Software testing is only as strong as the data and scenarios that support it. Using generative AI tests can help teams create smarter test data and more effective scenarios. Test data provides the input values and conditions, while test scenarios map out user actions. Together, they validate functionality, uncover defects, and check application behavior under different situations.
What Is Test Data?
In software testing, test data refers to the input values, conditions, and scenarios used to check whether a software application performs as expected. It is important because it helps validate functionality, performance, and behavior under different circumstances to confirm that the product meets requirements.
Before creating data for test cases, remember that test data can be positive or negative. Positive data is used to confirm that the system produces the expected results, while negative data is applied to validate how the system handles errors and exceptions.
The quality of test data directly impacts the success of testing. If the data is not relevant or representative, even the best testing efforts can fail. This is why testers need meaningful and well-prepared datasets. Next, let’s look at why test data is so important in the testing process.
Test data can be grouped into the following types:
- Valid Test Data: Valid test data is the positive data used during system testing. It includes all expected inputs and conditions, so the application will run properly in different cases.
- Invalid Test Data: Invalid test data is the negative data used to check if a program can handle errors and exceptions correctly. Some examples are:
- Null values entered in mandatory fields
- Values that fall outside the accepted range
- Use of special characters where they are not permitted
- Incorrect data format, such as a phone number with letters instead of digits
- No Data: No data is used to check whether the system will function as expected if a user leaves a field empty.
- Boundary Data: This refers to pairs of values used to check if a result lies inside or outside the acceptable range.
What Are Test Scenarios?
To understand a test scenario, it is first important to grasp what is meant by the term “scenario.” In software development, a scenario represents the path or sequence of actions a user takes while interacting with an application or website. The role of a tester is to study these user interactions with features and detect potential problems that could occur.
A “test scenario” is a group of manual or automated test cases created to examine both positive and negative aspects of a project. These scenarios reflect how end users may use software applications and help developers confirm alignment with client expectations. They give a clear view of the testing needs for a QA team.
A test scenario outlines a case in which the software will be used, along with the expected outcome. The purpose is to check how the application handles user actions, processes different data types, and works with external systems.
There are two types of test scenarios used in software testing. These are positive and negative test scenarios.
- Positive Test Scenario: In this type of testing, the behavior and features of the software are checked according to their intended use with valid inputs. It covers situations where users follow the standard and expected flow of actions.
- Negative Test Scenario: In this type of testing, the focus is on checking how the software responds to unexpected inputs and unusual conditions. Testers use invalid or incorrect data to confirm whether the application can manage errors and avoid unexpected failures.
How to Create Test Data?
Creating test data is important for delivering quality products. Below are four common methods to create test data:
- Manual Test Data Creation
- Automated Test Data Creation
- Backend Injection
- Third-party Tools
Manual Test Data Creation
Manual test data generation refers to creating sample data for testing by hand. One way is to prepare a list of inputs needed for testing, create the data with the support of QA team members or developers, and then check if it behaves as expected.
This approach is the simplest way to build test data. It is often used at the beginning of a project and includes different input and output combinations.
Automated Test Data Creation
Automated test data generation reduces the time taken to prepare, manage, and run tests compared to manual creation. It is carried out with automation testing tools such as LambdaTest that handle the entire process from start to finish. These tools are faster and more accurate than a human-only approach, which results in better outcomes over time.
LambdaTest is a GenAI-native test execution platform where users can run manual and automated testing. It provides an AI-native SmartUI platform to perform automated visual testing at scale across 3000+ browsers, operating systems, and real device combinations.
Backend Injection
Backend injection is a method of supplying test data directly to a database. A tester writes SQL queries and executes them to create large volumes of test data. This method is simpler than automated generation but may not always be precise.
It can be used in cases such as:
- Testing and debugging the application without depending on user input
- Checking the accuracy of the system under different conditions
- Reducing the time and cost compared to manual data creation
Third-party Tools
Third-party tools can be used to prepare test data more effectively. These tools connect with backend systems and feed in data that mimics real-world conditions. As a result, the test data becomes diverse and extensive, giving broader coverage.
