13 results about "Application error" patented technology

Techniques are provided for artificial intelligence (AI) based application error detection and resolution. Extensive amounts of time and resources are consumed by service providers when attempting to resolve application errors experienced by customers. Unfortunately, a service provider may spend tedious amounts of manual effort to evaluate and solve an error that is already known or already solved. The techniques provided herein reduce the amount of time and resources involved in detecting and resolving errors associated with applications. In particular, an error mapping is generated for a current troubleshooting case to resolve for an application. The error mapping is compared to error mappings of previously resolved troubleshooting cases. If a match is found, then a troubleshooting action associated with a previously resolved troubleshooting case is suggested or executed. Otherwise, a service ticket is created for solving the current troubleshooting cases.

The invention relates to the technical field of front-end application error repair, and provides an intelligent front-end error repair method based on a large model, which comprises the following steps of: calling a JavaScript global error monitor to capture context information during abnormal operation and performing structured packaging; converting a structure field in the context data packet into a corresponding error description text, inputting the corresponding error description text and an error pattern label generated based on historical error case clustering into a large language model for fine tuning of a front-end error repair task, and generating a JavaScript repair patch matched with the running exception; and verifying the repair patch in the sandbox environment, and deploying the repair patch in a hot update mode after the repair patch passes the verification. According to the invention, the integrity of key data required by fault positioning is ensured, and the security of user data is also ensured; in the patch generation stage, clustering analysis is conducted on historical error cases, error pattern labels are generated, the labels serve as prompt prefixes to be injected in the large language model fine adjustment process, and the repair accuracy under a specific error scene is effectively improved.

Methods, systems, and computer-readable storage media for a software debugging system that leverages a LLM in combination with an entity matching model to propose solutions for mitigating errors. In some examples, the LLM is used to generate embeddings that are used to identify two or more matched historical errors. The matched historical errors are potential matches to a current error that needs to be resolved. The entity matching model is used to determine a matched historical error from the two or more matched historical errors. The LLM is also used to generate a proposed solution to the current error based on a historical solution to the matched historical error. The proposed solution can be executed to resolve the current error.

Disclosed herein are a system, method, and computer program product embodiments for recommending knowledge resources to resolve errors at a source and / or target system. For example, a request to post data to a target system is received from a source system. A determination is made that an error occurred with respect to the request. An error context for the error is determined. The error context comprises information describing the error and information describing the system. A representation of the error context is provided as an input to an ML model. The ML model is configured to predict a knowledge resource for resolving the error based on the error context. A prediction indicating the knowledge resource for resolving the error is received from the ML model. A recommendation to apply the knowledge resource on the source and / or target system is provided via a user interface based on the prediction.

The invention provides an operation prompting method which can be applied to the technical field of big data. The operation prompting method comprises the steps of obtaining a database version number and a database error code sent by a database in response to an operation failure of an application program on the database; matching the database version number and the database error code with a database error code in a pre-constructed error code mapping library to obtain an application error code, the error code mapping library at least comprising a mapping relationship among the database version number, the database error code and the application error code; on the basis of the application error code, a prompt strategy is matched in an operation prompt library, and the operation prompt library at least comprises a mapping relation between the application error code and the prompt strategy; and popping up the window according to the prompt strategy. The invention further provides an operation prompting device, equipment, a storage medium and a program product.

To discriminate whether or not an application program constituted of a plurality of developed packages can be used by computer equipment.SOLUTION: The apparatus includes a storage unit 33 that stores the developed application program, an error occurrence state detection unit 31 that monitors an execution state when the application program is executed in the computer device and detects an error occurrence state, and a display control unit 32 that converts information on the error occurrence state detected by the error occurrence state detection unit into display information on whether the application program is usable by the computer device and causes a display unit to display the display information.SELECTED DRAWING: Figure 2

Systems and methods for enhancing application resiliency are provided. Some embodiments involve receiving an issue correction request from an application. Some embodiments involve sending an authentication token request to an authentication server and receiving an authentication token. Some embodiments involve creating a synthetic monitor instance in response to receiving the authentication token. In some embodiments, the synthetic monitor instance invokes a microservices instance that detects an application error. In some embodiments, the synthetic monitor instance creates a message. In some embodiments, the synthetic monitor instance sends the message to a service operations manager. In some embodiments, the service operations manager invokes an information technology solutions router. In some embodiments, the information technology solutions router requests a runbook. In some embodiments, the information technology solutions router invokes the runbook to respond to the issue correction request.

