This embodiment relates to software verification and in particular to automatic generation of Modified Condition/Decision Coverage (MC/DC) tests scenarios. A system and method for reducing Modified Condition/Decision coverage (MC/DC) test scenarios is described along with selection of test data automatically for an input Boolean expression. An MC/DC test case engine generates optimal test case for a Boolean expression using an algorithm. The optimal minimal number of MC/DC cases generated to for satisfy the MC/DC condition for n number of inputs may be n+1. The MC/DC test engine supports Boolean expression with Arithmetic and Comparison operators. The MC/DC test engine supports Boolean expression with no limitation on number of input variables.

Diagnosing faults in a hardware appliance. Information is read by a hand-held reader from one or more contactless tags associated with one or more components in a hardware appliance. One or more component faults and/or issues are identified based on the read information. A query is formed based on the identified one or more component faults and/or issues. A diagnostic database in the hand-held reader is queried, based on the formed query, and one or more query results are displayed in a ranked order on a display of the hand-held reader. In one aspect of the embodiments, the information read from the one or more contactless tags includes a pointer to a datastore in one of the one or more components. An ad hoc wireless network connection is established with the hardware appliance, and information in the datastore is downloaded over the connection.

Systems, methods, and apparatuses for autonomous functional testing of a processor are described. In one example, a processor includes a plurality of processor cores that are each coupled to a respective power management agent circuit; a cache shared by the plurality of processor cores; and a control register, that when set, causes: a save of a state of a first processor core of the plurality of processor cores to storage, a transfer of control of the first processor core to a power management agent circuit of the first processor core, isolation of the first processor core from the other of the plurality of processor cores by the power management agent circuit, performance of one or more functional tests from the cache on the first processor core caused by the power management agent circuit to generate a test result, removal of the isolation of the first processor core from the other of the plurality of processor cores by the power management agent circuit, and a transfer of the control by the power management agent circuit back to the first processor core.

The disclosure relates to systems and methods of self-healing automations. A system may access an indication of an occurrence of an event, an alert, or a situation detected at a channel in relation to a computer resource, wherein the event, the alert, or the situation indicates an alert state of the computer resource. The system may evaluate the event, the alert, or the situation against system may determine that one or more user-defined conditions for triggering the self-healing automation has been satisfied based on the evaluation. The system may identify a self-healing workflow based on the automation rule. The system may initiate the self-healing workflow to perform one or more remediation operations on the computer system.

Observability-based configuration remediation for use in a computing environment is disclosed. For example, a method includes detecting an incident in a computing environment and obtaining information related to the incident, the information including a dynamic state information set and a static state information set. The method further includes summarizing the information related to the incident as a textual prompt and then inputting the textual prompt into one or more machine learning models such that the one or more machine learning models, in response, generates an output including a resolution to the incident.

A method and system for determining anomaly and fault in open platform communications (OPC) data is disclosed. Through the utilization of at least one processor, the method comprises receiving a historic data from one or more sources for a predefined time period, wherein the historic data corresponds to a historical open platform communications (OPC) data from the one or more sources and an input data from at least one OPC client; analyzing the historic data using artificial intelligence/machine learning (AI/ML) models to identify events in the historic data; identifying patterns associated with the identified events using the AI/ML models; identifying one or more root causes associated with each of the patterns using the AI/ML models; correlating the identified patterns with the identified one or more root causes; and predicting one or more anomalies and faults associated with historic data, based at least on the correlation.

An information handling system configured to perform an input/output (I/O) health check of the I/O device and gather I/O health check data from the I/O health check performed and calculate channel margins based on the I/O health check data. The information handling system is further configured to determine whether there is an outlier among the channel margins and when there is an outlier, then generate a signature associated with the outlier. In addition, the information handling system is configured to compare the signature with signatures from a library of known issues and determine if there is a match between the signature and one of the signatures from the library, then inform a user of a known issue.

A computer-implemented method includes processing, by an Intelligent Root Cause Analyzer (IRCA), an error message. The error message is categorized by an Error Categorizer of the IRCA as an Error Category. A code change related to the Error Category is searched for by the IRCA. An ID of an impacted application or service and a Change Category is received by a Code Repository and Build and Pipeline. The Error Category and the Change Category are compared by the IRCA. A Service Dependency Graph is searched by a Dependency Reader of the IRCA for services that are called by the impacted application or service. A Monitoring system is queried by the IRCA for potentially different error messages and derived Error Categories from the services called by the impacted application or service.

Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.