Latency of in-system test (IST) execution for a hardware component of an in-field (deployed) computing platform may be reduced when a value of a physical operating parameter can be changed without rebooting the computing platform. A test (e.g., patterns or vectors) is executed for varying values of the physical operating parameter (e.g., supply voltage, clock speed, temperature, noise magnitude/duration, operating current, and the like), providing the ability to detect faults in the hardware components.

Methods and apparatus are described by which artificial intelligence (AI) is used to enable the rapid development of reliable test suites for web and mobile applications. An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite.

A system and method are provided for testing features of an embedded system. The system includes a low-powered computing device communicatively coupled to a control application interface, a sensor interface, and a robotic interface. The low-powered computing device may receive sensor signals generated during a test, provide sensor data corresponding to the sensor signals, receive commands for the test, and provide instructions for movement of a robotic handler corresponding to at least one of the commands for the test. The system also includes a computing device communicatively coupled to the control application interface, an image processing interface, and a database interface. The computing device may receive sensor data, receive image data corresponding to images of the embedded system captured during the test, receive tests capable of being performed, and provide commands for the test.

A test controller interfacing between a master computing device and slave computing devices includes a processor configured to launch a master application on the master computing device and a slave application to be tested on each respective slave computing device, with each slave application being the same as the master application. The processor is also configured to receive from the master computing device an input test command along with a test result based on execution of the input test command by the master application, and transmit the received input test command to each slave computing device. In addition, the processor is configured to receive a respective test result from each slave computing device based on execution of the received input test command, and compare each respective test result from the slave computing devices to the test result from the master computing device.

A testing apparatus, testing system, and non-transitory tangible machine-readable medium thereof are provided. The testing apparatus includes a first transmission interface, a second transmission interface, a storage, and a processor, wherein the processor is electrically connected to the first transmission interface, the second transmission interface, and the storage. The storage stores a test procedure, wherein the test procedure includes a test item. The storage also stores a piece of expected information corresponding to the test item. The processor reads the test item of the test procedure. The processor determines a test result of a touch mobile device regarding the test item according to the piece of expected information and at least one of a test datum received from the touch mobile device by the first transmission interface and a feedback signal received from a motor controller by the second transmission interface.

The disclosure provides an online test data record and offline data conversion analysis system and a method thereof. In the present disclosure, the test process information of the production line testing system performed on the circuit board to be tested is generated into online test result data in a database file format, and the offline analysis system receives the online test data from the production line testing system. The offline analysis system reads the corresponding data in the online test result data according to the designated data in the data designated instruction, and generates the offline test result data in the designated file format of the data designated instruction, and the offline analysis system perform the data analysis for the offline test result according to the analysis instruction.

Methods and apparatus are described by which artificial intelligence (AI) is used to enable the rapid development of reliable test suites for web and mobile applications. An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite.

A method includes receiving tester configuration data, test pattern data, and tester operation data; configuring a circuit for performing a designated test evaluation; generating a stimulus waveform; converting the stimulus waveform to an analog stimulus signal; transferring the analog stimulus signal to a first terminal of a MTJ DUT at reception of a trigger timing signal; generating time traces based on the trigger timing signal; generating a response signal at a second terminal of the MTJ DUT and across a termination resistor as the analog stimulus signal is transferred through the MTJ DUT; converting the response signal to a digitized response signal indicating its voltage amplitude; and performing the designated test evaluation and analysis function in the configurable circuit based on voltage amplitudes and time values of the stimulus waveform, the digitized response signal, and the timing traces.

A method for testing an integrated circuit, comprising: accessing a database associated with a test template, wherein said test template is configured to test a selected function of the integrated circuit; storing, in said database, data corresponding to at least partial predicted results of one or more random instruction sequences generated based on said test template; generating, by an automated test generation tool, a random instruction sequence based on said test template; executing said instruction sequence by a hardware exerciser, in the integrated circuit; and comparing results of said instruction sequence with said at least partial predicted results, to verify a function of said integrated circuit.

Example implementations relate to automatically rerunning test executions. Some implementations may capture data during executions of a test. The data may include test status data, test rerun data, test owner data, and/or code committer data. Some implementations may also dynamically determine, for a failed execution of the test, a number of reruns to execute based on the captured data. Additionally, some implementations may cause in response to the dynamic determination, automatic rerun executions of the test until one of the rerun executions passes, the rerun executions being performed up to the number of times.