Book Image

Testing Practitioner Handbook

By : Renu Rajani
Book Image

Testing Practitioner Handbook

By: Renu Rajani

Overview of this book

The book is based on the author`s experience in leading and transforming large test engagements and architecting solutions for customer testing requirements/bids/problem areas. It targets the testing practitioner population and provides them with a single go-to place to find perspectives, practices, trends, tools, and solutions to test applications as they face the evolving digital world. This book is divided into five parts where each part explores different aspects of testing in the real world. The first module explains the various testing engagement models. You will then learn how to efficiently test code in different life cycles. The book discusses the different aspects of Quality Analysis consideration while testing social media, mobile, analytics, and the Cloud. In the last module, you will learn about futuristic technologies to test software. By the end of the book, you will understand the latest business and IT trends in digital transformation and learn the best practices to adopt for business assurance.
Table of Contents (56 chapters)
Testing Practitioner Handbook
Credits
About the Author
Acknowledgement
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Benefits of automated TDM


The key benefits of the proposed automated TDM solution include the following:

  • Optimization of the process, reducing efforts to identify Test Data by 90%

  • Reduction in the hours spent waiting for testing environments to be restored, resulting in about a 35% saving

  • Reduced subsetting and data masking effort with the use of tools such as ILM Informatica, IBM OPTIM, SAP TDMS, and CA DataMaker

  • Right-sized environments and test data aligned to the testing requirements

  • Accurate and efficient delivery of TDM data in time

  • Cost reduction through reduced storage and infrastructure costs

  • Optimal testing coverage

  • Ability to track data and its usage

  • Masking of production data, and offering production-like quality of test data

  • Ability to create synthetic data to meet new testing requirements

  • Data creation based on different scenarios

  • Ability to run end-to-end test scenarios