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Data Driven Testing Is More Than an Excel File
İstanbul November 22, 2019
Test Automation & Digital QA Event
Mehmet Gök
#TAS19
About Me
Bs. Indsutrial Engineer
Ms. Computer Engineer
Telco Industry
Project Manager
Technical Analyst
Test Automation
Test Lead
Mehmet Gök
Agenda
Frameworks
Approaches
Test Data
Management Concepts
Service
Virtualization
ApplicationTools
Framework selection depends on the context,
the environment and even the team.
Frameworks
Automation (or Manual?) Frameworks
A scripting technique that
uses data files to contain the
test data and expected
results needed to execute the
test scripts. *
Data Driven Testing
* ISTQB gloassary
A scripting technique in
which test scripts contain
high-level keywords and
supporting files that contain
low-level scripts that
implement those keywords.*
Keyword Driven Testing
• Modular Testing
• Hybrid Testing
• Behavior Driven Development
Other Frameworks
The maintenance of the test data becomes more difficultday by day with the
complexity of your software under test increases and it becomes scary.
Test Data
Management
Concepts
Test Data Management Concepts
Subsetting
It is the process of moving a data set from one data source
to another, with all its relationships and dependencies.
Synthetic Data Generation
It is the process of producing nonsense data that does not
represent a real person, institution or any other confidential
data.
Data Integrity
A term describing that data is complete, reliable,
referentially integrated, valid and usable.
Masking
To create a derivative that is structurally and formally
similar to the actual data, but which cannot be estimated.
Data Integrity
Duplicate records may
result false positive bugs.
Redundancy
All those verifications are
for accurate and usable test
data.
Accuracy & Usability
Domain related data must
remain same to protect the
integrity of meaning.
Domain Integrity
Resource: Data Integrity Means Everything by Mike Butrym at https://www.getzephyr.com/insights/data-integrity-means-everything
Missing data may result
false positive bugs.
Completeness
Orphan records may result
false positive bugs.
Referential Integrity
Invalid data may result false
positive bugs.
Validity
The success of test automation begins with choosing
the most appropriate test data management
approach to your application and data.
Approaches
If a subset is to be copied from
one source to another, the data
model must be fully constructed
and given to the test data
management tool.
Data Modelling
Data discovery helps to raise
awareness about data and
eliminate data-driven errors.
Data Discovery
The reduction of errors and costs
caused by the test data is
avoidable by appropriately
preparing the test data.
Profile Test Data
Subsetting
It is very likely that personal
information will be transferred to
the test environment as a result
of subsetting.
Masking
Static Data
Masking
Static data masking is the masking process at the
database layer when creating a database copy or a
subset of production data.
Dynamic
Data
Masking
Dynamic data masking is the process of masking by
putting a proxy between the database and the
application layer which works on network layer or
database layer.
Dynamic Data Masking With Postgres
github.com/emin100/pg_ddm
Synthetic Data Generation
Database level generation
- Data structure should be known very well
in order to generate data synthetically.
- Synthetic data generation engines works
with rule-sets.
Application level generation
- No need for data modelling & discovery
- Service virtualization techniques needed
on 3rd party integrations.
- Synthetic data generation software
(Probably API) should be designed
Data
Modelling
Data
Discovery
Rule Set
Design
Service virtualization is a must of independent and
unlimited testing and synthetic data generation.
Service
Virtualization
Stubs, Hubs and Mocking vs Service Virtualization
Stubs Hubs/Drivers Mocking
Tools
Tools
Tool Selection Criterias
Learning Curve
Is it simple enough to use
immediately?
Regulations
Does it meet regulations like
GDPR?
Speed
Will it keep up with your
regression running frequency?
Cost
Is it worth the cost compared to
its benefit?
Decentralization
Can anyone create test data
within their authority wherever
they want?
Interoperability
Does it work in your environment?
Application
On-the-fly Test Data Generation – A Simple Case
Create an individual customer
Test data need: Create a customer who is
older than 18, is a Turkish citizen and is a man
TR Identity Number Generator
An Identity number generator method is needed
to get a TR Identity Number that will return a
person (>18, Male, Turkish Citizen)
Create Customer Method
You can use the same method that your
application uses to create customers so you can
create test data.
