An arrangement for automated testing of mobile devices comprising a learning arrangement for learning how to use test devices that do not match with an earlier already defined test case pattern. In the arrangement the learning arrangement generates instructions for performing a set of tasks. The tasks are then executed in the mobile device being tested. The mobile device provides feedback in form of error/success messages, screenshots, source code, return values and similar. Based on the feedback and earlier accumulated information the learning entity can generate a new set of instructions in order to execute the set of tasks successfully.

An information handling system includes a memory subsystem and a basic/input out system (BIOS). The BIOS performs multiple trainings of the memory subsystem, and each of the trainings is performed at a different temperature. The BIOS stores multiple derating values in a derating table of the BIOS, and each of the derating values corresponds to a respective tap value at a respective temperature. During a subsequent power on self test of the information handling system, the BIOS performs a first training of the memory subsystem, and stores a first set of tap values. During a runtime of the information handling system, a memory controller determines whether a temperature of the information handling system has changed by a predetermined amount. In response to the temperature changing by the predetermined amount, the memory controller utilizes the derating values in the derating table to automatically update the tap values.

A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

A system includes a memory device and a processing device coupled to the memory device. The processing device is configured to switch an operating mode of the memory device between a test mode and a non-test mode. The system further includes a test mode access component that is configured to access the memory device while the memory device is in the test mode to perform a test mode operation.

An operation verifying apparatus of a first embodiment acquires a log indicating the content of a sequence of operations performed on a predetermined device, identifies corresponding functions from the log, and automatically generates a program based on the identified functions. Input data, which is to serve as an argument of each of these functions, is set. Execution sets as well as test scenarios are each structured by combining a program and input data. Then each execution set is continuously executed. As a result, an operation test using a test program is executed.

Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.

Immersive web-based simulator for digital assistant-based applications is provided. A system can provide, for display in a web browser, an inner iframe configured to load, in a secure, access restricted computing environment, an application configured to integrate with a digital assistant. The application can be provided by a third-party developer device. The system can provide, for display in a web browser, an outer iframe configured with a two-way communication protocol to communicate with the inner iframe. The system can provide a state machine to identify a current state of the application loaded in the inner frame, and load a next state of the application responsive to a control input.

This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

Systems are provided for logging transactions in heterogeneous networks that include a combination of one or more instrumented components and one or more non-instrumented components. The instrumented components are configured to generate impersonated log records for the non-instrumented components involved in the transaction processing hand-offs with the instrumented components. The impersonated log records are persisted with other log records that are generated by the instrumented components in a transaction log that is maintained by a central logging system to reflect a complete flow of the transaction processing performed on the object, including the flow through the non-instrumented component(s).

A graph representation of cloud resources and their relationships is generated and maintained to provide insights into impact of incidents affecting cloud resources on others in the cloud environment. Cloud resource data for the cloud resources are obtained and relationships among the cloud resources are determined. Relationships among the cloud resources are determined based on analysis of configuration data associated with the cloud resources from which relationships among cloud resources of different types can be inferred, and external sources may also be utilized to facilitate identification of relationships. A graph representation of the cloud resources and their determined relationships is built where the cloud resource data are stored in vertices with directed edges between the vertices representing the identified relationships. The graph can be analyzed based on various graph algorithms to analyze impact of misconfigured or compromised resources to identify related cloud resources that are or would be affected.