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Testing Practitioner Handbook

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

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Product type Paperback
Published in Mar 2017
Publisher
ISBN-13 9781788299541
Length 350 pages
Edition 1st Edition
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Author (1):
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Renu Rajani Renu Rajani
Author Profile Icon Renu Rajani
Renu Rajani
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Toc

Table of Contents (49) Chapters Close

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

Conclusion

What has changed for analytics testing in SMAC world?

To address the testing for big data, one needs to understand the system and prepopulate data in the system. Testers need to create installations to be able to carry out real-time tests.

There are not many testing tools for big data; the testers are learning basic components of big data to be able to use big data design and development tools (the same as developers) to be able to test big data. Tools such as query surge can help test up to 100% data quickly.

Experience in testing EDW applications has helped shorten the learning curve for testers testing big data as they apply the knowledge of extract, transform, and load from DWH to the Hadoop HDFS.

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