Test Data: Definition, Types, Challenges, and Best Practices

What are the top three testing elements that make or break the test cases? For many testers (if not all), they usually are specification documents, properly outlined test steps or scripts, and relevant test data. And while the first two are well-known skills that QAs focus on majorly, the last one often gets sidelined. This is because testers randomly select the data they use during testing, although it might not be true for all businesses. However, for the companies and QA teams that think data values are a trivial and non-essential part of testing, follow through this blog till the end. You will learn why turning your attention to test data in software testing will elevate your testing efforts.

What is Test Data in Software Testing?

In software testing, test data refers to the input values, conditions, and scenarios useful for validating and verifying the functionality, performance, and behavior of the software. It is crucial because it helps assess the software’s performance under various circumstances and ensures that the product meets the specified requirements and functions. And before you start creating your new data for test cases, don’t forget that data is both positive and negative as per the tests. The former data type is to verify the expected results of the software, while the latter data type is to validate the exceptional and error-handling cases.

Another thing to remember about test data is their value in the testing process; trust us when we tell you all your testing efforts will fail if the data is not relevant. What you need is quality and representative data, and here we help you understand how to create such data sets. But before that, let’s understand the importance of data for testing.

Why is Test Data Important?

The importance of test data within the context of software testing is crucial. It serves as a fundamental component, facilitating defect identification, performance assessment, and assurance of software reliability.

A well-defined, comprehensive set of test data in software testing kickstarts the first step toward testing the software against real-world conditions. Many systems run without errors when there is no external load on them, but only a few can continue when users start actively navigating them. Test data makes sure that your software runs as perfectly for the customers as you want it to.

Read Here – Training Data vs Testing Data

What is Test Data Generation?

Test data generation refers to the process of creating and maintaining values for testing with the intention of using it for testing purposes. It consists of creating synthetic or representative data to validate the functionality, performance, security, and various other aspects of the software. 

There are several reasons why creating test data is crucial: it should match the test environment and be relevant to the testing at present. And not every data can be used for every type of testing. So, clearly, you need to generate data that is useful.

In practice, there are two ways to create test data in software testing:

• Manually
• By using test data creation automation tools
• Migrating existing data from production to the testing environment

Types of Test Data

Effective software testing requires a diverse range of test data to assess an application’s functionality comprehensively. Test data encompasses various scenarios that help uncover issues and ensure the software’s reliability. Here, we explore different types of test data, each serving a distinct purpose in the testing process.

Blank Data

Blank data test cases evaluate how the system handles missing or empty inputs. This type of test data is crucial to ensure that the software can gracefully manage scenarios where users leave required fields empty. It assesses the system’s error handling and validation processes, ensuring it provides meaningful feedback to users when data is missing.

Valid Test

Valid test data represents scenarios where users input correct and acceptable values. It is fundamental to verify that the software functions as expected under normal usage conditions.

Invalid Test

Invalid test data explores scenarios where users provide incorrect, unexpected, or malicious inputs. It aims to uncover vulnerabilities and weaknesses in the system’s data validation and error-handling mechanisms. By testing with invalid data, testers can assess how well the software defends against potential security risks and user errors.

Boundary Conditions

Boundary condition testing focuses on data values at the extreme edges of acceptable ranges. It helps identify issues that may arise when data is at the lower or upper limits of what the system can handle.

Huge Test

Huge test data cases examine the software’s performance and scalability by subjecting it to an extensive volume of data. This type of testing assesses how well the application can manage large datasets and identifies any performance bottlenecks or issues related to system resources, such as memory and processing power.

Tips for Implementing Test Data

Test data implementation is only good if done properly. We have a few tips that might help:

• Collect and use a comprehensive set of test data that covers a wide range of scenarios, including valid, invalid, boundary conditions, and edge cases, to achieve thorough testing.
• For the above point, consider using data generation tools to create diverse test data sets efficiently and consistently.
• Keep a mix of positive and negative test data to increase the test coverage.
• Maintain data privacy when using real user data.
• Develop reusable test data sets that can be applied across different test cases and scenarios to save time and effort.
• Evaluate the test data at every step of the project to ensure data integrity and accuracy.
• Implement data management strategies to maintain and update test data as the application evolves, ensuring data relevance.
• Automate the setup of test data to increase efficiency and reduce manual errors when preparing data for testing.

Test Data Properties

Test data is a fundamental component of software testing, and its properties play a pivotal role in ensuring the effectiveness of testing processes. Here are four key properties that test data should exhibit:

Relevance

Relevance is the property that ensures test data aligns with the specific test case or scenario under examination. Relevant test data mimics real-world usage, increasing the likelihood of detecting critical issues and providing meaningful insights into the software’s behavior.

