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

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

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

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

New Things Needed for IOT Testing

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

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

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

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

Service Virtualization Eliminate Testing Roadblock

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

References http://www.gartner.com/newsroom/id/2636073 http://www.reuters.com/article/us-johnson-johnson-cyber-insulin-pumps-e-idUSKCN12411L http://www.bbc.com/news/technology-35311447 https://en.wikipedia.org/wiki/Service_virtualization http://www.testandverification.com Colin Bull, Sven Euteneuer, Kai-Uwe Gawlik. Testing the Internet of Things - Intelligence is required. 2016 https://en.wikipedia.org/wiki/Synthetic_data http://ieeexplore.ieee.org/document/7004228/

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.

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