The application discloses a kind of component running method and system of low code platform, belong to electric digital data processing technical field, the method includes the following steps: for component setting sandbox isolation environment;Load the component, and establish second window for component;To the component is style isolation, scope isolation and code isolation;In the second window, the component is run;Based on proxy mode, the component is monitored.Through sandbox, independent second window, style isolation, scope isolation, code isolation and monitoring in running etc. multiple isolation modes, component is run in low code platform, improve the reuse rate of component, external component is isolated from main application, guarantee the running performance and stability of main application, avoid error due to component and lead to main application error or crash;Ensure that component does not contaminate main application.

The embodiment of the invention provides a GPU rendering exception diagnosis method and device and a storage medium. The diagnosis method comprises the steps of determining whether a reason for GPU rendering exception is an application error or not based on a display result rendered by a GPU and a display result rendered by a CPU; under the condition that the application program error is excluded, according to first dump data generated in the GPU rendering period, whether the reason for the GPU rendering exception is that the application program calls the GPU drive program error or not is determined; and determining that the reason of the GPU rendering exception is a window management synthesizer error or a GPU drive program error according to second dump data generated during the working period of the window management synthesizer under the condition of excluding the error of calling the GPU drive program by the application program. The diagnosis method improves the diagnosis efficiency.

Exemplary implementations relate to a method for error detection in an LLM application. The method comprises obtaining an error observation related to an application error, evaluating the error observation using a natural language processing model, and determining the cause of the application error based on the evaluated error observation. The method can be used, for example, for diagnostics during the application's production operation.

An example method includes receiving, by an application server system and from one or more client computing devices, application error data associated with at least one error that occurred during execution of at least one application, receiving mapping data that provides a mapping between (i) library-dependent source code of the application(s) and (ii) at least one third-party library from which the library-dependent source code is loaded during execution of the application(s), determining, based on the application error data and the mapping data, a match between the library-dependent source code and at least one portion of the application error data, attributing the at least one error to the at least one third-party library, generating library error data associated with the at least one third-party library, and sending, to at least one third-party library development system, the library error data.

Technology is disclosed for diagnosing and mitigating errors in computer applications. One implementation facilitates determining a root cause of an error affecting a computer application. A root cause of a computer application error is efficiently determined using a set of fault profiles associated with the computer application. A fault profile includes information regarding a particular fault, such as a failing computer resource, which may be the root cause of another error or condition that is detected during the operation of the computer application. A fault profile can also include telemetry data that typically occurs on the computer system in connection with the particular root cause fault. Upon detecting an error, telemetry data from the time of the error is compared against the set of fault profiles, and a fault profile that is similar is determined. Based on the similar fault profile, the root cause of the error is determined.

Systems and methods for enhancing application resiliency are provided. Some embodiments involve receiving an issue correction request from an application. Some embodiments involve sending an authentication token request to an authentication server and receiving an authentication token. Some embodiments involve creating a synthetic monitor instance in response to receiving the authentication token. In some embodiments, the synthetic monitor instance invokes a microservices instance that detects an application error. In some embodiments, the synthetic monitor instance creates a message. In some embodiments, the synthetic monitor instance sends the message to a service operations manager. In some embodiments, the service operations manager invokes an information technology solutions router. In some embodiments, the information technology solutions router requests a runbook. In some embodiments, the information technology solutions router invokes the runbook to respond to the issue correction request.