Artificial intelligence for monitoring a portable electronic device testing arrangement

1 . An automated method comprising:

determining, via a machine learning (ML) model, a list of virtual devices used for an automated testing arrangement for portable electronic devices, wherein the listed virtual devices correspond to registered portable electronic devices, and wherein the listed virtual devices each comprise a graphical user interface (GUI);

testing, via the automated testing arrangement, one or more first portable electronic devices, wherein the testing comprises the GUI of a particular listed virtual device interacting with a testing container that includes at least one physical portable electronic device corresponding to the particular listed virtual device;

during the testing, monitoring, via the ML model, the automated testing arrangement for the one or more portable electronic devices to obtain information;

comparing, via the ML model, the information with the list of virtual devices; and

based at least in part on the comparing, determining issues with virtual devices used for the automated testing arrangement.

2 . The automated method of claim 1 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more virtual devices that are out of market.

3 . The automated method of claim 2 , further comprising:

providing, via the ML model, an alert to a user of the automated testing arrangement that one or more virtual devices that are out of market.

4 . The automated method of claim 1 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more second portable electronic devices are designated for use in the automated testing arrangement.

5 . The automated method of claim 1 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more of the one or more first portable electronic devices are not a registered portable electronic device.

6 . The automated method of claim 1 , further comprising:

determining, via the ML model, portable electronic devices available for including in the one or more first portable electronic devices.

7 . The automated method of claim 1 , further comprising:

determining, via the ML model, additional virtual devices available for inclusion on the list of virtual devices.

8 . The automated method of claim 1 , wherein the one or more first portable electronic devices comprise one of a smartphone, a tablet, or a laptop computer.

9 . An automated testing arrangement for portable electronic devices, the automated testing arrangement comprising:

one or more processors; and

one or more non-transitory media comprising instructions stored thereon, the instructions being executable by the one or more processors to cause the one or more processors to perform one or more actions comprising:

determining, via a machine learning (ML) model, a list of virtual devices used for the automated testing arrangement for portable electronic devices, wherein the listed virtual devices correspond to registered portable electronic devices, and wherein the listed virtual devices each comprise a graphical user interface (GUI);

testing, via the automated testing arrangement, one or more first portable electronic devices, wherein the testing comprises the GUI of a particular listed virtual device interacting with a testing container that includes at least one physical portable electronic device corresponding to the particular listed virtual device;

during the testing, monitoring, via the ML model, the automated testing arrangement for the one or more portable electronic devices to obtain information; and

based at least in part on the comparing, determining issues with virtual devices used for the automated testing arrangement.

10 . The automated testing arrangement of claim 9 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more virtual devices that are out of market.

11 . The automated testing arrangement of claim 10 , wherein the one or more actions further comprise:

providing, via the ML model, an alert to a user of the automated testing arrangement that one or more virtual devices that are out of market.

12 . The automated testing arrangement of claim 9 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more second portable electronic devices are designated for use in the automated testing arrangement.

13 . The automated testing arrangement of claim 9 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more of the one or more first portable electronic devices are not a registered portable electronic device.

14 . The automated testing arrangement of claim 9 , wherein the one or more actions further comprise:

determining, via the ML model, portable electronic devices available for including in the one or more first portable electronic devices.

15 . The automated testing arrangement of claim 9 , wherein the one or more actions further comprise:

determining, via the ML model, additional virtual devices available for inclusion on the list of virtual devices.

16 . The automated testing arrangement of claim 9 , wherein the one or more first portable electronic devices comprise one of a smartphone, a tablet, or a laptop computer.

17 . One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform actions comprising:

determining, via a machine learning (ML) model, a list of virtual devices used for an automated testing arrangement for portable electronic devices, wherein the listed virtual devices correspond to registered portable electronic devices, and wherein the listed virtual devices each comprise a graphical user interface (GUI);

testing, via the automated testing arrangement, one or more first portable electronic devices, wherein the testing comprises the GUI of a particular listed virtual device interacting with a testing container that includes at least one physical portable electronic device corresponding to the particular listed virtual device;

during the testing, monitoring, via the ML model, the automated testing arrangement for the one or more portable electronic devices to obtain information;

comparing, via the ML model, the information with the list of virtual devices; and

based at least in part on the comparing, determining issues with virtual devices used for the automated testing arrangement.

18 . The one or more non-transitory computer-readable media of claim 17 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more virtual devices that are out of market, and wherein the actions further comprise:

providing, via the ML model, an alert to a user of the automated testing arrangement that one or more virtual devices that are out of market.

19 . The one or more non-transitory computer-readable media of claim 17 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more second portable electronic devices are designated for use in the automated testing arrangement.

20 . The one or more non-transitory computer-readable media of claim 17 , wherein determining issues with virtual devices used for the automated testing arrangement comprises determining one or more of the one or more first portable electronic devices are not a registered portable electronic device.