Bro, manage test data like a pro! [QA Fest 2018]

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This document provides guidance on effectively managing test data for software testing. It discusses 6 approaches to preparing test data: 1) using the user interface, 2) inserting data via API calls, 3) direct SQL insertion into the database, 4) using data sets with DBUnit, 5) versioned data dumps, and 6) reusing production data. The key recommendations are to choose the approach that best fits the system under test, speed up test data management with APIs, reuse live data when possible, and mix strategies as needed to build reliable test cases.

Tecnologia

Bro, manage your test data
like a pro!
Mikalai Alimenkou
@xpinjection
http://xpinjection.com

Telegram channel
https://t.me/xpinjection_channel

Test
Data
Application
Driver
Test
Logic
Test components

Try to avoid
dependent tests
to improve
stability and
speed

Data independency as performance driver
• Most dependencies are data related
• Dependent tests = no parallel execution
• Data should be test specific with no reuse
• Use small focused datasets

Regular system example
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS

If you have user
interface, then
you can use it for
testing

#1. Prepare test data using UI
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#1. UI

Easiest approach to start with
Blackbox solution
Clear and simple
Unstable
Too long
Failures chaining

If UI is unreliable,
you can switch to
API level

#2. Data insertion via API
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#2. API

Reuse existing API for tests
Data oriented
Much quicker
Require clear API
Greybox
Coding skills
Existing tools

If you have data,
put it in the
database

#3. SQL for direct data insertion to DB
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#3. SQL

Use internal system knowledge
Flexibility
Quickest way
Whitebox
Support and sync
Too verbose
Direct data access
Caching

#4. Datasets with optional commit
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#4. DBUnit

Additional level of data abstraction
Great tools around DBUnit
Suitable format
Whitebox
Support and sync
Small datasets
Caching
Flexible constraints

#5. Versioned DUMPs
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#5. DUMPs

Support DUMPs for different suites
/serviceA/dump/smoke/v50/data.tar.gz
/serviceA/dump/smoke/v51/data.tar.gz
/serviceA/schema/v51/db.jar
/serviceA/dump/regression/v50/data.tar.gz
/serviceA/dump/regression/v51/data.tar.gz

DB Object Pools
• DB instance
• Heavy domain objects
• Available data roots

Version your test data with schema
Direct data access
Quick
Unclear
Required skills
DUMP preparation via UI
Infrastructure support
Artifact repository

If your live system
produces data, it
can be used for
testing

#6. Production data DUMP
SYSTEM
DB
QUEUE
UI
API
EXTERNAL
SERVICES
API CONTRACTS
#6. LIVE DATA

Real data is always better for testing
Live data
No preparation
Large
Permissions
More realistic cases
Security

Summary
• Use UI for test data if the system is black box for you
• Speed up you test data management with API calls
• Put data directly in DB if system is white box for you
• Actively use Docker to prepare test DB images
• Reuse live data if possible for complex systems
• Mix approaches to fill test DB and store dumps to build
Docker images
• Be happy unicorn in the rainbow world ;)

It is not an easy
task to create
reliable tests, try
hard and know
your tools

@xpinjection
http://xpinjection.com
mikalai.alimenkou@xpinjection.com

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Usually it is hard to analyze personal effectiveness and detect wastes in development process because developer’s work decomposition is not transparent and available for analysis. As a good sample of ineffective process imagine developer, who spends 1 day on task implementation and then reimplements it several times according to code review notes during next 2 days. Or another developer, who is waiting for code review during 2 days, switching context to other tasks, finally gets notes and switches back to initial task, trying to refresh all details in his head. And so on and so forth… Code review tool usage helps to aggregate lots of useful information about any code change at any stage (static analysis, code review, rework, acceptance, integration into main branch). In this talk I’m going to demontrate how this information could be used for detailed analysis of development effectiveness and wastes detection. Based on mentioned analysis you could implement many improvements for your development process and then measure their success.

