What is Test Data in Software Testing

OVERVIEW

The test data is similar to the production data used by test cases when testing software applications. It is typically collected in the test data document used by test cases and scripts. Unless test data is designed in advance, test cases may not cover all scenarios and ultimately impact software quality.

Note : Checkout LambdaTest's free online test data generator. Download random test data in bulk in multiple formats.

As software applications become more complicated and testing more rigorous, the amount of test data being ingested by testers has increased exponentially during the information and technology revolution.

The testers not only collect/maintain data from existing sources but also generate volumes of test data to ensure their quality contribution in delivering the product for real-world use. Therefore, testers must develop a strong understanding of the most efficient test data collection, generation, and maintenance approaches.

Test data is an integral part of the testing process for a tester. It provides information to the tester to facilitate finding defects and corrective actions. Test engineers load the application with data or stress it with huge amounts of invalid data to check breakpoints and other aspects of the application's performance. While executing test cases, test engineers need to input some data into the application to get the expected output.

Test data is a production-like set of data used by test cases to determine whether an application is working correctly. Test data is usually collected into a document called a test data document that helps organize it so testers can easily access it when they run their tests.

Testing the product with test data is essential when designing a new application. This can help determine if a product needs additional development or if it's ready to move on to further testing. Developers can identify coding errors by testing preliminary data before completing productivity and efficiency tests.

Test data have the following types:

• Valid Test Data: Valid test data is the positive data used for system testing. This includes all possible inputs and conditions so that the application will run smoothly in different situations.
• Invalid Test Data: Invalid Test Data is the negative data used to verify that a program will handle negative conditions and exceptions correctly. Some examples are:
• Null values can be used in the case of mandatory fields.
• Out of Range Values.
• Special characters are not allowed.
• Invalid data format, such as a mobile phone number with alphabetical characters in place of numbers, causes errors.
• No Data: No data helps ensure the system will work properly if a user leaves a field blank.
• Boundary Data: It is a pair of test data values that indicate whether an observed value falls within or outside the range of expectations.

Good test data combines valid and invalid data, including all positive and negative test scenarios.

Managing large volumes of data generated while testing all significant test cases can take time for testers.

A test data generator is a software tool that helps in software testing by generating mock data. The test data generation process involves collecting, managing, and maintaining a large quantity of data from various sources to implement test cases that ensure the functional soundness of a system. The generated data can be random or specific to the desired results.

A test data generator can be used to create structured or unstructured data. Structured data is generally more helpful for databases because these systems often save data in specific tables, columns, and types of information. In contrast, random data is not suitable for this purpose.

There are different test data generators:

• Random Test Data Generator: Random test data generator is the most straightforward data generator. It can be used to test many programs by having it output random bit streams and have those bit streams represent the required data types.
• Goal-Oriented Generator: This generator generates input for any path specified instead of just the usual way of generating information from entry to exit. It generates all possible combinations of good moves and has little chance of generating infeasible paths.
• Pathwise Test Data Generator: Pathwise test data generator follows a fixed path instead of giving it a choice among many paths. This leads to greater path knowledge, which provides better coverage prediction. It is similar to the goal-oriented generator.
• Intelligent Test Data Generator: Intelligent test data generators rely on sophisticated analysis of the code to be tested to guide their search for test data. This approach requires significant insight into the various situations that may arise so that it can generate appropriate test data quickly.

Test data helps testers determine whether the data is ready for release or not. The correct test data is essential in deciding if the software performs as per its requirements in different scenarios. With the help of the set test data boundary in the application, test data also helps in checking for negative scenarios.

Here are a few reasons why test data is important in software testing:

• Identify & Eliminate Bugs Early: Better test data coverage can help you identify bugs and errors early in the software testing life cycle. Identifying them early on helps in saving time and effort.
• Enhanced Test Data Coverage: Correct test data provides clear traceability and a bird's eye view of the test cases and defect patterns.
• Smoother Testing Cycles: Maintaining test data helps you prioritize test cases, augment your test suites, and minimize them, which means smoother and more efficient testing cycles. Effective test data can be used for both functional testing and regression testing.
• Higher ROI: If you reuse and maintain test data efficiently, you will have fewer defects in production and can reuse the same data set for regression testing on future projects. You will also save resources that would have been spent on creating duplicate test data for each new project and addressing defects.

Note : Also Checkout Code Coverage vs Test Coverage to understand the difference.

