This application relates to a computing system and method for an automated program error repair. In one aspect, the computing system includes a storage, a preprocessing processor, and an automated error repair processor. The storage stores a program code. The preprocessing processor acquires the program code from the storage and preprocesses the program code. Preprocessing includes tokenizing the program code with tokens, converting the tokens into vectors, and adding location information for the tokens. The automated error repair processor receives the preprocessed program code as an input from the preprocessing processor, detects an error in the preprocessed program code, corrects the detected error, and outputs the error-corrected program code. Detecting and correcting the error are performed based on a deep learning result and the location information for the tokens.

Provided is a method and system for identifying and evaluating common cause failures of system components, wherein at least one analytical artifact and machine readable system related to at least one of spatial, topological data and machine readable system related lifecycle data are processed to analyze automatically a susceptibility of system components to common cause failure based on common cause failure influencing factors.

In general, embodiments relate to a method for providing solutions to hardware component failures, comprising: creating a device state chain using a device state path, a current device state, and a next device state for a device; identifying root cause of a hardware component failure in the device using the device state chain; performing a context-aware search in a shared storage using the root cause of the hardware component failure; and obtaining, in response to the context-aware search, a result specifying a proposed solution for the hardware component failure.

In some examples, an electronic device records, in an entry of a time-state data structure that includes a plurality of entries to store respective times, a time in response to invocation of a time-lapse process that lasts a predefined time duration independently of a time clock of the electronic device. The electronic device determines whether times in successive entries of the plurality of entries of the time-state data structure are within a threshold of one another, the threshold based on the predefined time duration. Based on the determining, the electronic device sets a parameter representing a quality of the time clock.

A fault recovery system includes various fault management circuits that form a hierarchical structure. One fault management circuit detects a fault in a functional circuit and executes a recovery operation to recover the functional circuit from the fault. When the fault management circuit fails to recover the functional circuit from the fault within a predetermined time duration, a fault management circuit that is in a higher hierarchical level executes another recovery operation to recover the functional circuit from the fault. Such a fault management circuit is required to execute the corresponding recovery operation within another predetermined time duration to successfully recover the functional circuit from the fault. The fault recovery system thus implements the hierarchical structure of fault management circuits to recover the functional circuit from the fault.

Methods, systems, and computer program products for multi-controller declarative fault management and coordination for microservices are provided herein. A computer-implemented method includes processing information pertaining to at least one fault impacting multiple resources within a given system, wherein respective portions of the multiple resources are managed by multiple independent controllers; determining, by each of at least a portion of the multiple independent controllers and based at least in part on the processing of the information, one or more desired resource states and one or more remediation actions; generating, based at least in part on one or more of the determined desired resource states and the determined remediation actions, a sequential ordering of the determined remediation actions to be carried out by the at least a portion of the multiple controllers; and automatically initiating execution of the determined remediation actions in accordance with the generated sequential ordering.

In one example described herein a system can receive, by a scheduler of a server, a request to execute a quantum algorithm. The system can determine, by the scheduler, a quantum computer system of a plurality of quantum computer systems to execute the quantum algorithm based on a database that stores associations between each quantum computer system of the plurality of quantum computer systems, at least one parameter associated with the quantum algorithm, and error information. The system can transmit, by the scheduler, the request to the quantum computer system for executing the quantum algorithm.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.

Systems, methods, and computer programming products for predicting, preventing and remediating failures of batch jobs being executed and/or queued for processing at future scheduled time. Batch job parameters, messages and system logs are stored in knowledge bases and/or inputted into AI models for analysis. Using predictive analytics and/or machine learning, batch job failures are predicted before the failures occur. Mappings of processes used by each batch job, historical data from previous batch jobs and data identifying the success or failure thereof, builds an archive that can be refined over time through active learning feedback and AI modeling to predictively recommend actions that have historically prevented or remediated failures from occurring. Recommended actions are reported to the system administrator or automatically applied. As job failures occur over time, mappings of the current system log to logs for the unsuccessful batch jobs help the root cause analysis becomes simpler and more automated.

According to an embodiment, a tag reading apparatus acquires tag data of a wireless tag to be learned without transmission from the wireless tag to be learned. The tag reading apparatus extracts a data signal from a reception signal output from an antenna that has received a radio wave from the wireless tag to be learned. The tag reading apparatus learns the extracted data signal in association with correct answer data of the wireless tag to be learned.