You're reading from Testing Practitioner Handbook Gain insights into the latest technology and business trends within testing domains.

Product type Paperback

Published in Mar 2017

Publisher

ISBN-13 9781788299541

Length 350 pages

Edition 1st Edition

Concepts

Application Testing

Author (1):

Renu Rajani

View More author details

Table of Contents (49) Chapters

Preface

1. State of Digital Transformation – What Has Changed in the Last Four Years (2013-16)? FREE CHAPTER

2. Future of Testing Engagement Models – Are Predictions of Increased QA Spends Justified?

3. The Benefits of Replacing Testing Subcontractors with Managed Testing Services

4. Digital Quality Assurance in a Factory Model

5. Crowdsourcing – Enabling Flexible, On-Demand Testing COEs

6. Testing Goes an Extra Mile over Weekends

7. Testing in Agile Development and the State of Agile Adoption

8. Agile and DevOps Adoption are Gaining Momentum

9. Does the Rise of DevOps Undermine Agile?

10. Role of Automation in DevOps Life Cycle

11. Assessing the State of Your DevOps Adoption with DevOps Benchmarking Approach

12. Accelerating DevOps – ChatOps is the New Cool

13. Behavior-Driven Development (BDD) Using Gherkin in Agile/DevOps Environment

14. Automating Configuration Management for DevOps Test Environments

15. Automated Test Data Management in the DevOps Environment

16. Testing in DevOps Life Cycle Using Microservices Architecture

17. Automated Test Environments for DevOps

18. Service Virtualization as an Enabler of DevOps

19. Best Practices in Identifying Regression Test Cases

20. Accessibility Test Automation in DevOps Environment

21. Performance Tuning of Java Applications

22. Testing Mobile Applications – Key Challenges and Considerations

23. Testing Analytics Applications – What Has Changed in SMAC World

24. Migrating Applications to Cloud Environments – Key Testing Considerations

25. How Should a Tester Adapt to Cloud – Call for Change of Mindset among Testers

26. On-Demand Performance Testing on Self-Service Environments

27. Quality Assurance for Digital Marketing Initiatives

28. Security Dashboard for the Board

29. Applying Robotic Automation to Mobile Applications Testing

30. Key Considerations in Testing Internet of Things (IoT) Applications

31. Algorithmic Business – In Need of Model-Based Testing

32. Making Testing Adaptive, Interactive, Iterative, and Contextual with Cognitive Intelligence

33. FinTech – A New Disruptor in Industry and Implications for Testing and QA

34. Blockchain Technology – Assuring Secure Business

35. Technologies for Digital Supply Chains and QA Considerations

36. Potential Innovations in eHealth-Care – Implications for Testing and QA

37. Trends in the Global Automotive Sector – Implications for Testing and QA

38. Digital Transformation in Consumer Products and Retail Sector – QA Considerations

39. Digital Transformation Trends in Energy and Utilities – QA Considerations

40. Smart Energy and Smart Grids – in Need of Effective Testing

41. Testing Airline Digital Applications – Case for Responsive Design

42. Orthogonal Array Testing (OAT) – an Application in Healthcare Industry

43. Future of Consulting in the Era of Digital Disruption

44. Future of Testing in the Digital World

45. Future of Testing – Career Opportunities

46. Robotics and Machine Learning Combined with Internet of Things – What Could This Mean for Indian Services Industries

A. References

Index

Technologies supported

The cognitive testing applies to all types of applications and is not just limited to web interfaces. The key principle behind cognitive testing is the ability of the system to access and analyze large volumes of data (both structured and unstructured). The system also applies machine learning to extract context sensitive intelligence from the analyzed data and use it to predict future behavior. All kinds of applications, for example, Cloud based, IoT, Mainframe, Database, Mobile, and so on, can benefit from cognitive testing techniques.

Consider an example of testing scenario: IBM Watson can crawl through the defect data, various requirements and design documents, development code base, and production incidents to identify likely points of failure. The intelligence gather can be used to tailor test execution plan accordingly. Platforms such as Watson (it all started out by Watson cracking the jeopardy show as an intelligent machine) have come a long way.

IBM Watson...