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Datamaker User Story - Suresh Jetty

                         

Quality Test Engineer, Mahindra Satyam Computer Services Ltd

Author’s Profile
Suresh Jetty has been associated with Satyam Computer Services Ltd since 2007. As a Quality Test Engineer at Satyam's Testing Practice, his areas of focus include data management and testing. Suresh holds a certification in the GT Datamaker tool and is also an HP Accredited Integration Specialist. He has a degree in Electronics and Communications from VR Siddhartha Engineering College, Vijayawada.

Disclaimer: Any views or opinions presented below are solely those of the author and do not necessarily represent those of the company.

About the GT Datamaker tool
Satyam's initiative on test data management led me to explore TDM (Test Data Management) tools that can be used to perform key activities like test data generation, data masking, database subsetting and off course secure data management. The Datamaker tool from Grid-tools is a one-stop solution for all of these TDM activities. There are other contemporary TDM tools, but none of them are sufficient when it comes to the execution of the combination of TDM activities at a single place.

As a part of my research on different tools for TDM, I have evaluated tools on data masking, generation, data management and subsetting. This evaluation lead me to believe that the GT Datamaker tool is the most efficient and user-friendly.

The Datamaker tool can be used to perform test data generation, masking, subsetting and extraction by means of different Datamaker inbuilt techniques and customizable functions as per the requirement.

Right from the starting of the development (white box testing might be performed here which includes unit testing and functional coverage) until maintenance for all types of testing, Datamaker can provide quality test data.

FIG1: Usage of Datamaker for quality data in different phases of software

Salient Features of Datamaker

1. Inheritance of test cases that have been designed enables reflection of changes made in the parent test case to the child test cases

2. Easy database subsetting

3. Easy and powerful data masking techniques

4. Database code coverage will be assured with the “All Pairs Testing”

5. Quick creation of accurate and high volume of data

6. Powerful version control and security

7. Supports large number of databases like Oracle, DB2, MS-SQL, MySQL, Sybase, etc.

8. Can be integrated with test automation tools like QTP, Quality Center, Load Runner, Forecast and Soap UI

FIG2: Basic Test Data Management activities using Datamaker

Data Generation

There are various inbuilt techniques used in Datamaker for test data generation, some of them are listed below.

1. Datamaker provides simple right-click functions to populate data into the required number of fields in a given table

2. Data can be taken directly from source database which can be easily altered if required

3. All pairs test data generation will help in quick data generation and ensures the maximum database code coverage

4. Inheritance is a key feature of data maker reducing the risk of generating the data multiple times

Data Masking
Datamaker basically uses three modes to mask the data;

1. Using variables

2. Using customizable functions where it can support a variety of functions (e.g. using predefined functions like hash keys, credit cards masking functions, etc which can be selected from a simple drop down box)

3. Right-click and select the options like randomize range, incremental values, fixed value replacement, referring to seed tables data etc.

Database Subsetting
This involves the separation of test data from the original data source.

Some of the advantages of database subsetting setting include:

1. Avoiding risk based database cloning methods for the creation of database for testing environment - generally cloning is done to replicate the database, this may include unwanted data as well which occupies more space and proves to be cost

2. Making data available at module level, thus avoiding the risk of interacting with unnecessary data during testing

At a granular level, the project hierarchy in Datamaker can be represented as shown below.

FIG3: Brief hierarchy of a project in Datamaker

Click here to read Suresh's Q&A with Grid-Tools