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

Testing the data


Testing for these volumes of data requires analytics applications to gather, analyze, interpret, and present content from multiple channels, including web and mobile.

Testing analytics applications requires exploration of social media, mobility, analytics, and cloud (SMAC) world. It involves gathering customer data from social and mobile channels, leveraging the data, and storing it in cloud platforms. The three key characteristics of data include volume, variety, and the main challenge to address in testing analytics application involves validating the 6Vs.

Characteristics of data to be tested and testing to be done includes the following things:

  • Data volumes (Test for semantics, distributed processing, and scalability)

  • Data variety (Test for visualization, schemas, and data federation)

  • Data velocity (Test real-time, on the fly integration and on-demand storage)

In addition to bearing in mind the volumes, variety, and velocity of the data to be tested, the testing should be carried...