Test Data Generation Automation

Hari Mahesh

Test data is the most crucial part of software testing because it is the good quality of test data that results in effective testing. Test data should be well formatted, varied, and accurate to bring out bugs in the application in an effective manner. On the other hand, creating this data manually takes time and invites errors, particularly in larger and more complex applications. These challenges can be solved by the automation of test data generation.

This article explains how automation works for generating test data, its benefits, and an effective way to implement it.

Why is Test Data Generation Automation Needed?

Test data generation is critical in testing the application’s behavior, performance, and stability. If you are testing the AUT manually, then test data needs to be created by humans, which is not only a manual task but also error-prone and does not meet the current demands of modern software projects. Test data generation automation is a game changer that pays off in terms of improved efficiency, accuracy, scalability, and cost.

We will now see why automation is key for software and data testing.

Improved Efficiency

Manual test data creation is time-consuming, especially for complex applications requiring large datasets. Automation drastically reduces this time by automating repetitive and predefined tasks. Tools can quickly generate data for various scenarios, allowing testers to focus on other critical testing activities. This efficiency is vital in Agile development environments with frequent updates.

Higher Accuracy

Human errors like typos, wrong matches, or inconsistent application of data rules can easily occur in manual processes. Automation ensures data integrity, omitting errors in processes, and also ensures that any data generated will conform to the defined constraints and relationships. It generates trustworthy datasets; those datasets drive up accuracy and, therefore, confidence in testing outcomes. There are also data automation tools to perform data testing, which removes the possibility of having a buggy application as a result.

Scalability

As applications become more complex, manual data generation cannot meet the new emerging needs. You can also take advantage of test scalability with automation by easily creating a large dataset for performance, integration, and edge-case testing in a short period of time. This allows teams to generate their data mimicking real-life scenarios. This ensures hat all testing conditions are being fulfilled.

Consistency and Repeatability

You need test data so that you can repeat tests consistently to get reliable testing results. Automation syntax and rules-based data generation guarantee: no more bad test data. This capability is especially useful for regression testing and Continuous Integration, where you require a fresh, uniform set of data time and again. Test data automation tools and processes also ease debugging, as they remove the possibility of data discrepancies.

Cost Savings

Manual generation of test data is a time and resource-consuming task that contributes to high project costs. Test data generation automation cuts costs by decreasing the time invested in repetitive processes and enhancing productivity. It enables skilled team members to dedicate more time to high-value testing work, thereby increasing overall productivity. The incremental improvement in ROI over time is derived from re-using automation scripts across test cycles, saving time & effort.

Types of Test Data

First, what is test data? Test data refers to the information or data that is used during software testing to verify the functionality, accuracy, and performance of a software application. It helps simulate real-world scenarios and ensures that the application behaves as expected under various conditions. Various kinds of test data are used for different tasks and help testers understand how the application is functioning under different test conditions. This guarantees the full spanning of functional and non-functional requirements.

Let us review different forms of test data.

Valid Data

It conforms to the application’s rules and constraints so that the application behaves as expected. It is used to validate the application when given the right inputs. For instance, if a user enters the right account number and pin in a banking application, then the user should be able to log in successfully. This ensures that the application’s core function can perform as expected.

Invalid Data

Deliberately wrong data is entered to violate AUT’s constraints, rules, or expected input formats. Here, we test the scenarios to verify that the app does not crash or behave incorrectly when it receives unexpected inputs. As an example, filling in alphabetic characters in a number-only field must provide an adequate error message.

Boundary Data

Boundary data tests the limits of the application’s acceptable input ranges, including minimum, maximum, and just-outside-the-limit values. It ensures that the application handles edge cases correctly. For example, if a field accepts numbers between 1 and 100, testing with values like 1, 100, 0, and 101 validates boundary conditions. Testing with boundary data uncovers issues that might occur at the edges of input ranges, which are common areas for bugs.

Null Data

It is defined as empty or missing values that are used to check how the application reacts to such situations. It measures how the application works if required fields are left empty or optional fields are not filled in. For example, submitting a form that does not include the required information should return an error or validation message. Data with null data for the application is tested to ensure the application’s robustness so that if the input is null, the application does not crash.

Random Data

This means generating random data, which is exactly what a user could do but within the allowed limits. It helps simulate the actual-world use cases where various user inputs can be different and inconsistent. For example, sanity testing on online forms with the combination of text/numbers/special characters in non-critical fields. Random data can reveal bugs that are not discovered when using defined test data.

Masked Data

This can be defined as anonymized data that comes from real-world datasets to maintain privacy and paperwork compliance with regulatory standards like GDPR or HIPAA. It facilitates realistic testing without using sensitive or Personally Identifiable Information (PII). Customer names and email addresses in a test database, for instance, can be substituted with values such as [email protected]. It uses masked data to maintain compliance while enabling realistic test datasets.

Static Data

This remains unchanged across test cases and test cycles, serving as a constant reference point. It is used in scenarios where predictable, repeatable results are required, such as validating reports or performing regression testing. Static data makes debugging easier, with the test environment always being the same.

Dynamic Data

Dynamic data is created or changed during the test’s execution to mimic real-time updates. It’s useful for testing dynamic scenarios with ever-changing information like shopping cart items or live transaction data. For example, you can create unique usernames on each run of your tests to prevent collisions. Dynamic data can also be used to test apps with dynamic workflows or real-time interaction.

