Testing teams need access to high-quality test data to achieve comprehensive and efficient test coverage. Test data can be obtained from a variety of sources: from production environments (after masking or hashing) and synthetically generated.
Using full-production data to provision test environments is time-intensive and introduces risks like sensitive data leakage. Automating this process is essential to improving QA efficiency and reducing risk.
1. Test Cases
A test case is a set of instructions that guide the tester on ‘HOW’ to check if an application or software meets a specific requirement. It also includes a detailed description of the input data and the expected output, based on which the tester can mark the test as pass or fail. Often, a set of test cases is organized into a ‘test suite’ that tests a logical segment of the software application.
Test cases can be created using a number of testing design techniques such as boundary value analysis, equivalence class partitioning and decision table testing. Regardless of the methodology used, a test case should follow a standard format such that it is easily understandable and can be reused in future testing cycles.
Ideally, test cases should be designed before the test script execution starts. However, this is not always possible due to the fact that creating test data may require a lot of pre-steps or time-consuming test environment configurations. In such a scenario, it is important to develop test cases using the best practices for effective test data management.
A well-written test case will include a test summary, description, test steps and expected results in clear and concise language. Moreover, it will make use of the standard terminology to avoid overlapping or duplicated test steps. It will also ensure that the test case is more reliable and understandable for both manual and automated test execution.
There are four types of test data that can be used during the testing process – synthetic data, normal test data, invalid test data and blank test data. A good test case should be able to use all of these test data types and have an additional field to record the source of the test data. This will help in identifying what kind of test data is required for each different scenario and will save time on the creation of the same test data for repetitive testing activities. Having different repository versions for each kind of test data will also be helpful during regression testing as it will help in finding what type of changes can break the test.
2. Test Data
The test cases executed need input data that is used to identify and correct defects. The quality of this test data is a major factor in how quickly the application can be made bug-free and ready for deployment.
It’s important to provide a variety of data types in order to cover all possible scenarios and tests. The test data should also be up to date. This includes identifying and removing outdated data sets as well as validating the test data for new functionality.
One of the biggest challenges for testing teams is providing sufficient test data. The test data must be accurate and realistic but it also needs to be scalable for future iterations of the application. This is why the use of artificial intelligence and machine learning are key to ensuring that a testing environment is up to date with the latest data.
The right test data management tools will provide intelligently sized datasets, reusability of data, and the ability to create unique data sets. The tools will also be able to provide different kinds of data based on your specific requirements such as valid, invalid, blank or boundary data. This can ensure that all tests are run under the correct conditions and can identify potential defects.
Test data should be re-generated and refreshed on a regular basis. This will allow for a more accurate representation of real-life operating conditions and can help avoid issues in production that could be triggered by inaccurate test data.
Many organizations face challenges when it comes to sourcing their test data. This is especially true if the test environments and databases are not centralized across multiple software teams. If there are more team members than test environments or databases, this can cause conflicts and lead to a delay in the testing cycle.
Another challenge for test data management is privacy and security concerns. It’s not always practical to use production data in a test environment because it can contain sensitive information. However, there are a number of tools that can be used to protect this data such as data masking. This encrypts the personal data while retaining the formatting and other attributes that are useful for testing purposes.
3. Test Environment
The test environment is a key part of the entire software testing cycle. A reliable and consistent test environment can save a lot of time in the process of preparing and deploying a test case.
This means that it is vital to create a test environment that closely resembles the live system and contains all the components required for the test cases to work properly. Creating such an environment can be difficult and time-consuming. However, there are some tips to make it easier.
For example, having a central repository for all the different kinds of data that could be required during a specific kind of testing can be extremely helpful. If a new test case is created or the existing one is modified, it can be checked first in this repository if the required data exists. If not, the required data can be fed into the test environment before it is used for testing. This will minimize the amount of manual effort needed to prepare and maintain the test environment.
Also, ensuring that the test environment is a good size for a given project by considering the number of teams and the amount of data they will need to perform their tests can help reduce the time required for preparing and maintaining the test environment. Having too much or too little data in the test environment can be problematic, so it is important to determine the optimal size for the test environment during the planning phase of a project.
Finally, it is important to consider the security of the test environment. If the test environment needs to contain sensitive client or employee data, it is crucial to ensure that the proper de-identifying techniques are applied. Additionally, if the test environment requires frequent refreshment of the data, this should be planned in advance with the team requiring access to the data.
In addition to these tips, it is also essential to ensure that the right test data management tools are in place and utilized. These tools can automate the processes of identifying, creating, provisioning and providing access to test data. This can help to eliminate the need for manual processes and improve the overall quality of the test process.
4. Test Results
A robust test data management process enables functional testing teams to verify the correctness of the end-to-end business application in production by generating actual images of production environments. This verification allows the testing team to discover any errors at an early stage, decreases time-to-market and reduces costs associated with rework due to production issues.
An efficient test data management strategy provides several benefits for organizations including the ability to manage all the data in a central repository, allowing multiple teams to access and use it. Moreover, it helps reduce the risk of a single team having the sole control over a significant amount of data. Additionally, the process automates the various steps of preparing test data such as masking, cloning, and generation. This significantly speeds up the process and ensures that only quality test data is used.
The main problem with a lot of current test data management tools is that they are not integrated with other software testing technologies such as service virtualization and automation, which can significantly speed up the process of creating a test environment. Additionally, most of them also require expert training and experienced resources to operate. Hence, the test data provisioning process takes a longer time for individual sprint teams to have their own copies of the required test data.
The best test data management solutions allow the testing team to have a bird’s-eye view of the entire application in the form of test cases and requirements, helping them detect any defects and inconsistencies at an earlier stage. This in turn decreases the effort involved in fixing them.