In a typical software testing business, the phrase test data strategy means data utilized for testing a specific piece of software. Every software tester understands that a particular software and test data have very strong impacts on each other. Test data represent a strong driving force for automated testing. Automated testing can detect bugs and errors in a software application in a matter of seconds. But with manual testing, you might be required to run the test over again to make sure that the bug has not been hidden by an unnoticed mistake.
Testing automation reduces the time needed to perform these tedious tasks. It is also essential because it enables your team to focus more on writing quality software applications. However, automated testing is not as simple as some would think. In order to benefit from test data across the board, you need to be well-versed in differentiating among different types of test data. Different types of test data are categorized into two:
black box testing: The black box test data refers to those that are not designed for any kind of analysis. These kinds of tests do not require any kind of prior knowledge about what the code does. They are commonly used by testers in real world situations. A good test data strategy should include black box tests. This will reduce the burden of the testers as they won’t have to deal with interpreting and validating complex or live data.
Service virtualization: A test data strategy should also include integration of service virtualization. With this strategy, testing can be streamlined and made easier. It makes full use of the existing data as well as new storage systems and resources. Service virtualization allows testers to test software that runs on specific hardware devices. This will make the testing process easier since there is no need to modify and recreate hardware environments for every test.
Large testing environments: Many companies today are turning to test data strategies that have the ability to simulate large testing environments. These test data strategies make it possible for testers to work within large test spaces and hardware devices without modifying any environment. This is very helpful especially for medium size and small enterprises that don’t have the resources to create and maintain their own test data environments. Service virtualization enables testers to work within these test spaces and hardware devices. This will improve the productivity of the testers since they won’t have to spend time learning and adapting their work flow in a big data environment.
Software testing: A good test data strategy should also have the ability to handle software development testing. Sometimes, software development teams require access to real data to determine if the software being developed is complete and functional. This will be very valuable for the software development team once the software is deployed. The team will be able to easily determine bugs and errors that might pose a threat to the functionality of the software development project.
Test automation: It is important that the test data strategy also includes automation in its tool. Test automation enables testers to test the software and fix the problems manually. This will save the company money, since manual testing may not always yield the desired results. However, it can increase the complexity of the test since there will be more factors to test and check. It is recommended to test automation on the test data set that includes data from different software tests.
There are many more scenarios when an automation strategy will be needed for a test data strategy to work effectively. However, test automation should not be included in the test data strategy to the fullest extent. When automation is integrated with the strategy, test data accuracy will greatly improve. The testers will no longer need to manually test each and every piece of information in the system. They will also be able to make more changes or modifications in the software in response to the automated tests.
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