A method, computer program product, and/or system is disclosed for identifying special cases for testing an integrated circuit that includes defining interesting cases, preferably by a user; obtaining an instruction from an instruction set architecture (ISA); determining that there is an interesting case for the obtained instruction; computing (i) a size of the input space (I.sub.0) of the ISA, and (ii) an interesting case space (I.sub.i) which is an input space leading to the interesting case for the obtained instruction; obtaining a special case fraction by dividing the interesting case space (I.sub.i) by the input space (I.sub.0); determining a special case fraction (I.sub.i)/(I.sub.0) is less than a threshold; and identifying, in response to the special case fraction being less than the threshold, the interesting case as a special case. In an approach the special case is documented.

An apparatus may include a processor that may be caused to access a plurality of measurements of a device. The processor may provide the plurality of measurements as an input to an autoencoder, the autoencoder being trained based on measurements of devices in working condition and access an output of the autoencoder, the output comprising a reconstruction of the input based on decoding an encoded version of the input. The processor may further be caused to determine whether the device will fail based on the output.

One embodiment includes a monitor module for a first device, wherein the first device is configured to obtain measurement data from a second device, to compare the measurement data to a reference value, and to send a signal when the measurement data in comparison to the reference indicates an error condition. Machine learning can be used, where a head-end is capable of modifying the second device when the reference value so indicates. This enables various embodiments to fix the second device without human intervention

Example aspects include techniques for detecting, for one or more instances of a dependency call from a service to a dependency in the cloud computing platform, the one or more instances of the dependency call having a common set of dependency call inputs, that a value of a dependency call performance metric of the dependency call is outside of a threshold range, providing, to a machine learning (ML) model and based on detecting that the value is outside of the threshold range, the common set of dependency call inputs for the one or more instances of the dependency call, obtaining, from the ML model and based on the common set of dependency call inputs, an expected value for the dependency call performance metric, and determining, based on comparing the value to the expected value, the entity causing the value to be outside of the threshold range.

According to an aspect, a method includes searching for a correlated log identifier in a correlation database based on detecting a metrics-based anomaly. The method also includes providing, in a problem diagnosis, related log information associated with the correlated log identifier based on locating one or more log entries including the correlated log identifier in a same time window as the metrics-based anomaly. The method further includes searching for a correlated metric in the correlation database based on detecting a log-based anomaly and providing, in the problem diagnosis, related metric information associated with the correlated metric based on locating one or more metrics records including the correlated metric in the same time window as the log-based anomaly.

Exemplary methods, apparatuses, and systems include receiving a plurality of read operations directed to a portion of memory accessed by a memory channel. The plurality of read operations are divided into a current set of a sequence of read operations and one or more other sets of sequences of read operations. An aggressor read operation is selected from the current set. A supplemental memory location is selected independently of aggressors and victims in the current set of read operations. A first data integrity scan is performed on a victim of the aggressor read operation and a second data integrity scan is performed on the supplemental memory location.

An apparatus to facilitate transparent network access controls for spatial accelerator device multi-tenancy is disclosed. The apparatus includes a secure device manager (SDM) to: establish a network-on-chip (NoC) communication path in the apparatus, the NoC communication path comprising a plurality of NoC nodes for ingress and egress of communications on the NoC communication path; for each NoC node of the NoC communication path, configure a programmable register of the NoC node to indicate a node group that the NoC node is assigned, the node group corresponding to a persona configured on the apparatus; determine whether a prefix of received data at the NoC node matches the node group indicated by the programmable register of the NoC; and responsive to determining that the prefix does not match the node group, discard the data from the NoC node.

Software performance can be predicted based on different system configurations. In one example, a computing device can receive historical datasets associated with copies of a software application executed by a group of computing environments during a prior timespan. Each historical dataset can indicate respective changes during the prior timespan to at least one performance characteristic of one of the copies of the software application executed by one of the computing environments in the group. Each computing environment in the group can being configured differently than the other computing environments in the group. The computing device can also convert the historical datasets into training data for a machine-learning model, and train the machine-learning model. This can yield a trained machine-learning model configured to generate a forecast of the performance characteristic for the software application over a future timespan.

A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT that defines a computer-based neural network. The method includes training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model, and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value. The method can further include using the identified test input values to test the system under test.

In various embodiments, a method for page cache management is described. The method can include: identifying a storage device fault associated with a fault-resilient storage device; determining that a first region associated with the fault-resilient storage device comprises an inaccessible space and that a second region associated with the fault-resilient storage device comprises an accessible space; identifying a read command at the second storage device for the data and determine, based on the read command, first data requested by a read operation from a local memory of the second storage device; determining, based on the read command, second data requested by the read operation from the second region; retrieving the second data from the second region; and scheduling a transmission of the second data from the fault-resilient storage device to the second storage device.