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Design patters exist for years in software development. Some developers love them, some think they are useless. But design patters has very clear goals: describe common solutions for common problems, create shared language for community, improve understanding and reuse of existing approaches. Test automation has its own set of problems, so there is a set of helpful design patterns for this area. In this talk I will run through all known patterns and describe them in details with several practical samples.

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Working in many companies as consultant, delivery manager or tech lead I have always seen the same mistakes made in test automation process. I could count successful cases on fingers of one hand. Sometimes people don’t understand the true value of test automation, sometimes just could not organize this process spending lots of money and time without any significant result. I want to share 5 top mistakes aggregated from whole my practice and solutions I recommend for them.

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Gamification in outsourcing company: experience report.

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Бытовая классификация тестировщиков с точки зрения разработчика

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Bro, manage test data like a pro! [QA Fest 2018]

1. Bro, manage your test data like a pro! Mikalai Alimenkou @xpinjection http://xpinjection.com

3. Telegram channel https://t.me/xpinjection_channel

4. Test Data Application Driver Test Logic Test components

5. Try to avoid dependent tests to improve stability and speed

6. Data independency as performance driver • Most dependencies are data related • Dependent tests = no parallel execution • Data should be test specific with no reuse • Use small focused datasets

7. Regular system example SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS

8. If you have user interface, then you can use it for testing

9. #1. Prepare test data using UI SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #1. UI

10. Data Registry

11. Easiest approach to start with Blackbox solution Clear and simple Unstable Too long Failures chaining

12. If UI is unreliable, you can switch to API level

13. #2. Data insertion via API SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #2. API

14. Reuse existing API for tests Data oriented Much quicker Require clear API Greybox Coding skills Existing tools

15. If you have data, put it in the database

16. #3. SQL for direct data insertion to DB SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #3. SQL

17. Unit/integration tests

18. Use internal system knowledge Flexibility Quickest way Whitebox Support and sync Too verbose Direct data access Caching

19. #4. Datasets with optional commit SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #4. DBUnit

20. Unit/integration tests

21. API/System level tests

22. The same approach for NoSQL

23. Managed or embedded NoSQL storage

24. Additional level of data abstraction Great tools around DBUnit Suitable format Whitebox Support and sync Small datasets Caching Flexible constraints

25. #5. Versioned DUMPs SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #5. DUMPs

26. Support DUMPs for different suites /serviceA/dump/smoke/v50/data.tar.gz /serviceA/dump/smoke/v51/data.tar.gz /serviceA/schema/v51/db.jar /serviceA/dump/regression/v50/data.tar.gz /serviceA/dump/regression/v51/data.tar.gz

27. Wrap DUMPs in Docker images

28. DB Object Pools • DB instance • Heavy domain objects • Available data roots

29. Version your test data with schema Direct data access Quick Unclear Required skills DUMP preparation via UI Infrastructure support Artifact repository

30. If your live system produces data, it can be used for testing

31. #6. Production data DUMP SYSTEM DB QUEUE UI API EXTERNAL SERVICES API CONTRACTS #6. LIVE DATA

32. Real data is always better for testing Live data No preparation Large Permissions More realistic cases Security

33. Summary • Use UI for test data if the system is black box for you • Speed up you test data management with API calls • Put data directly in DB if system is white box for you • Actively use Docker to prepare test DB images • Reuse live data if possible for complex systems • Mix approaches to fill test DB and store dumps to build Docker images • Be happy unicorn in the rainbow world ;)

34.

35. It is not an easy task to create reliable tests, try hard and know your tools

36. @xpinjection http://xpinjection.com mikalai.alimenkou@xpinjection.com

Notas do Editor

Департаменты развивают принципы локализации ответственности, что очень вредно.
Департаменты развивают принципы локализации ответственности, что очень вредно.
Департаменты развивают принципы локализации ответственности, что очень вредно.
Департаменты развивают принципы локализации ответственности, что очень вредно.
Департаменты развивают принципы локализации ответственности, что очень вредно.
Департаменты развивают принципы локализации ответственности, что очень вредно.

Bro, manage test data like a pro! [QA Fest 2018]

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