Systems and methods are provided that may be implemented to perform failure analysis and/or self-healing of information handling system storage. In one example, an information handling system may perform self-recovery actions to self-heal system storage issues when there is a OS boot failure due to a failure to detect a system storage drive by determining one or more possible recovery actions based on a current system storage drive status retrieved by an embedded controller (EC) or other programmable integrated circuit of the information handling system. In another example, manufacturing quality control analysis may be performed on boot failure information that is collected at a remote server from multiple failed information handling systems.

The present disclosure provides systems and methods for generation of parsing scripts or rules for unstructured or semi-structured system log messages, including systems and methods for identifying and clustering of same or substantially similar system log messages using machine learning. Patterns indicative of the same or substantially similar types system log messages can be generated based on the clustering of the system log messages and calculated similarities of attributes or distances between common features/fields of the system log messages, with the results of the clustering presented for analysis and development or adjustment of parsing scripts.

Apparatus and method for scalable error reporting. For example, one embodiment of an apparatus comprises error detection circuitry to detect an error in a component of a first tile within a tile-based hierarchy of a processing device; error classification circuitry to classify the error and record first error data based on the classification; a first tile interface to combine the first error data with second error data received from one or more other components associated with the first tile to generate first accumulated error data; and a master tile interface to combine the first accumulated error data with second accumulated error data received from at least one other tile interface to generate second accumulated error data and to provide the second accumulated error data to a host executing an application to process the second accumulated error data.

Validating actions in a digital assistant-based application is provided. The system identifies an application with a conversational interface. The system selects an action from an action repository and generates, via a natural language processor, a trigger phrase for input into the application. The system executes the application to process the trigger phrase to identify an action of the application. The system identifies a parameter used by the application to execute the action, and generates, based on the parameter and via execution of the conversational interface of the application, a first query responsive to the trigger phrase. The system generates a first response to the first query for input into the application. The system determines, based on execution of the application to process the first response, a state of the application. The system evaluates the state to determine an error code and provide a notification based on the error code.

Techniques for storage management involve: obtaining information indicating an error of a storage device of a data storage system; if the number of occurrences of the error within a predetermined time period exceeds a predetermined threshold, stopping obtaining the information indicating the error; and generating an event indicating whether the number of occurrences of the error within the predetermined time period exceeds the predetermined threshold for further diagnosis of the error. As a result, errors from the storage device can be automatically managed, which helps to improve the data storage system's capacity to handle different types of errors of the storage device.

A verification information modification device includes processing circuitry configured to acquire, from each verification device that uses verification information of software to verify a file forming the software, an error log relating to erroneous detection that has occurred in the verification device, when it is determined that a same error has occurred in a predetermined number or more of verification devices based on the acquired error log, extract an error log of the error from acquired error logs and create information indicating verification information that has caused the erroneous detection and candidates for modification details of the verification information based on the extracted error log, and output the information indicating verification information that has caused the erroneous detection and candidates for modification details of the verification information.

A method for error management is provided. The method comprises receiving a message call request regarding an error event generated by a software application. The message call request comprises a message ID associated with an error type. In response to the call request a message cache is searched for the message ID. If the ID is in the cache, an error message associated with the ID is returned. The error message provides a description of the error and suggested remedial action. If the message ID is not in the cache, the error message is fetched from a message repository that contains error messages corresponding to respective message IDs. The fetched error message is loaded into the cache and returned. Message call request data is stored in a metrics repository. The message call request data comprises frequency metrics that describe how often the message ID is received.

An embodiment may involve storage containing incident logs and mappings between incident logs and vector representations generated by a machine learning (ML) model. The embodiment may further involve one or more processors configured to: receive, from a client device, a classification request corresponding to an additional incident log; transmit, to the ML model, additional values as appearing in the additional incident log, wherein reception of the additional values causes the ML model to generate an additional vector representation of the additional incident log; obtain confidence measurements respectively representing similarities between the additional vector representation and each of the vector representations corresponding to the incident logs; determine, based on the confidence measurements, a set of one or more incident logs that are semantically relevant to the additional incident log; and transmit, to the client device, representations of the one or more incident logs and their corresponding confidence measurements.

The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information. In this manner, the feedback framework provides sufficient context information associated with the feedback to diagnose and address issues while delivering the feedback and context information quickly and efficiently.

A system includes a cluster of nodes, memory, and a processor, where the cluster includes an application programming interface (API) server and one or more components. The processor is configured to initialize an interface to the API server, where the interface is operable to send status information from the one or more components within the cluster via a single output stream. The API server is configured to modify the single output stream of the API server to output status information associated with a first component of the one or more components within the cluster. The status information is aggregated and it is determined whether the cluster is at a failure point. In response to determining that the cluster is at a failure point, an execution signal is set to false, where the execution signal is accessible to an automation tool in communication the cluster.