Data testing is extremely important because data integrity is critical for anyone in business, especially for business owners. Who will accept apps with fake data or weak security measures?
However, data pre-processing is a time-consuming task. You must search for test data or generate it from scratch and then manage and maintain it. It’s not enough to have tests in place. You need to know which application features you’ll test and in which scenarios to get the most accurate and comprehensive coverage. No need to tell additionally that the data must be precise and complete to fix bugs.
Challenges With Test Data Sourcing
When you start writing your test cases, you need the information used as input – accurate input data. This stage is usually called “test setup.”
Note: You should not use data from other testers or standard production data. It is better to have your own. Always create a fresh set of data for your needs and always put it into the database you are testing.
Also, testers should consider the data sources required for the sub-sets they plan to execute. For instance, you need to test only 1000 customers out of the more than 1,000,000 you have. This way, you can understand how people perceive your brand.
There are some constraints too. One of the constraints is mapping PII policies. As privacy is a significant issue, testers need to classify PII data. They can use test data management tools that address such issues and suggest the right policies based on the standards/catalogs.
To address current and future challenges, we must ask questions like:
- When and where should we start the conduct of TDM?
- What are the most crucial processes or steps that we can automate?
- What are the particular tasks to automate?
- When should we test it all?
- Do we test everything before putting it into production, or do we test particular functions only before they go live?
And many more questions. We also have more specific challenges for the test data sourcing:
- You don’t possess sufficient knowledge about the tools used for gathering test data.
- Test data generator tools lack the required functionality.
- It is not enough data for the application under all environments you must test.
- The data requirements are unclear for business processes.
- The production environment data.
- You lack time to gather large volumes of data.
- Testing may need to look for possible data dependencies/data combinations.
- Communication between testers, architects, DBAs, and BAs is not appropriate. However, you need to have the data generated as required by the testers.
- Tests must apply to multiple applications, data versions, and continuous release cycles.
- Data may be created during the test execution.
QAs need to work in tandem with developers for further testing coverage of the application under test (AUT). An important aspect is running all test cases with every possible negative case.
Corrupted Test Data
Before we run tests, we need to ensure that the data is valid and that the application can read the data sources. When running multiple AUTs simultaneously, data corruption often occurs in the testing environment.
Testers work with products against their requirements. After testers finish their work, they generally don’t do any data cleanup. The next performer will get that modified data and execute the test, which may fail. But is not a code error or defect.
In most cases, this is how data becomes corrupted or outdated. To prevent this problem or at least reduce it significantly, we can apply the following solutions:
- Always keep copies of your data.
- Always return the data backup to its original state.
- Divide the data stored in the test system among the testers.
- Inform the data warehouse administrator about any changes.
Data Testing Techniques
- Manual Test data generation: Test data is entered by testers according to the case requirements. It is a time taking process and also prone to errors.
- Automated Test Data generation: This method involves data generation tools. This approach has two advantages — speed and accuracy. But it is much more expensive than manual test data generation.
- Back-end Data Injection: This technique involves injecting data into the database through SQL queries to update the existing data. It is fast and effective, but should only be used by experienced developers so that the current database won’t get corrupted.
- Using Third-Party Tools: Such tools work according to your test scenarios to generate or inject data. The minus is the price, but the tools are precise and personalized as per the business needs.
Example 1: Prepare Test Data For White-box Testing
In white box testing, developers prepare the required data to cover all branches of the program source code, all the application API, and negative API.
Everyone who works in a software development life cycle, like Product Owners, Business Analysts, and Developers, should be involved in test data preparation.
Example 2: Prepare Test Data For Black-box Testing
Here, testers do not work with the structure, design, and application code under test. Their main goal is to identify and localize errors. Testers apply either functional or non-functional testing through various black-box testing methods.
To design the test data, the specialists responsible should consider such data set categories as no data, valid data, invalid data, invalid data format, boundary condition data, equivalence section, decision data table, state transition data, and use case data. Before testing categories, testers initiate data gathering and analysis of the existing resources of the application under test.
When you write test scripts, we recommend documenting the data requirements at the test case level and marking them as usable or not reusable.
Example 3: Prepare Test Data For Performance Testing
Performance tests require large data volumes. Sometimes manual data creation fails to detect subtle bugs. You can trace them only in the actual data generated by the application under test.
To collect data you cannot create manually, ask your lead/manager to make it available from the live environment. This data will ensure that the application runs smoothly for all valid inputs.
Data is “ideal” if you can identify all the application errors in the minimum size of the data set.
It’s important to prepare data that incorporates all application functionality.
Example 4: Prepare Test Data For Security Testing
A security test ensures that information systems are secure against malicious intent. Here, tests need to cover several different aspects:
- Confidentiality: All client data is stored securely according to data protection requirements. It is never used for any other purpose. As a short example, if an application uses SSL to encrypt messages, and you want to ensure that the encryption is correct, you can design a test.
- Integrity: Make sure that the information you provide is accurate. You can prepare suitable test data for the site if you start by taking an in-depth look at the design, code, databases, and file structures.
- Authentication: The case is the process of establishing the identity of a user. The test data can be different combinations of usernames and passwords. The purpose is to ensure that only authorized people can access the software system.
- Authorization: The test data for identifying the particular user’s rights contain various combinations of users, roles, and operations. The goal is to verify that only users with sufficient privileges can perform a particular procedure.
Get Test Data Using the dbForge Data Generator
dbForge Data Generator is a powerful and simple-to-use tool that can quickly generate high-quality test data in any volume. It offers over 200 generators with customizable configurations to populate SQL tables. Users can produce large amounts of realistic test data without wasting time on manual filling.
This tool also ensures data consistency across multiple tables and data dependency across columns through data integrity support. Users can automate and schedule data generation procedures through the command line.
You can read about producing realistic test data with the dbForge Data Generator or try the dbForge SQL data generator directly. A 30-day free trial is available.
Conclusion
When you create test data, you must meet the project requirements, industry standards, and guidelines. It is a critical part of your testing workflow, and you can’t afford to miss it. If you can manage test data efficiently, we can deploy bug-free products for end-users.
Before you start the testing routines, make sure to prepare the proper test environment properly. If all data is unavailable for testing, testers should create their own data beyond the standard production data. Several methods described above allow you to achieve this goal. So, select an optimal approach that will be cost-efficient and time-saving.
