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The Standard for Test Data Management
Solutions with Q-up
ü Generate synthetic data, les,and data pools
üPartial and live anonymization of productiondata for software testing
ü Simulate the business logic in test data
ü Create dynamic test data and simulatehistorically grown data
ü Ensure referential integrity
ü Lower costs for maintenance andtest data creation
ü Simple integration into all widely available test suites
Call us on: 0800-787 32 82or on: +49 (0) 6171 69410-29or email us at: [email protected]
We would be happy to help you explore new appli-cation areas for synthetic test data and the benetsand advantages of working with Q-up. *from a German landline, Mo-Fr, 10 am to 1 pm and 2 to 5 pm
Supply and Support
GFB EDV Consulting und Services GmbH
Obere Zeil 2
61440 Oberursel, Germany
Tel: +49 (0) 6171 69410-0
Fax: +49 (0) 6171 69410-11
www.q-up-data.com
• Wizards for Oracle & SQL databases
- Simple read-out of business logic
- Automatic generation of Q-up projects
- Preserves referential integrity
• Disassociation of test data
Compliance & data protection
- Live anonymization of production data
- Security for clients and external test teams
- Safe data migration to new systems
• Integration into all widely available test suites
*
A Real-Life Solution
®
Ranorex
Impressum
Publisher & Editor:
GFB EDV Consulting und Services GmbHObere Zeil 2, 61440 Oberursel, Germany
Managing Directors: Bernhard Baumgarten,Oliver Mächold,Michael Völker
HRB: 5878 Amtsgericht Bad Homburg
Michael Völker (V.i.S.d.P.)
Contact: [email protected]
Tel: +49 (0) 6171 5060-60
Fax: +49 (0) 6171 5060-66
Image Rights:Title: © fotomek - Fotolia.comP5: © fffranz - FotoliaP6: © bbostjan - istockphoto.com
Copyright © 2013 GFB EDV Consulting undServices GmbH, Oberursel.All rights reserved.
www.q-up-data.com
The Evolution of Test Data Management
The Author
Oliver Mächold is responsible for Q-up Sales &Marketing at GFB EDV Consulting und Services GmbHin Oberursel, Germany. [email protected]
Preamble
As part of a series of White Papers about test data
management, this document discusses the ano-
nymization of data with Q-up. One of the main goals
of test data management is to use data that are realis-
tic, but not real. This is important in order to comply
with data protection legislation but also with indus-
try- and company-specic guidelines. But any deci-
sion whether to use synthetic or anonymized data
for testing purposes must also take commercial and
quality issues into account. Only in very few cases is
it possible to determine the data to be anonymized
automatically or with the assistance of s oftware. This
means that an analysis of the data is required very
early on in the process, when test cas es are created.
In the subsequent design of the test data, the results of
this analysis can be applied in Q-up. To support the
analysis stage, it is advantageous to employ a specic
process model. As part of our 6-phase process mod-
el for test data management, GFB offers a range of
tailored services to ensure an efcient and effectiveimplementation of the test cases.
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The anonymization process with Q-up is made up of 2 steps:
1) Designing the anonymization solution with Q-up Visual Designer and the Import Wizard.
2) Running the anonymization with the Q-up AutoLoader
Anonymizing data with Q-up
Figure 1 – Overview of the anonymization process in Q-up
Analysis of the data sources
To begin with, we have to dene which data should
be imported for anonymization. With the database
systems MS-SQL Server, Oracle DB Server, DB/2 andMySQL, you can use the res pective Q-up Wizards for
this tasks.
The Wizards:
• analyze the database structure,
• recognize any dependencies within the
databases for import,
• make recommendations for editing, and
• generate templates for use in Q-up.
When anonymizing at les (such as VSAM/ISAM
les), the templates for generating the required sen-
tence structures must be created manually. If a con-version from EBCDIC to ASCII is required, you can
use predened Q-up user functions for this.
After the analysis of the data sources, each data s ource
will have a set of templates available which maps the
record structure of the underlying data sources. Next
you can dene, which data content should be ano-
nymized in what form and in which sequence (order
of the templates in tasks).
Anonymizing the record contents
The are many different functions available for anonymizing
the contents of records. These range from simply concealing
certain elds with “xxxxxx” or generating random strings
for elds to the context-sensitive replacement of values ac-
cording to business rules. These business rules can be as
simple or as complex as required and with Q-up's Test Data
Language (TDL) you can map them to the anonymization
process.
Figure 2 – Q-up Visual Designer
Step 1
Generating anonymized data volumes
Depending on whether data should be changed “in place”
(i.e. source and target database are identical) or whether re-
sults should be sent to an external database, the anonymized
data may need to be saved in a different way. Q-up's concept
of templates and tasks gives you all the exibility you need
in this respect. This means that you can add steps to the task
which update the data in the source database, write them to
the target environment via a staging database or send them
as a at le or XML document to a different target system, as
required. Q-up supports a wide range of systems including
all standard IT products (including legacy systems).
Context sensitivity example
Existing addresses are replaced by other real addresses, sofor exampleFrank Schmidt, Grüneburgweg 6, 60322 Frankfurt am MainbecomesFranz Schäfer, Frankfurter Straße 16, 63263 Neu-Isenburg.In addition, the names are similar (length, initials) to main-tain the distribution in large volumes of data.
www.q-up-data.comThe Test Data Generator
