Your software and application tests are only as good as your data — which is why test data creation is such a crucial component to any organization’s overall testing strategy.
As your tests evolve from simple to complex, your data will inherently evolve with it. Your data will need to reflect real-world scenarios, be highly similar to production data, and likely be refreshed after each test run to maintain accuracy.
When it comes to continuous testing — a state of testing that every team should be striving for — a robust test data creation tool will dramatically increase efficiencies while also saving on time and cost.
In this blog, we will discuss what test data creation is, requirements for test data creation, how to meet those requirements, and what to look for when selecting a test data creation tool.
What is Test Data Creation?
Test data creation is the process of manually or automatically creating synthetic test data that mimics real data to use for software testing. Test data creation is used for simulating realistic scenarios for tests cases to determine how an application performs under varying conditions.
Because using real data involving real people can pose a significant security risk, test data creation leverages algorithms, rules, and patterns to create fake data that can be stress tested in large volumes.
When done correctly, the test data that is created can be used for a variety of types of testing — such as integration testing and unit testing. That is a prominent reason why selecting an effective test data creation tool can improve software quality while also reducing costs.
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Test Data Creation Requirements
In today’s current landscape of software testing, the growing complexity of test data places more importance on test data management. It has never been more critical for data to be both high-quality and as up-to-date as possible.
Throughout your test data creation efforts, you should ensure that your data is:
Populated: Meets the requirements of test cases established throughout development.
Complete: Freshly created data that covers everything you need for a given test.
Synthesized: Incorporates seamlessly with any additional data required.
Masked: Hides any identifying personal information.
Compliant: Adheres to any relevant data privacy or security regulations.
As regulations concerning data security and privacy become more stringent, a test data creation tool for producing synthetic test data becomes more and more important. When you utilize realistic yet fake data, you significantly reduce the risk of exposing sensitive information — particularly important for the financial services and communications sectors.
How to Meet Test Data Creation Requirements
Depending on the type of test data your team is relying on — production or synthetic — your use of a test data creation tool may vary. Below are the types of data that a test data generation tool can be used for.
Synthetic Test Data
This artificial data closely resembles (but is not actually) a company’s real data. This type of test data is typically used when production data is not available and can be created using a variety of methods — AI, data cloning, and beyond.
Production Test Data
Production test data is data already in use by an enterprise. Since it is real-life data, there are steps in place to ensure that it is properly masked and that it complies with any applicable legal regulations. A test data creation tool would thus be helpful in adhering to those regulations.
Learn more about synthetic test data vs. production test data masking.
What To Look For in a Test Data Creation Tool
There are a number of test data creation tools on the market, so which one do you choose Below are several test data creation tool attributes to consider before making your selection.
Quality
As the adage goes, quality always beats quantity. That remains the case when it comes to test data. It does not matter how much test data you have if it is faulty or incomplete. Your test data creation tool should be able to produce the exact data you need to cover every one of your test case scenarios.
Speed
One of the main benefits of your test data creation tool should be the speed at which it provides you with your data. That is another reason why synthetic data can come in handy: since it is fake, you can get rid of the dataset when it is no longer needed without the risk of exposing any important information.
Security
Speaking of important information, your tool should be aligned with every applicable data privacy regulation. You can avoid security risks and a potentially damaged reputation by avoiding real-world data in favor of synthetic test data — or, at the very least, masked data.
Ease-of-Use
A test data creation tool that is easy to use goes a long way in translating to testing efficiency. This means the tool should allow members of the testing team to produce data independently and without having to rely on a cumbersome system only a few members have the knowledge to use.
Scalability
As your testing efforts grow, the importance of scalability comes into play. Your test data creation tool should be able to scale with your testing. At enterprise scale, your production data may be fresh and accurate, but it becomes more difficult to handle. Consider a tool that can provision both production data and synthetic data to account for a wide range of testing types.
Versatility
Test data goes hand-in-hand with test coverage, so your test data creation tool should be able to account for several different testing environments. That means having test data that can adapt to different fields, industries, system integrations, and beyond.
Cost
The primary objective of using a test data creation tool is to save time and, ultimately, money. An enterprise must always keep the bottom line in mind when choosing a tool. Ensuring the testing ROI is a positive one can be a critical factor.
Using AI for Test Data Creation
While there are several test data creation tools out there to choose from, only BlazeMeter offers one that leverages the latest advancements in AI.
With Test Data Pro, users can transform how their test data works for them. In addition to accelerating their testing cycles, teams can elevate application quality with:
AI-Driven Test Data Creator: Test data creation is streamlined by converting text to test data functions.
AI-Driven Data Profiler: Automatically generate additional data & rapidly identify hardcoded data.
AI-Assisted Test Data Function Generator: Ditch manual coding & instantly generate test data functions with natural language.
Chaos Testing: AI-powered test data enhances system resilience by challenging systems & identifying vulnerabilities.
Global communications giant BT Group saved millions by transforming their testing with BlazeMeter’s AI-Driven Test Data. See how they achieved such remarkable results by reading the full case study.
Bottom Line
Having high-quality, realistic test data plays a major role in achieving a state of continuous testing — a mode of testing that improves application quality and testing efficiency across the board.
That is why having a proper test data creation tool is so important. It ensures that your test data covers every test case scenario you need, keeps your testing environment secure and risk-free, and adapts to growing test complexity and scale.
The best way to transform how your test data works for you is with BlazeMeter’s unrivaled test data capabilities. Get started using BlazeMeter test data today for FREE and experience how it will grow with you.