Power Your IT Business With DataOps (2) - PowerPoint PPT Presentation

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Power Your IT Business With DataOps (2)

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There are no doubt other approaches towards Test data management, but we have listed down the basic ones from them. Let’s take a look at it and get more information about it at www.enov8.com. – PowerPoint PPT presentation

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Title: Power Your IT Business With DataOps (2)


1
Efficient And Imperative Test Data Management
Strategies
2
  • 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.

3
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.
4
  • 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.

5
  • 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.

6
  • 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.

7
  • 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.

8
  • 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.

9
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 61 2 9437 4214
  • Website - https//www.enov8.com

10
Thank You
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