The present embodiments relate to data center outage detection and alert generation. An outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage. The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage.

A plurality of potential fault scenarios are accessed, wherein a given potential fault scenario of the plurality of potential fault scenarios has at least one corresponding root cause, and a representation of the given potential fault scenario comprises a don't care value. An actual fault scenario from telemetry received from a monitored system is generated. The actual fault scenario is matched against the plurality of potential fault scenarios. One or more matched causes are output as one or more probable root cause failures of the monitored system.

In accordance with an embodiment, described herein are systems and methods for supporting a declarative non code self-learning advisory framework in an orchestration based application integration. The systems and methods can provide an advisory framework as a component of an integration platform which can allow declaratively defined recommendations, guidance, warnings etc. to be shown to the consumer of the platform on occurrence of certain events. The advisory framework can provide benefits such as: 1) allowing any entity to declaratively define/modify the rules and advices which will immediately get reflected across the customer fleet without dependency on product's release cadence; 2) where such updates to declaratively defined rules and advices does not involve any code changes to do the product; 3) comprises a structure which is generic and not component specific; and 4) can have self-learning capabilities from the generated product metrics.

A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.

A GUI testing device may be configured to execute a testing state machine for interacting with a software application to generate an initial screen of a GUI. The GUI testing device may be configured to determine a current state in the testing state machine based upon a matching trigger target in the initial screen to a given state. The current state may include an operation, and the operation may associate with a trigger target to operate on. The trigger may include a source state, a destination state, and a trigger target. The operation may include a user input operation, and an operation trigger target. The GUI testing device may be configured to perform the operation on the matching trigger target in the initial screen to generate a next screen of the GUI, and advance from the current state to a next state based upon the trigger.

Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.

Methods and systems described herein automate troubleshooting a problem in execution of an application in a distributed computing. Methods and systems learn interesting patterns in problem instances over time. The problem instances are displayed in a graphical user interface (“GUI”) that enables a user to assign a problem type label to each historical problem instance. A machine learning model is trained to predict problem types in executing the application based on the historical problem instances and associated problem types. In response to detecting a run-time problem instance in the execution of the application. the machine learning model is used to determine one or more problem types associated with the run-time problem instance. The one or more problem types are rank-ordered and a recommendation may be generated to correct the run-time problem instance based on the highest ranked problem type.

A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

A GUI testing device may be configured to execute a testing state machine for interacting with a software application to generate an initial screen of a GUI. The GUI testing device may be configured to determine a current state in the testing state machine based upon a matching trigger target in the initial screen to a given state. The current state may include an operation, and the operation may associate with a trigger target to operate on. The trigger may include a source state, a destination state, and a trigger target. The operation may include a user input operation, and an operation trigger target. The GUI testing device may be configured to perform the operation on the matching trigger target in the initial screen to generate a next screen of the GUI, and advance from the current state to a next state based upon the trigger.

A design support system includes memory, a receiving unit, and an associating unit. The memory stores information on design element classification that classifies a design element included in a product, and information on design requirement classification that classifies a design requirement required for the product. The receiving unit receives technical information regarding a design trouble. The associating unit refers to technical information regarding a design trouble, received by the receiving unit, and associates a classification item in the design requirement classification to which the design trouble belongs and a classification item in the design element classification to which a design element causing the design trouble belongs with each other, along with information on a phenomenon indicating a failure status of the design element included in the technical information.