Information (PII) of real people, the test data generator integrates algorithms, patterns, and rules, to generate fake data that can be stress-tested under boundary conditions, in edge cases, with massive volumes of data, or invalid data.

The generated test data can be used for acceptance testing, integration testing, system testing, and unit testing. It helps identify issues early on in the software development cycle and ensures that the software application is robust and reliable.

For today’s DevOps and testing teams, a test data generator is indispensable for improving software quality, reducing costs, and saving time and resources.

Test Data Generator Challenges

Today's data teams understand the importance of test data management, especially when it comes to provisioning test environments with fresh, high-quality test data, on demand. But for real-life production data to become test data, it must be:

• Complete, fresh, and trustworthy
• Masked, effectively hiding personal information
• Populated, to meet the requirements of the development project
• Synthesized, when additional test data is required
• Compliant, to address data privacy legislation

Besides the overall lack of clean and available production data, data privacy compliance is a key driver in synthetic data generation for the very reason that the data is not real.

Recent developments in data privacy regulations are forcing companies to be far more careful in ensuring they do not expose sensitive information through their testing practices. This is particularly relevant in industries like telecommunications, financial services, and healthcare, which encounter a multitude of Data Subject Access Requests (DSARs) on a daily basis.

Test Data Generator Solutions

Today’s testing teams are tasked with delivering high-quality results, on time, in compliance with privacy regulations, at minimal cost. These demands often lead them to seek a test data generator solution based on production or synthetic data.

Production test data
In this case, the enterprise uses data already in its production databases, processing it to ensure that it is properly masked and subsetted, to comply with legal and organizational requirements. Test data management tools are recommended for both test data management and data pipeline purposes.

Synthetic test data

As the name suggests, this type of test data is artificially generated, but closely mimics the attributes of the company’s real data. Synthetic data, which is used when there is insufficient production data, is safer for use in data governance tools.

7 Considerations for choosing a test data generator

When choosing a test data generator, consider the following 7 factors:

1. Speed

Will the chosen approach enable you to provision data faster? How much time will you save? A synthetic dataset can often be provisioned more quickly since it doesn’t require access to multiple systems in production. And when the data is no longer needed, it can be discarded without worrying that it might expose any user information.

2. Cost

A test data generator is only really effective, when it’s cost-effective. Enterprises must always consider the bottom line and measure the ROI of their chosen technologies. A test data generator responsible for preparing and also masking data on the fly, can be doubly efficient.

3. Quality

It’s not just a matter of producing test data faster, and at a lower cost. You want your test data to be realistic, balanced, and high-quality. You also want the test data to maintain its referential integrity. You want a test data generation solution that delivers precisely the data you need to ensure 100% coverage of your test cases.

4. Security

As mentioned, data privacy issues top organizations’ list of priorities for a reason. Real-world data that might expose users’ information puts the entire company at risk, therefore superior data masking tools are required. Any masking hiccups might result in stiff penalties, as well as damage to your reputation.

5. Simplicity

A user-friendly test data generation process helps enterprises reach their test data goals more easily. A self-service, rule-based test data generator allows testing teams to provision data independently, without having to rely on one centralized system that only a few can operate. In the era of agile development, this is a must.

6. Versatility

Different testing environments demand different data formats, and the test data generator's ability to adjust accordingly can help cut costs and prevent delays. The more adaptable your test data generation system is, the easier it'll to match testing needs like population volumes, verticals, CI/CD data pipeline tools, and more.

7. Scale

Test data generation at enterprise scale is another critical capability. Production data is spot on, but needs to be transformed and adapted, which can take time. Synthetic data creation may be a bit less accurate, but can accommodate a wide range of data types and formats, to suit your needs.

Business Entities: A New Approach to Test Data Generation

An entity-based test data management approach, where a data schema unifies all the entity’s data attributes, across all systems, and new data is generated according to user-defined business rules. The test data from these business entities (customers, suppliers, or orders), when persisted in a test data warehouse, can be secured with data masking on the fly, and then delivered to any testing environment on demand.

Entity-based synthetic test data is:

• Specific and complete – generated per test case to ensure 100% coverage

• Accurate – with data generated according to predefined business rules

• Consistent – with relational integrity an integral part of every entity schema

• Divisible – with subsets based on different parameters, for real-time data provisioning

• Available for use – with test data ready on demand, via API or self-service portal