GenRocket Demo 1

GenRocket is a synthetic test data generation system that
allows testers to generate any test data
on-demand, in real-time.

Patented Technology
• GenRocket was awarded a patent for test data generation with
parent, child, and sibling relationships in January 2017.
• Patent US 20140143525 A1 for Systems and Methods for Data
Generation.

The Five Components of GenRocket

4. Receivers

5. Scenarios

GenRocket Terminology Summary
GenRocket Term Think of it like… Example
Domain
A database table A user database table
Attribute
A column in a database
table
A column in the user database table – Name, Email,
etc.
Generator
The component that
generates test data for an
Attribute
A “NameGen” Generator
Receiver
The component that
formats the test data from a
Generator
XML, SQL, JSON, Web Services, JDBC
Scenario
Instructions for the
GenRocket Runtime to
generate test data
A user Scenario that generates user test data

GenRocket Ecosystem
Model test data in the cloud and generate it locally.

Test Data Demo

1. Create Data Model
GenRocket Web is used to model the
data requirements for your test cases.
This data model is a representation of
the same data model in your
application.
3. Manage Scenarios
Downloaded Scenarios are placed
where testers can access them for
their testing needs.
• Shared Git repository
• Test case management tools
• Other testing tools
• CICD pipeline
2. Create Scenarios
From the data model, test data
Scenarios are created to generate
test data for each test case. These
Scenarios are downloaded from
GenRocket Web and run on a
tester’s local machine or on a server.
Model, Create, and Manage Test Data Generation Scenarios

GenRocket Scenarios can
generate test data
anywhere, in any format,
for any type of test.
API Tests
Integration Tests
Populate Any
Database
Excel Files End2End
Jenkins Pipeline
Unit Tests
Any File
Format
Test Data Scenario

1. The application’s
data model is updated
for the next release
2. GenRocket
automatically updates
all test data Scenarios
to match the new data
model
3. Testers are ready to
test using test data that
reflects the new data
model
Test Data Scenario
Scenario Lifecycle Management

Solving The Test Data Challenge / Enabling Agile Testing
The Test Data Challenge GenRocket Solution
1. Delays in Data Delivery: When QA organizations rely on
test data being pruned from production they have to wait
days or weeks for their test data to be delivered.
2. Cumbersome Changes: Since production data is
generic, testers spend a significant amount of time
altering test data to meet their test case requirements.
3. Security Risk / Regulatory Compliance: Production
data by default has sensitive data in it. Without the time
consuming process of data masking, teams risk exposing
sensitive data.
• Financial Services
• Healthcare
• Government
• EU (GDPR – May 2018)
1. On-Demand Data: Any tester on the team can generate
test data in a matter minutes which removes any test data
bottlenecks.
2. Fast Changes: Testers can easily generate test data to
meet their test case requirements. This removes the time
investment for modifying test data.
3. Secure and Compliant: GenRocket generates synthetic
test data so there is no risk of sensitive information being
exposed inside the test environment.

Financial Services Example

• Financial Services
• Real Estate
• Information Technology
• Enterprise & Consumer Software
• Government
Customer Industry Types

How GenRocket is Disrupting
Test Data Management

1.HigherQualityTestData
Traditional Test Data
Management Solutions
Types of Test Data
1. Realistic
2. Random
3. Conditioned
1. Realistic
2. Random
3. Patterned
4. Conditioned
5. Negative
6. Permutations
Test Data for New Data
Models and Applications
Production test data does not exist
for new models or new applications.
Test data can be generated for
models that don’t exist in
production.
Data Masking Required
Not necessary / can do “synthetic
data replacement” when needed

Traditional Test Data Management
Solutions
Test Data Provisioning Methods
Centralized: Testers have to wait for
TDM manager to procure and deliver new
data sets.
Decentralized: Testers are empowered
to generate their own test data. This test
data is easily sharable to the entire team.
Modifying Test Data
Manual: Testers often have to modify
test data sets to meet their specific
testing needs. Not scalable.
Automated: Testers can easily modify
test data Scenarios to generate the data
they need.
2.FasterProvisioningofTestData

Training Requirements High: Requires extensive training Low: Tester friendly tool
Resource Level
High: Dedicated and specialized
resource
Low: Any member of your testing
team
3.EasiertoUse/LessTrainingRequired

4. Much Lower Cost
Solution Price $25,000 to $400,000+ $1,500/User/Year

Hosting Options

• Success criteria defined in advance
• Up to 3 Projects with up to 5 Domains per Project
• Up to 60 days for the POC
• Weekly Check in Call
GenRocket Proof of Concept (POC)

Create Customized GenRocket Solutions: The GenRocket API
The GenRocket runtime application interface (API) allows programmers
to access the GenRocket Runtime to directly run or modify Scenarios
from their own programs. This allows users to dynamically change test
data on the fly.

GenRocket Demo 1

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GenRocket Demo 1