Such tools are usually more accurate than manual approaches because they are built with system and domain knowledge. They are structured so that even non-technical users can work with them with minimal training. Their setup makes them suitable for filling systems with real-time data, which helps testers run checks on both current and historical records.
Building Effective Test Scenarios
Writing a practical test scenario for your application requires a structured process. Below is a four-step method to guide you through it.
Identify and Review the Software Requirements
Begin by studying the documentation that explains the product requirements as defined by the System Under Test (SUT). These include the Business Requirements Specification (BRS), the Software Requirements Specification (SRS), and the Functional Requirements Specification (FRS). Additional resources, such as manuals or reference books linked to your application, may also be used. These documents help in identifying the functions that need to be tested.
Anticipate User Actions
Think of the possible steps users may take while interacting with the product. Draw insights from requirement documents and map them to expected user activities. For instance, if the application is a search engine, a user may want to look up images or browse articles.
It is also important to think of different ways users might engage with the features to uncover potential weaknesses. A thorough test scenario covers regular use cases as well as uncommon ones so that all aspects of the software are tested.
Formulate Test Scenarios
Create scenarios that represent all possible user activities connected to the program. Assign numerical identifiers to these actions to track the scope of your testing approach. At this stage, revisit the requirement documents to confirm that the scenarios you have written are complete. Check that each user activity aligns with a defined function of the software.
Match Scenarios with Requirements
Finally, compare every scenario with the product requirements. Use a Traceability Matrix to show these connections and confirm that your testing matches the client’s expectations. Record the scenarios in detail to assess overall performance and combine your findings into one document. This gives teams a complete view of the effectiveness of the test scenarios.
Turning Test Data into Insights
Collecting test data is only the first step. The real value comes when teams analyze this data to uncover patterns, highlight risks, and generate insights that improve both testing and development. Instead of simply checking whether a feature works or fails, insights drawn from test data can reveal why it behaves a certain way, how frequently issues occur, and what parts of the system are most vulnerable.
Smarter insights from test data help testers move beyond basic validation. By studying failure trends, coverage gaps, and performance under different conditions, teams can predict defects earlier, refine their test scenarios, and align testing priorities with real user needs.
Best Practices for Smarter Testing
Here are some of the best practices for smarter testing.
- Data Delivery: Teams should set up quick and repeatable ways to create copies of production data for testing and development. A strong test data management (TDM) approach removes manual work such as database setup, configuration, and validation. This makes the delivery process smoother and more consistent.
- Data Quality: Test data can be drawn from masked production records or synthetic datasets. Maintaining quality means ensuring the data is accurate, consistent, and suited for different testing needs.
- Data Age: Preparing test data can take time, and because of this, teams may use stale data in non-production environments. Outdated data reduces accuracy and may lead to expensive late-stage problems. Fresh data should be used to prevent these risks.
- Requirement Alignment: Each test scenario must map to at least one requirement or user story. Before combining multiple requirements into one scenario, create a separate scenario for each. Avoid overly complex scenarios and run only those that match customer priorities to reduce unnecessary costs.
- Data Accuracy: Testing can be difficult when multiple datasets are required at the same time. For example, a procure-to-pay process may need data from both financial and customer management systems. Accuracy across these datasets is vital to maintain testing value.
- Data Size: Working with subsets of data may miss unusual cases that can lead to failures. A better approach is to use full-size copies where common blocks of data are shared across environments. This keeps the scope intact without using excess storage.
- Data Security: Masking sensitive records is important to prevent leaks during testing. Replacing real data with realistic but fictional values protects against breaches and keeps testing environments compliant with regulations.
- Data Infrastructure: The right tools should be used to manage the growing volume of test data. A strong toolset supports provisioning, storage management, and sharing across different teams.
- Data Consolidation: Non-production environments often contain redundant data. A TDM strategy reduces this by sharing common records across systems used for testing, development, reporting, and support. This lowers storage needs and costs.
- Data Archiving: Archiving test data helps manage storage challenges while keeping useful datasets available. It creates libraries of past records without overloading resources, making it easier to reuse data when needed.
Conclusion
In conclusion, turning test data into insights is crucial for improving software quality and guiding testing efforts. Applying structured scenarios and analyzing results helps teams identify risks, optimize coverage, and make informed decisions for more reliable software.