Diversity

Diverse test data encompasses a broad spectrum of inputs and conditions. It covers both typical and exceptional scenarios, including valid, invalid, boundary, and edge cases. Diversity helps uncover a wider range of potential defects and ensures robust software quality.

Manageability

Manageability refers to the ease of handling and maintaining test data throughout the testing lifecycle. Test data should be well-organized, easy to update, and accessible to testers. A well-managed test data repository simplifies test case creation and execution, streamlining the testing process.

Consistency

Consistency is essential for maintaining the stability of testing processes. Test data should exhibit consistency across test runs, ensuring that results remain reproducible and reliable. Inconsistent data can lead to inconclusive or unpredictable testing outcomes.

Test Data Preparation Techniques

Test data preparation is a critical aspect of software testing, and various techniques can be used to prepare test data effectively. Here are different test data preparation techniques:

• Manual Data Entry: Testers manually input data into the system under test, ensuring data accuracy for specific test scenarios.
• Fresh Data Insertion: Feed fresh test data into a newly built database as per your testing requirements and use it for executing test cases by comparing the actual results with the expected results.
• Data Generation: Synthetic data is created using data generation tools, scripts, or programs. This technique is particularly useful for generating large datasets with diverse values.
• Data Conversion: Existing data is transformed into different formats or structures to assess the application’s ability to handle diverse data inputs.
• Data Subsetting: A subset of production data is selected and used for testing, focusing on specific test cases and scenarios to save resources and maintain data relevance.

Why Should Test Data be Created Before Test Execution?

The typical answer would be to smoothly run the test data management process. We recommend you work on generating values before starting the test execution to avoid missing the product delivery deadline and clearly follow the test generation steps particular to the testing environment.

What is Corrupted Test Data?

Corrupted test data refers to test inputs or datasets that have been compromised or altered, leading to inaccurate or unreliable testing outcomes. Data can become corrupted due to various factors, such as errors during data generation, transfer, or storage. Utilizing such test data during software testing can lead to misleading results, failed tests, and inaccurate assessments of the software’s performance and functionality. For instance, if a the database becomes corrupted due to a software glitch, the test results derived from this data might not accurately reflect the application’s behavior. It will potentially lead to incorrect conclusions about its quality and readiness.

An example of corrupted data can be the input dataset that is used to simulate user interactions during load testing. The test results could show an unusually high rate of errors and performance issues, even if the software is functioning properly under normal conditions.

Test Data Testing Types

Test data is everything, and you can create it as per your needs. But do you need different test data types in software testing? Preferably, data for testing depends on the types of testing you are conducting.

White Box Testing

In white box testing, test data is extracted from assessing the original software code that is under examination. You can decide on what input values to create and select as per the below points:

• Go for maximum coverage of the code. Make sure that every branch of the code is tested at least once.
• Choose the data that checks all the paths of the code at least once.
• Focus on the types (single or multiple) that are invalid for calling different methods.

Performance Testing

Running performance tests checks the system’s ability to handle user load and response time under stressful workloads. Test data for performance tests would look quite different from the data for white box testing. For this, you need to focus on data for testing that resemble real data from the production environment. You can receive all the relevant data directly from your customers by asking for and retrieving feedback information from them. Or, if you already have sufficient data in your production environment, you can use the same for testing.

Security Testing

The purpose of security testing is to check if the system can defend itself against malicious attacks. And surely any data within the system needs to be protected as well. So, the data you require to test your software’s security system should have the following features:

• Ability to verify if the encryption is done properly.
• Integrity of data by in-depth analysis of design, code, and databases.
• Different input data to test the user authentication.

Black Box Testing

Totally opposite to white box testing, black box testing hides the code from the testers. In such a case, creating data from the QA’s experience can do the trick. But for some quantitative data for testing, you can look at selecting data that satisfies these conditions: invalid, valid options, edge cases, boundary conditions, special characters/illegal values, and use case data sets.

How to Create Test Data?

In the above section, we talked about different ways to generate test data. Here, we elaborate further on those approaches.

Manual Test Data Creation

One of the most popular and common techniques to create test input values is the manual method. Testers identify and list down varying test scenarios/conditions to generate input data aligning with those tests. Mostly, QAs make use of Word files, text files, and XML files. But as it goes with every manual task, test data creation is a time-consuming process with the possibility of errors.

