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…optimise your IT investments
Successful approaches for Test Data Management
Philip HowardResearch Director – Bloor Research
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Agenda
What is test data management?
What would you like from test data?
The options for creating test data
Important considerations
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What is TDM?
“The provisioning of the hayloft” by Herman Johannes van der Weele
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What is TDM for?
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What’s it all about?
Poor quality software costs
The earlier you find errors the less it costs to fix them
Thus you should test early and often
But: that requires that suitable data is available as often as you want to test
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What do you want from test data?
Data when you need it (supporting agile development)
No contention for resources
Test data that is representative of the real data
Test data that preserves the relationships that exist in the real data
Where appropriate, the ability to secure sensitive information
Minimal administrative requirement
Support for different sizes of dataset
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What is it you want 1?
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What is it you want 2?
“Bone of contention” by Nick Hunter
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What is it you want 3?
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What is it you want 4?
Conceptual view of a business entity
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What is it you want 5?
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What is it you want 6?
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What is it you want 7?
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Creating test data
1. Manual cloning or copying
2. Database subsetting
3. Synthetic generation
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Manual approaches
Require significant administrative effort
Require significant understanding of data structure and prone to error
Administration effort directly proportional to agility of testing environment
Tends to mean either:Multiple copies of data for each development/testing team
Which is expensive
Contention between teams for resources
Difficult to ensure that data is representative, that relationships are maintained and that data is secure
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Database sub-setting
Requires minimal administrative effort once set up
Understanding of data structures is automated
Reduces time to create test cases
Shrinks footprint for non-production environments
Can have different sized datasets for, say, unit vs integration testing
Tools available/built-in to ensure that data is representative, that relationships are maintained (ensuring referential integrity) and that data is secure
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Synthetic data generation
Requires full understanding of data structures
Apart from that, zero impact on production environment
Can re-generate data on demand
Data automatically secure
More complex in terms of understanding, compared to sub-setting, but required in some environments
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Important consideration: understanding the data
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Important consideration: refreshing the data
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Important consideration: data masking
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Masking requirements
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Conclusion
Manual methods of generating test data take too long, are too expensive, can’t ensure proper coverage, are inefficient for data masking and are not suited to agile environments
Test data needs to be representative, it needs to cover relevant cases, it needs to be quick and easy to refresh the data and you need sophisticated data masking capabilities
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