Identity Share System (KPS-NVI)
In order to gather a synthetically generated
customer KPS service should be virtualized
Data Driven Testing Is More Than an Excel File
Thanks!
/mehmetg
To get in touch;
mehmetgok.com
mhmtgk@gmail.com

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Data Driven Testing Is More Than an Excel File

  • 1. Data Driven Testing Is More Than an Excel File İstanbul November 22, 2019 Test Automation & Digital QA Event Mehmet Gök #TAS19
  • 2. About Me Bs. Indsutrial Engineer Ms. Computer Engineer Telco Industry Project Manager Technical Analyst Test Automation Test Lead Mehmet Gök
  • 4. Framework selection depends on the context, the environment and even the team. Frameworks
  • 5. Automation (or Manual?) Frameworks A scripting technique that uses data files to contain the test data and expected results needed to execute the test scripts. * Data Driven Testing * ISTQB gloassary A scripting technique in which test scripts contain high-level keywords and supporting files that contain low-level scripts that implement those keywords.* Keyword Driven Testing • Modular Testing • Hybrid Testing • Behavior Driven Development Other Frameworks
  • 6. The maintenance of the test data becomes more difficultday by day with the complexity of your software under test increases and it becomes scary. Test Data Management Concepts
  • 7. Test Data Management Concepts Subsetting It is the process of moving a data set from one data source to another, with all its relationships and dependencies. Synthetic Data Generation It is the process of producing nonsense data that does not represent a real person, institution or any other confidential data. Data Integrity A term describing that data is complete, reliable, referentially integrated, valid and usable. Masking To create a derivative that is structurally and formally similar to the actual data, but which cannot be estimated.
  • 8. Data Integrity Duplicate records may result false positive bugs. Redundancy All those verifications are for accurate and usable test data. Accuracy & Usability Domain related data must remain same to protect the integrity of meaning. Domain Integrity Resource: Data Integrity Means Everything by Mike Butrym at https://www.getzephyr.com/insights/data-integrity-means-everything Missing data may result false positive bugs. Completeness Orphan records may result false positive bugs. Referential Integrity Invalid data may result false positive bugs. Validity
  • 9. The success of test automation begins with choosing the most appropriate test data management approach to your application and data. Approaches
  • 10. If a subset is to be copied from one source to another, the data model must be fully constructed and given to the test data management tool. Data Modelling Data discovery helps to raise awareness about data and eliminate data-driven errors. Data Discovery The reduction of errors and costs caused by the test data is avoidable by appropriately preparing the test data. Profile Test Data Subsetting It is very likely that personal information will be transferred to the test environment as a result of subsetting. Masking
  • 11. Static Data Masking Static data masking is the masking process at the database layer when creating a database copy or a subset of production data.
  • 12. Dynamic Data Masking Dynamic data masking is the process of masking by putting a proxy between the database and the application layer which works on network layer or database layer.
  • 13. Dynamic Data Masking With Postgres github.com/emin100/pg_ddm
  • 14. Synthetic Data Generation Database level generation - Data structure should be known very well in order to generate data synthetically. - Synthetic data generation engines works with rule-sets. Application level generation - No need for data modelling & discovery - Service virtualization techniques needed on 3rd party integrations. - Synthetic data generation software (Probably API) should be designed Data Modelling Data Discovery Rule Set Design
  • 15. Service virtualization is a must of independent and unlimited testing and synthetic data generation. Service Virtualization
  • 16. Stubs, Hubs and Mocking vs Service Virtualization Stubs Hubs/Drivers Mocking
  • 17. Tools
  • 18. Tools
  • 19. Tool Selection Criterias Learning Curve Is it simple enough to use immediately? Regulations Does it meet regulations like GDPR? Speed Will it keep up with your regression running frequency? Cost Is it worth the cost compared to its benefit? Decentralization Can anyone create test data within their authority wherever they want? Interoperability Does it work in your environment?
  • 21. On-the-fly Test Data Generation – A Simple Case Create an individual customer Test data need: Create a customer who is older than 18, is a Turkish citizen and is a man TR Identity Number Generator An Identity number generator method is needed to get a TR Identity Number that will return a person (>18, Male, Turkish Citizen) Create Customer Method You can use the same method that your application uses to create customers so you can create test data. Identity Share System (KPS-NVI) In order to gather a synthetically generated customer KPS service should be virtualized