Data Testing for Data Quality

Data testing plays a critical role in software quality assurance by verifying the integrity, accuracy, and reliability of the data used during testing. High-quality data is essential for producing meaningful and trustworthy test results. Poor or inaccurate data can lead to false positives or negatives, masking defects or creating unnecessary noise that complicates debugging. This makes data testing an essential practice for ensuring the success of software systems.

Key Objectives of Data Testing

The primary objectives of data testing revolve around ensuring the accuracy, usability, and compliance of data used in various testing processes. These objectives include:

Ensure Data Completeness and Correctness

Data completeness ensures that all required fields and records are present in the dataset, while correctness verifies that data values are accurate and align with defined rules. Missing or incorrect data can lead to inconsistent results and overlooked defects.

Validate Data Transformations in ETL Processes

Testing ensures that data is transformed accurately according to business rules for applications involving data extraction, transformation, and loading (ETL). For instance, a transformation that aggregates sales data must correctly calculate totals and averages.

Verify Compliance with Data Governance Policies

Organizations must ensure that their data complies with regulations such as GDPR, HIPAA, or CCPA. This involves validating that sensitive information is masked or anonymized and that data usage aligns with privacy policies.

Test Data Usability Across Multiple Scenarios

Data must be functional and applicable across various test cases, such as functional, performance, and integration testing. Ensuring usability involves creating datasets that cover edge cases, typical workflows, and stress conditions.

Techniques in Data Testing

To achieve these objectives, data testing employs various techniques tailored to specific data-related aspects. These include:

• Schema Validation: Ensuring database schemas meet design specifications.
• Data Validation Rules: Checking constraints, formats, and relationships.
• Data Transformation Testing: Validating the accuracy of data mappings and transformations.
• Data Volume Testing: Assessing system performance with varying data loads.

Strategies for Test Data Generation Automation

Automating test data generation requires thoughtful planning and implementation to ensure it aligns with testing objectives and business requirements. Effective strategies streamline the process and enhance the relevance and reliability of the generated data. Let’s explore key strategies for automating test data generation and how they contribute to efficient testing.

Understand Requirements

Before initiating automation, it’s essential to have a deep understanding of the application’s business logic, database schemas, and data constraints. This includes analyzing:

• Business Logic: The rules governing application functionality (e.g., user registration must require a valid email).
• Schemas: The structure of databases, including table relationships and field types.
• Constraints: Rules such as unique keys, data formats, and ranges.

Use Data Templates

Data templates are predefined structures that outline the format, rules, and constraints for the required test data. These templates act as blueprints for automated tools to generate data consistently.

Key Features of Data Templates:

• Reusability: Templates can be applied across multiple test cases, saving time and effort.
• Customization: Templates can be tailored to specific test scenarios, such as boundary testing or performance testing.

Parameterization

Parameterization involves using variables and placeholders to create diverse datasets dynamically. This approach ensures that test data is not static but adapts to varying scenarios.

How It Works:

• Define parameters such as age, salary, or product category.
• Assign ranges or specific values for each parameter (e.g., age = 18 to 60).
• Generate multiple combinations of parameterized data for comprehensive testing.

Integrate with CI/CD Pipelines

Incorporating automated test data generation into Continuous Integration/Continuous Deployment (CI/CD) pipelines ensures that fresh and relevant data is always available during development cycles. Read more about Continuous Integration and Testing: Best Practices.

How It Works:

• Scripts or tools for data generation are triggered automatically during CI/CD builds.
• Fresh test data is created for each test suite, ensuring relevance to the latest code changes.
• Old test data is cleared or refreshed to avoid conflicts.

Synthetic Data Generation

Synthetic data is artificially generated data that mimics real-world datasets without exposing sensitive information. It is created using algorithms, templates, or simulation models.

Key Advantages:

• Data Privacy: Synthetic data eliminates the use of actual user data and ensures compliance with privacy regulations like GDPR or HIPAA.
• Customizability: It can be tailored to specific test scenarios, such as stress testing or edge-case validation.

Test Data Generation Tools

There are many test data generation and test data management tools available in the market. A few examples are IBM InfoSphere Optim, GenRocket, Jailer, DATPROF, and so forth. However, intelligent test automation tools such as testRigor have built-in test data generation capabilities. With testRigor, it’s very simple and straightforward to generate test data through its generative AI.

generate from template "##########", then enter into "Mobile" and save as "generatedPhone"

Another advantage of testRigor is that you can create, store, and reuse the same test data. Generated test data is stored in the cloud, so you can use it at any time during test execution by just mentioning its name. For data-driven testing, you can create rows of centralized test data yourself in the tool and then use them in the test case effectively. If you need to make any updates to this test data, you can do so in the test data tab, and the changes will be reflected in all the test cases. Read: How to do data-driven testing in testRigor.

We have a detailed how-to guide explaining the step-by-step procedure for generating unique test data with testRigor. You can read the article and try it yourself: How to generate unique test data in testRigor.

Conclusion

Test data generation automation is an essential part of modern software testing that saves time, reduces errors, and makes data creation scalable and realistic. Determining test data requirements, implementing templates and parameterization, and incorporating the test data generation process into CI/CD pipelines are some of the strategies that will make generating test data effective and efficient. The next step is replacing your record & playback or traditional time-consuming test automation with something more battle-tested and viable long-term, like testRigor, which goes further and combines test automation with dynamic data generation to reduce maintenance and shorten test cycles.

Using these techniques coupled with testRigor, you can easily implement a testing pipeline, ensuring you deliver the high-end software customers expect in today’s era of fast and reliable software.

“We spent so much time on maintenance when using Selenium, and we spend nearly zero time with maintenance using testRigor.”

Keith Powe VP Of Engineering - IDT