Efficient And Imperative Test Data Management Strategies

Efficient And Imperative Test Data
Management Strategies
With the growing pace and advanced technologies, consumers expect the software/application
to perform all the long, complex, heavy, and useful activities efficiently and in no time. A
successful and efficient software has a high dependency on high-quality data. The process of

test data management involves the creation of test data, analysis of test data, and execution of
the test data extraction process.
Most of the testing professionals use the data that are already in the system for checking the
test data. The conclusions were made based on the passing and the failing of the data, if it
passed, the data in the system is the same as the one you wrote while making the automated
test. And if it failed, it is probably because the data did not match the existing one and has
changed.
The newbies follow this practice, but experienced ones soon realise that a single automated
test procedure doesn’t work for all the test data cases. Instead, test procedures should be
automated to give credible and accurate reports.
Different companies take a different approach to manage test data and increase efficiency. In
this blog, we have listed down three ways in which you can check the test data effectively.
The tale of the three strategies for data testing:
Every data strategy is accompanied by two main components: a creational strategy and a
cleanup strategy. As the name suggests, the creational strategy involves ​test data creation, and
a cleanup strategy involves cleaning up the test data.

The Elementary approach:
This approach has no creational strategy, and hence the name is “elementary approach.” The
automation code written here does not contribute to data creation which can be useful in
testing. And the same applies to the cleaning of data. The approach does not participate in
cleaning up the data after running the test in the system.
It is true that this approach can not be used in most of the environments but is useful as it
serves as a foundation for other platforms. The application of the elementary approach is
limited to a few cases only, and it is thus imperative for businesses to manage the data in the
system to get the desired results.

The approach functions like, if another user changes the data within the system, the test fails.
And on the other hand, if we want our test case to change the data in the system and check the
result, the re-running of the test will fail. In both scenarios, we would see a competition
between the test cases, and no consistent results can be obtained. And therefore, the
elementary approach doesn’t work.
Refreshing the data source:
The problem faced in the elementary approach can be solved by resetting the data source
itself. This approach is called “ the refresh data source approach.” The data shall get reset
between the test execution. By doing this, it ensures that the system contains the same data
each time when the test run is started. This approach can turn out to be costly as refreshing the
data each time can take hours or even days. Also, more man-hours are required to implement

the approach. You need highly skilled professionals to have a better strategic approach.
The limitation of this approach is the same as that of elementary one; it works with only a few
test suites, applications, and environments. For implementing this approach efficiently, you
need to understand the team’s constraints and align them with the goals expected from the
test results. This approach may not be acceptable by the company’s management, as most of
the testers involved with the project would be sitting idle until the data is refreshing.
The selfish data generation approach:

Since the above approach is time-consuming, the next approach is based on creating a unique
data set for each test execution. This approach is called “selfish data generation.” This approach
doesn’t have a cleanup strategy but does have a creation strategy. It creates a test case that
generates data to check for the functionality and data uniqueness. The race situation is not
encountered in this approach as there is an availability of new and unique data each time to
modify and verify the functionality.
Also, the time is saved for data refreshment, and none of the testers shall sit idle. However, the
limitation faced by using this approach is that the data builds up quickly within the system. This
might not sound like a serious issue, but enough data for testing is never generated.
If the ​IT test environment management ​is healthy, the automated tests run a lot. When a new
small test case is generated, the data size explodes, and this is the drawback of the “selfish
approach.” It only focuses on data creation and not the cleanup.
There are no doubt other approaches towards ​Test data management​, but we have listed
down the basic ones from them. You may need more advanced approaches to go ahead with
the testing and availing consistent results. A well-planned strategy gives accurate, reliable,
viable, and repeatable results.
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Website :- ​https://www.enov8.com