Methods, systems, and computer readable media for networking testing. In some examples, a system includes a test controller and a training data collector. The test controller is configured for receiving a test case including test case definition information defining a network test for a system under test (SUT); determining test system resource information for test system resources configured to execute the test case; and executing the test case on the SUT. The training data collector is configured for collecting at least a portion of the test case definition information; collecting SUT status information or SUT configuration information or both for the SUT; collecting metadata associated with the test case including at least one test context label; and processing collected data to produce artificial intelligence training data.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.

One or more computing devices, systems, and/or methods for visual troubleshooting a network device setup. Images of the network device setup are provided to the system. A GenAI component processes the images to generate one or more device identifying features. The features are further processed to identify the device. The system utilizes hardware-specific information to prompt the GenAI component to answer troubleshooting-related questions concerning the device setup. The images may be pre-processed to include one or more visual guides to assist the GenAI component.

A method and apparatus for adjusting a boot option of a server, a non-volatile readable storage medium, and an electronic apparatus are provided. The method for adjusting a boot option of a server includes: invoking, in response to detecting failure of an adjustment operation executed for a boot option of a target server, a first detection parameter corresponding to the target server; detecting a connection state of a universal serial bus (USB) port of the target server according to the first detection parameter; and adjusting, in response to determining that the USB port connection state is used to indicate that a target USB port connected to a target USB device is provided on the target server, a boot option of a USB device to a target boot option of the target server.

The present disclosure relates to a method and device for performing performance harvesting, where multiple cores are embedded in a matrix structure and configured to perform their operations independently, for allowing the remaining cores, which operate normally despite some cores not functioning, to independently produce results of operations by harvesting their respective performances, by being configured to test the operations of each of the multiple cores, bypass cores with defects (or faults, fails, etc.), and exclude the defected cores from the operations.

A hardware accelerator includes a plurality of functional circuits, a stream switch, a plurality of direct memory access (DMA) channels coupled to the plurality of functional circuits via the stream switch to stream data to and from functional circuits of the plurality of functional circuits, and a debug and trace unit coupled to the stream switch, wherein in operation, the debug and trace unit monitors a set of data signals to and from the stream switch via wired probes and implements one or more event counters, one or more triggers, and one or more tracers using components internal to the hardware accelerator including one or more registers of the hardware accelerator, and wherein the one or more tracers output trace data packets via the stream switch.

Processor-based system supporting in-field testing using external dynamic random access memory (DRAM) for storing and accessing test scan data. The processor-based system includes a processor that includes one or more central processing units (CPUs) that each have access to resources, such as cache memory, a memory controller to access system memory (e.g., DRAM), interfaces circuits, to perform tasks by executing of program code. The processing-based system includes an internal, built-in testing system that allows the processor-based system to be placed into test mode to perform in-field testing of the processor-based system. To support larger-sized scan data, the processor-based system is configured for the built-in-test system to access test scan data stored in DRAM in the processor-based system in a test mode. In this manner, the DRAM supports storing larger-sized test scan data so that greater in-field test coverage can be performed in the processor-based system.

Processor-based system supporting in-field testing using external dynamic random access memory (DRAM) for storing and accessing test scan data. The processor-based system includes a processor that includes one or more central processing units (CPUs) that each have access to resources, such as cache memory, a memory controller to access system memory (e.g., DRAM), interfaces circuits, to perform tasks by executing of program code. The processing-based system includes an internal, built-in testing system that allows the processor-based system to be placed into test mode to perform in-field testing of the processor-based system. To support larger-sized scan data, the processor-based system is configured for the built-in-test system to access test scan data stored in DRAM in the processor-based system in a test mode. In this manner, the DRAM supports storing larger-sized test scan data so that greater in-field test coverage can be performed in the processor-based system.

This disclosure enables various hardware/software configurations that enable verification of stability of production environments. These configurations are technologically advantageous and improve functioning of computers, because these configurations minimize interference with or disruption of business operations in production environments, while allowing non-technical personnel to perform due diligence of various IT equipment in such environments and enabling iterative feedback to allow for identification of gaps in such diligence.

A test and measurement system includes one or more test and measurement instruments at least one of which connects to a device under test (DUT), one or more memories, a generative artificial intelligence (AI) model connected to the one or more test and measurement instruments, and the one or more memories, and one or more processors to provide an artificial intelligence (AI) assistant as an interface to the generative AI model, present a user interface that allows a user to enter a prompt, use the AI assistant to translate the prompt into one or more queries for the generative AI model, send commands to the test and measurement instrument connected to the DUT to perform one or more tests on the DUT, take results from the one or more tests and convert them to user-interpretable results, and provide the user with results from the prompt at the user interface.