Supercharge IoT validation with AI in a hyper-connected world
The evolution of connected products and the Internet of Things (IoT) landscape presents extremely complex challenges for testing. Time-to-market has become vital, thereby making quicker validation cycles non-negotiable. The rapid growth of Artificial Intelligence (AI) is expected to revolutionize IoT testing across different stages of the testing lifecycle and at different layers of the IoT ecosystem (device, gateway, platform) as discussed below:
- Test management: AI can analyze vast amounts of data from sensors and devices to discover hidden scenarios and edge cases and create test cases that manual testers can miss. AI can delve into complex interactions between various components of the IoT ecosystem and empower testers to expand the test coverage for IoT.
- Test execution: AI can analyze past test results and data and create learning models that allow us to continuously refine the test execution pattern and fix issues in execution, leading to more effective testing over time. This continuous self-healing process accelerates defect detection and reduces the risk of IoT system failures.
- Test automation: AI can automate repetitive testing tasks, such as test case generation and execution, enabling testers to focus on more strategic and critical aspects of testing. This can significantly reduce the time it takes to complete a test cycle and make the whole process extremely efficient, improving accuracy and increasing revenue streams.
In this paper, we explain how the future of IoT testing relies on the harmonious integration of AI and human expertise to ensure the robustness and quality of next-generation IoT systems.
