Power Your IT Business With DataOps (2)

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Efficient And Imperative Test Data Management
Strategies
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• 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 doesnt 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.

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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.
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• 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 doesnt work.

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• 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.

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• 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 teams constraints and
  align them with the goals expected from the test
  results.
• This approach may not be acceptable by the
  companys management, as most of the testers
  involved with the project would be sitting idle
  until the data is refreshing.

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• 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
  doesnt have a cleanup strategy but does have a
  creation strategy.

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• 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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Contact Us

• Company Name Enov8
• Contact Person Ashley Hosking
• Address Level 5, 14 Martin Place, Sydney, 2000,
  New South Wales, Australia.
• Phone(s) 61 2 8916 6391
• Fax [phone]
• Website - https//www.enov8.com

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