Curiosity and Lemontree present - Test Data Automation: Move from slow and manual lookups to on-the-fly “find and makes”

© Curiosity Software Ireland Ltd. 2022
Test Data Automation
Move from slow and manual lookups to on
the-fly “find and makes”
Huw Price
Managing Director
Curiosity Software
Huw.Price@Curiosity.Software
@DataInventor

Short facts
• Established 1999
• Currently 50 consultants
• Owned by entrepreneurs
and employees
• Consultancy- or project
deliveries
• Customers in Sweden
and Norway
• Telecom, Banking and
Finance, Public, Energy
and Industry
• Various of partners

Questions & follow up:
patric.helje@lemontree.se
rickard.ridderstrom@lemontree.se
And now...
• 8:15 Welcome – a short introduction
Isabella Nyman, Lemontree
• 8:20-8:50 Huw Price, Curiosity
• 8:50-09:00 Q&A

A perennial problem
Test data bottlenecks are a persistent barrier to delivering
quality software on time or within budget:
Most organisations report that
enhancing test data generation
and provisioning is critical to
making testing more efficient
Capgemini, Sogeti, The World Quality Report 2021-22, p. 9.
2021
What’s been done to
stop test data
sinking time, budget,
and quality?
2022
2020
The average test team
spent 44% of their time
finding, waiting for, or
making test data
Capgemini, Sogeti, Continuous Testing Report 2020, p. 21.

A problem that will get worse if left
unsolved
5 trends that will add to the complexity and demand for data:
1. New technologies adopted alongside the old – how quickly can you find and make integrated data for
architectures that range from Mainframes to Kafka, “AI” and cloud technologies?
2. “Agile”, DevOps and CI/CD – can you provision the right data at the pace of system change?
3. Containerisation, source control and reusable code libraries – can you keep test data up-to-date, giving
the magnitude of changes made as developers rip-and-replace code?
4. Automation in testing – can you make the right data available to automated requesters, at the pace with
which they tear through data?
5. Evolving privacy legislation – can you demonstrate that you’re compliant with all the rules?

How do people find and make their data now?
• Finding data is random
• Finding data is not structured
• If you can’t find data you have to make it or ignore it
• People don’t reuse what other have done
• Data is not integrated with any of the dev/test toolchain
Up to 50% of a testers time can be spent finding
or making data

Unstructured Find and Make
Tester Looking for
Data
• It’s Slow
• It’s Random
• It’s Unpredictable

Finding Data – A Structured Approach
• Split the “business” from the “technical”
• Create shopping carts – aggregated views of data
• Add in extra “business” attributes as they come up
• Connect the find to “allocation” – keep data separate
• Store user searches for replay and analysis – recreate the data when re-
provisioning the environment
• Auto create “covered” combinations of data when you provision any environment
• Integrate your finds into your automation

Structured Find and Make
Tester Looking for
Data
BP
CT
AR
FIN
NM
AC
FI
TP
Test Data Mart
EX
FR
Add Attributes
Not Found
Look for Similar & Modify
Clone Existing
Make New
Allocated Data to test – Read or Transact
T1-2 T2-3 T3-2 T4-2 T5-6 T6-10
Direct into the DB,
Message, UI or API
Business Technical
# Bankruptcies
Currently In default
Late Payments
Age
Fraud Hold

Building a data mart
Finding data --- Business vs Technical
Allocating data
Demo Finding Data

Conditioning your data at the beginning of a sprint or…..
Capture user requests
Create base business cases
Explode or Model Combinations
Find and Make data
Demo Environment Provisioning

Environment or Container Provisioning
TDM – Subset – Mask - Condition
Clone interesting,
rare or bugs
Production
Dev/Test/QA
Conditioning Engine
Mask
Virtual Databases
FIND
SQL1
FIND
SQL2
FIND
SQL3
MAKE1 MAKE2 MAKE3 MAKE4
AD HOC
MAKE5
MODEL
PROCESS MODEL
DATA MODEL
DATA COMBINATIONS

A Quick Summary - Find, Make & Condition
What does it do?
• Builds and captures business queries
• Stores and analyses the data you are looking for
• Breaks down queries to look for similar data
• Calculates the data needed to satisfy the find
• Maps the finds to a make, update, clone
• Models the potential variations of data
• Creates data across multiple systems in any format
• Allocates data to tests based on criteria
How does it help?
• Learns the data that you need to test with
• Builds up a systematic set of data
• Saves time searching for data
• Save time working out what data is needed
• Systematically works out what to make
• Uses the requirements to model your data
• Saves time setting up complex integration tests
• Allows teams to work efficiently
Green == Quality Improvements
Blue == Save time and Speed up
development

Questions and Discussion

Next steps? Let’s chat!
Book us for a free 90 minute workshop – let’s team up to solve your test data
challenges.
− We’ll demo to your team & discuss their challenges with them.
− We’ll create a set of solutions and next steps together.
Visit TestDataAutomation.io and hit “Arrange Demo”.
Follow @CuriositySoft on Twitter and find us on LinkedIn.
Based in Sweden or Norway? Contact Isabella.Nyman@Lemontree.se to solve
your test data needs!

Thank you

Curiosity and Lemontree present - Test Data Automation: Move from slow and manual lookups to on-the-fly “find and makes”

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