In telecommunication applications, user equipment (UE) typically refers to devices such as smartphones, tablets, laptops, and other types of devices that provide end users with many different capabilities including the capability to communicate and exchange content with each other and with Web servers over a network. The definition of user equipment can be expanded to also include the Internet-of-things (IoT), which are devices that provide the capability to collect and exchange data amongst themselves and with servers over a network. User equipment can communicate over networks through a wired or wireless medium using different types of network protocols such as but not limited to the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) standard.
The number of UE devices is growing rapidly. New types of devices and new versions of existing types of devices, with diverse capabilities, are continuously and rapidly being introduced. Thorough testing of the devices under many different scenarios is essential to verify that the devices will function correctly.
Automated testing is usually relied upon because it is scalable to large numbers of devices and can be accomplished quicker, more systematically, and at less expense than, for example, manual testing. This is turn can increase test coverage and reduce time-to-market, increasing reliability while reducing the cost to both manufacturers and consumers.
However, there are aspects of conventional automated testing paradigms that are costly. Automated testing is currently performed in large-scale, centralized laboratories that include a shielded room that prevents radio frequency (RF) signals that might interfere with the testing from entering or escaping. Devices to be tested are shipped in quantity to the laboratories, which can contribute to the cost of testing. While automated to a large degree, aspects of the testing still require significant manual support, such as equipment maintenance, test case delivery, device profile delivery, test data collection, data analytics and reporting, and consulting, for example. These factors contribute significantly to the cost of testing.