Before executing test cases on our existing data, we should ensure that the data is not corrupted and that the application under test can access it. When multiple testers work on different modules of automation testing

In the same environment, testers modify existing data as per their need. When they are done with the data, they leave it as it is. The next tester picks up this modified data and performs another test execution, which may result in a failure that is not due to a code error or defect.

In most cases, failure to update data causes corruption or outdated information. To avoid these problems, follow the below solutions.

• Have a backup of your data.
• Ensure that your modified data has been returned to its previous state.
• Divide the data among the testers.
• Keep the data warehouse administrator informed about any data changes or modifications.

When multiple testers are responsible for testing the same build, they may try to manipulate common data sets to accommodate their own needs.

If you have prepared data for specific modules and are concerned about losing it, it is best to make backup copies of the same.

According to various research studies, preparing data for testing can be very time-consuming. Around 30-60% of a tester's time is spent searching, maintaining, and generating data for testing and development. Here are the possible reasons for this scenario:

• Testing teams do not have access and depend on others for the data they need, limiting the testing team's ability to identify and fix defects.
• Agile testing has yet to be widely implemented. While multiple teams and users work on the same project, each team often uses its database and thus has different data sets. This creates conflicts that hamper testing, as the data set is only sometimes up-to-date when it comes time for another team to test the app.
• Searching a production database for information related to your application is like searching for a pin in a haystack. You need special cases to perform good tests, but you can only find them if you dig through dozens of terabytes.
• Recognizing dependencies between data values makes it harder to determine the values of individual data points. This makes preparing cases more complex and, therefore, time-consuming.
• Most testing teams need more facilities to self-refresh their test databases. This means that they must rely on DBAs to refresh the database. Some teams must wait for days or even weeks for this refreshment to occur.

Creating test data is crucial when it comes to delivering quality products. Here are four ways 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 is creating sample data for manual testing. One approach is to prepare a list of items used for testing, generate sample data using your QA team members or developers, and then validate that it works as expected.

Manual test data is the most straightforward way to create test data. It is often created at the beginning of project implementation and includes all possible combinations of inputs and outputs.

• Automated Test Data Creation

Automated test data generation effectively reduces the time taken to develop, maintain, and execute tests compared to manual test data. It is performed with the help of automation testing tools like LambdaTest that automate the whole process from start to finish. These tools are faster and more accurate than a human-driven approach, which results in greater efficiency over time.

LambdaTest is a test orchestration and execution platform that allows users to run manual and automation testing of web and mobile apps across 3000+ browsers, operating systems, and real device combinations.

LambdaTest helps over 1Million users across 130 countries to test their mobile and web apps. Using LambdaTest, businesses can ensure that their products have been tested thoroughly and achieve a faster go-to-market.

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• Backend Injection

Backend injection is one method of providing test data to a database. A tester writes relevant SQL queries, then injects them into the database to create large amounts of test data. It is easier than automated data generation methods but needs to be more accurate.

It can be used in the following scenarios:

• To test and debug your application without the hassle of user interaction.
• To be able to test the accuracy of a system under a variety of circumstances.
• To avoid the time and expense of manual data generation.
• Third-party Tools

Third-party tools can help build up your test data effectively. These tools thoroughly understand back-end applications and can pump in data like a real-time scenario. Hence, the test data is diverse and voluminous, enabling comprehensive test data coverage.

These tools are more accurate than manual methods because they thoroughly understand the system and domain. The tools are designed so that even non-technical people can use them with little expertise in the domain. The tools' design makes them ideal for populating real-time data into the system, thus allowing users to perform necessary tests on historical data.

White box testing is a software testing technique focusing on a program's internal structure and coding. This type of testing may test the code's responsiveness and the occurrence of invalid parameters. White-box testing focuses on statement, branch, and path coverage.

It aims to ensure that internal operations are carried out according to specifications, that each application component follows a proper framework, and that the developer can access all components as necessary.

White-box testing uses the source code of a program as the primary input for test data selection. Test data may be selected based on the following:

• It is desirable to test all parts of a program, including the branches. This can be accomplished by generating a test data set that covers every part of the program source code.
• Path testing is a method in which all possible paths in a program are tested at least once. Test data preparation can be performed to ensure that as many cases as possible are covered

Performance testing determines how fast a system responds under a particular workload. The goal of this type of testing is to avoid bugs and eliminate bottlenecks. An essential aspect of performance testing is that the sample data used should be as close as possible to real-world conditions.