Automated Test Data Creation

The next step to any manual activity is transitioning toward automation. There are multiple automation tools available in the market that support test data creation while offering various testing features in-house. Testsigma is one such automation tool; it is a fully customizable, unified platform equipped with test data management and test development.

This section will show how to generate data for testing using Testsigma. Read Here – Test Data Automation

Log into your Testsigma account and click on the ‘+’ sign on the left side panel. You will see the option to create a Test Data Profile. 

On the test data profile page, you have the option to name the data set and create values for testing in bulk. The below image shows an example of test data creation on this profile page.

Then, click on the Create button at the top right side of the page to finish test data creation. You can use this profile and create more for multiple test sets for your product.

Alternatively, you can import test data onto the Testsigma platform and manage the values as you work on the tests. The file supported for import is Excel.

Lastly, you can use any created data profiles in the test cases. Here is a comprehensive guide on how to use test data in test cases using Testsigma.

Alternatively, Testsigma supports automated test data generation.

For dynamic test generation, testers can call Test Data Generators in test steps by using !|Data Generator| format. And thereafter, replace the Data Generator placeholder with the actual data from the list on the screen.

This image shows the parameters you can use in the test step.

Once you select the !|Data Generator|, you can see the list of data values available to generate.

You can access the built-in Test Data Generators available on Testsigma here.

Besides assisting data creation, Testsigma also provides:

• Test data management
• Data-driven testing
• NLP-based test case creation
• Easy test maintenance

Want to see how Testsigma can make end-to-end test creation 5x faster?

Challenges In Obtaining Test Data

Manual preparation of test data is time-consuming, and automated requires immense skills and some resources. There are some other challenges as well, such as:

• Testers lack access to the data sources.
• Delay in receiving the right set of data from data analysts or developers.
• Inconsistent, complex, and large volume of data sets.
• Possible lack of data privacy
• Dependencies on one another.

What Does the Ideal Test Data Do?

Ideal test data in software testing serves several important purposes to ensure thorough and effective testing of a software application:

• Ideal test data covers a wide range of scenarios, including valid inputs, invalid inputs, edge cases, and boundary conditions.
•  It allows for performance testing by simulating various usage scenarios and load levels.
• For systems that involve data manipulation, an ideal test data set helps maintain data integrity throughout the application’s processes.
• It includes values that are at the extreme ends of acceptable ranges.
• It exposes defects, bugs, and vulnerabilities in the software by providing inputs that mimic various user interactions.
• The right set of data includes inputs designed to trigger security vulnerabilities, helping testers identify potential security breaches, data leaks, and unauthorized access points.

Best Practices for Preparing Effective Test Data

After challenges, it’s time to focus on the best practices for preparing data for testing. These test data preparation techniques are sure to propel your testing efforts to the next level:

• If you want the creation process to move faster, choose automation that eliminates delays and offers self-access to data sources.
• Before starting the generation, identify the list of test scenarios and conditions that require varying data.
• Involve the right teams and individuals to handle complex and large volumes of data for smooth movement between systems.
• Focus on using fresh, new data from the sources to receive relevant results upon testing.
• Regularly conduct cleaning of the test input data to remove duplicates and missing values.

If not fully, these best practices are sure to help you prepare data types useful in testing that are reliable, relevant, and contribute to effective software testing and quality assurance.

Automated Test Data Generation Tools

Data generation is more important than you think. And with generating test data being a continuous process for every testing project, automation tools are mandatory for all businesses. From generating realistic data for comprehensive coverage to creating large volumes of data for performance testing, these tools offer a range of capabilities to cater to your specific testing needs.

• Testsigma

Run web automation testing on more than 3000 browsers and devices without coding a single line

• TDM Data Generator
• Mockaroo
• QuerySurge
• GenRocket
• Data Factory
• DATPROF

Read more about these here: Test Data Generation Tools

Conclusion

Among many essentials in testing, data often gets in and stays in the back seat. Despite every tester making use of test data in almost every test case, it testing is still not taken seriously. It is often because QAs randomly select data from their side during the test case run time or have to wait for data analysts or developers to send them values, which are only sometimes relevant to the tests.

This blog is our attempt at highlighting the importance of data for testing. We discuss different test data creation techniques and automation strategies that businesses and testers can use from scratch. Even the list of automation tools for generating test data places the spotlight on the current platforms that can surely assist your testing efforts.

Frequently Asked Questions

What is an example of test data?

An example of test data for a login feature would be:

Valid: 

Email=”user@example.com”, Password=”Password123″

Invalid: 

Email=”invalid email,” Password=”invalid”

What is test data also called?

You can call test data in various ways, such as production test data, input values, and experimental testing data.