1. A Must to Know - Testing IoT
Swapnilkumar Prajapati
Senior Consultant, Capgemini

2. Abstract
Forecasts: Gartner says 20.4 billion connected things by 2020 and total spending will reach almost $2 trillion
Essential: Rapidly migrating or developing and rolling out IoT-enabled apps into the mobile app market with high quality
Trade-off: Prioritizing speed over quality can prove very costly, even today
Honorable: Certain aspects of traditional testing will continue
Prospect: New solutions, approaches and frameworks to address demand for the IoT testing
IoT – Internet of Things

3. Complex World IoT Testing
Wide variety of devices
Environmental conditions
Big Data
Cultural diversity
Testing in the field

4. Challenges of testing all things
Many IoT platforms and its expensive - Effort consuming to replicate environment required for the IoT testing
Gadget available for testing might be of inadequate capacity/quality or is not available at the right time
Too many IoT communications protocols to test and lack of standardization
Many groups and third party involved, problem with single thing might affect testing of whole system
IoT security threats on the rise
Fast-moving data and increased load take a toll

5. New Things Needed
for IOT Testing

6. Quick Defect Resolution, Frequent Deployments
Shift in Test Approach
Quick Defect Resolution, Frequent Deployments
TAAU – Test as a User
TBAU – Test by a User
Test element of human interaction
Tailor the testing process according to the customer needs
Altering tool chain accordingly to user feedback

7. Artificial Intelligence and Machine Learning
Redefine Test Design
A new way to look
Test Components
Requirement
Test Flows
Test Execution Engine
Test Data
Test Case Generator
Defects Data
Data Generator
Test Cases
Artificial Intelligence and Machine Learning
High possible number of test cases that could be difficult to achieve through manual test design
Simplify and automate the test design
Risk based assessment

8. Next to Original Data Generation for the Internet of Things
Synthetic data
Next to Original Data Generation for the Internet of Things
Difficult to obtain data
Data Recorders
Generate high volume of data for data processing and analytics validation
Auto play on different devices, simulations and for compatibility testing
Original Data from different devices
Data Structures
Extract
Data Values
Algorithms
Data Generation
Configurations
Data Rules
Data
Masking
IoT Data Generation Framework
System / Service / Database

9. A way forward for the Internet of Things
Test Automation
A way forward for the Internet of Things
Manual
End to End
Test
Automated API Test
Automated Integration Test
Automated Component Test
Automated Unit Test
TAAU, TBAU

10. Service Virtualization Eliminate Testing Roadblock

11. Conclusion
As Internet of Things rises, testing will dramatically evolve
Planning and design will be crucial for the success
Testing will prove more complex and require testing in field as well
Testers must adapt to new platforms, approach and techniques
Automation will become necessary to reduce test cycle
Improvements to network infrastructure, standardization of IoT tools/protocols and testing tools will continue to make the process easier over time

12. References http://www.gartner.com/newsroom/id/2636073
Colin Bull, Sven Euteneuer, Kai-Uwe Gawlik. Testing the Internet of Things - Intelligence is required. 2016

13. Author Biography
Swapnilkumar Prajapati is a Senior Consultant within Capgemini Financial Service Testing Division. He is responsible for testing activities for the ETL Testing, System Integration Testing and End-to-End Testing. He holds a bachelor’s degree in computer engineering and has more than 9 years of relevant industry experience with in-depth knowledge across the software-testing life cycle within insurance and banking areas.

14. Thank You!!!