The question arises: 'How do I get my hands on real data?'. The simple answer would be to obtain this data from the customers. Your customers might be able to provide you with existing data or even help you think through how real-world data might be structured. If you are in a maintenance testing project, you may want to copy data from your production environment into the testing bed. One practice is anonymizing (scrambling) sensitive customer data like social security numbers, credit card numbers, and bank details before copying it.

A security testing process is designed to investigate the security features of a system to ensure that it protects data from malicious intent. To provide protection, test data that needs to be designed should cover the following topics:

• Confidentiality: The sensitive information provided by the users should not be shared with any third parties. Testers should design test data that verifies user data and information encryption.
• Integrity: To determine whether a system is functioning correctly, you should decide if the data it provides is accurate. The best way to do this is to take an in-depth look at the design, code, databases, and file structures.
• Authentication: User identity verification is an essential part of software development. Developing test data for this purpose combines usernames and passwords that are checked to ensure only authorized people can access the system.
• Authorization: Authorization identifies the rights of a specific user. Testing data may contain combinations of users, roles, and operations to check whether only authorized users can perform a particular operation.

Quality assurance testers test the applications from a user's perspective, not from an internal perspective. Their work includes integration testing, system testing, and user acceptance testing. The term black box refers to the fact that they do not have access to the code or design of the application under test.

Testers identify and locate program errors, ensuring the program performs as intended. By using different techniques of black box testing, testers and QAs ensure quality by applying functional or non-functional testing.

At this point, testers need test data as input for executing black box testing techniques and implementing them cost-effectively. Test data should reflect all application functionality without exceeding cost and time constraints.

When designing test cases, it is essential to consider the various data categories encountered. These include no data, valid data, invalid data, illegal data format, boundary condition data, equivalence partition, decision data table, state transition data, and use case data. Before going into these categories in detail, testers should first gather existing resources from the application under test (AUT).

To ensure that your data warehouse remains up to date, you should document the data requirements at a test-case level and mark them reusable or non-reusable when you script your test cases. It helps you understand the data required for testing from the very beginning so that you can reference it for further use later.

Since we discussed the importance of test data and its advantages, it's time to discuss how to make the best out of your test data. Here are the best practices for preparing effective test data:

• Data Delivery: Organizations should streamline creating copies of production data for development or testing. Solutions that offer fast, repeatable data delivery will help organizations meet the demand for these copies. Specifically, application team leaders should consider solutions that feature a streamlined test data management (TDM) approach that eliminates manual processes, such as target database initialization, configuration steps, and validation checks.
• Data Quality: Operations teams often go to great lengths to provide test data to a software development team. This is because there are different types of test data, such as masked production data or synthetic datasets. And as Test data management teams must balance considerations for data that is both agile and durable, ensuring data quality is conserved across three key dimensions:
• Data Age: Due to the time and effort required to prepare test data, operations teams often fail to meet several ticket requests. As a result, stale data is sometimes used in non-production environments, which can impact testing quality and result in costly, late-stage errors.
• Data Accuracy: Testing data management processes can be complicated when multiple datasets are required at a specific time. For example, testing a procure-to-pay process might require that data be integrated across customer relationship management and financial applications.
• Data Size: Developers must often work with subsets of data that are unlikely to satisfy all functional testing requirements. When using subsets of data, you may miss test case outliers, increasing project costs due to data-related errors. In an optimized strategy, full-size copies of your test data are provisioned in a fraction of the space by sharing blocks of common data across copies.
• Data Security: Masking test data is essential for protecting sensitive information from prying eyes. By replacing actual data with realistic but fictitious values, you can ensure regulatory compliance and protect against a data breach in test environments.
• Data Infrastructure: Organizations must select tools that make the most of infrastructure resources to handle the overwhelming volume of test data. An effective toolset should have the following:.
• Data Consolidation: Organizations often keep non-production environments with 90% of the data in them redundant. A TDM approach will lead to reduced storage and costs by sharing common data across domains, including those used for testing, development, reporting, production support, and other use cases.
• Data Archiving: An effective approach can help address the storage challenges that testing teams face. It can make it possible to maintain libraries of test data while optimizing storage use.

Software testers are responsible for creating complete test data in compliance with industry standards, legislation, and the baseline documents of the project. The more efficiently we manage our tests, the more likely we can deploy reasonably bug-free products for real-world users.

Preparing test data is essential for the project test environment setup. We can't miss any tests because we need to have complete data available for testing. The tester should create their test and existing production data to help them evaluate the product under testing.

We hope to answer your questions regarding test data through this extensive test data tutorial.

Happy Testing!