Abstract: An application migration method from client-server architecture to blockchain architecture is provided. The application migration method includes the following steps: S1: modeling an application program of a conventional client-server architecture and abstracting the application program to a program call tree; S2: defining a taint propagation rule for taint analysis by taking key data as tainted data, to acquire all taint variables in the application program; and S3: identifying a key module of the application program on the program call tree according to a tainted path, and setting a code refactoring rule to perform code refactoring on a method in the key module according to a special program structure.

Abstract: Methods, systems, and devices for automating code analysis, dependency mapping, and adaptive task execution with artificial intelligence are described. In some implementations, a server platform may receive input data including a codebase and associated documentation. The server platform may parse the input data to extract task parameters, generate meta tags describing functions and dependencies, and construct a workflow representation of tasks, the workflow representation including operating-system-level input events and associated state transitions. The server platform may execute the workflow representation by generating the operating-system-level input events and verifying state transitions through a perception layer that captures pre-execution and post-execution states. Each executed input event and corresponding state transition may be logged in a provenance module with cryptographic metadata for secure auditability.

Abstract: The present disclosure relates to the field of computer technology, and discloses a code generation method and apparatus, a computer device, and a storage medium. The method includes: obtaining a target generation position of a code to be generated; extracting context code information corresponding to the target generation position, and generating code prompt information matching the context code information; and inputting the code prompt information into a preset code generation model, and generating, by the preset code generation model, target code corresponding to the target generation position. The preset code generation model is pre-trained based on a prompt information sample.

Abstract: Systems, methods, and machine-readable media are disclosed for a global scheduler that manages jobs across nodes of one or more clusters. The global scheduler receives a request to replicate a volume from a first storage node to a second storage node, the global scheduler handling job scheduling for one or more storage operating systems across one or more clusters. The global scheduler stores a new job in a job queue based on the request to replicate the volume. The global scheduler determines that the new job has a higher priority than other jobs in the job queue. The global scheduler allocates one or more resources of the first storage node to the new job in response to determining that the new job has a higher priority than other jobs in the job queue. The global scheduler sends an instruction to run the new job.

Abstract: A root cause analysis system according to an embodiment of the present disclosure may include: an Analysis tool calculator that operates in a microservice environment and calculates a corresponding analysis tool, which is a member of analyzing a root cause of target software, based on comparison software configured of corresponding independent modules, which are independent modules classified by function to correspond to the target software, which is software to be analyzed; and a Cause analyser that analyzes the root cause of an abnormal operation occurring in the target software using the corresponding analysis tool.

Abstract: A system that includes a failed test detector and a task updater can automatically update tasks associated with consistent failures of software tests in a software development management platform. The failed test detector can use a set of evidence of test (EOT) files that indicate software testing results over a period of time to identify tests that are consistently failing when executed against versions of a software application. The task updater can automatically create tasks associated with such consistently-failing tests in the software development management platform. The task updater can also automatically close existing tasks associated with tests, in the software development management system, if the failed test detector determines that those tests are no longer failing consistently.

Abstract: A system for automating information technology software actions using advanced AI techniques including a processing system, storage medium, a communications interface, a user interface, a natural language processing model or neural network, operable to interface with at least one of an embedded prompt or chatbot prompt and hosted on a control node which communicates with one or more nodes comprised by a node network via the communications interface, and program instructions on storage medium that direct the processing system to receive an instruction from the user interface, process the instruction using the one or more natural language processing model or neural network, along with one or more AI agents, and execute the instruction one of locally or on a node of the node network via the communications interface.

Abstract: As described herein, a system, method, and computer program provide a test environment as a service. The service provides a user interface presenting a plurality of configurable parameters for creating a test environment. The service receives, via the user interface, user input defining configurations for one or more configurable parameters of the plurality of configurable parameters. The service creates the test environment in accordance with the configurations.

Abstract: Automated root cause identification using data flow analysis of plural execution traces. A computer system generates data flow dependency graphs from first and second execution traces an entity. These graphs represent input/output data flows of corresponding executions of the entity. The computer system generates topological sortings of those graphs and identifies output pairings across these graphs based on outputs having common labels and topological correspondence. The computer system identifies output pairing(s) that are mismatched as having different values and, for at least one mismatched output pairing, traverses the graphs in order to identify input pairing(s) that are topological root(s) to the mismatched output pairing(s) and that are causal to the mismatch(es). Each input pairing comprises inputs that have a common label, a common topological correspondence, and mismatched values.

Abstract: An automatic code deployment pipeline with multiple stages for automated build, test and deployment of code updates to a production execution environment includes functionality for detecting end-user latency introduced by the code changes prior to full production deployment of the updates. In response to a request to deploy an updated application (or component thereof) to a full production environment, the pipeline executes the updated application in a less-than-full-production execution environment, and obtains end-user latency metrics during the execution of the updated application. The pipeline determines, based on analysis of the obtained end-user latency metrics for the updated application relative to latency metrics for the non-updated application running in the production execution environment, whether the update causes a latency regression for the application. For a significant latency regression, automated deployment is halted.

Abstract: A Logstash SNMP input plugin is customized to work in an IPv6 environment. A cloned copy of the Logstash SNMP Input Plugin is debugged to identify modifications to make to the cloned copy of the Logstash SNMP Input Plugin to enable operation in the IPv6 environment. The cloned copy of the Logstash SNMP Input Plugin is updated to implement the modifications to enable operation in the IPv6 environment. The updated cloned copy of the Logstash SNMP Input Plugin is recompiled to generate a custom SNMP Input Plugin. The custom SNMP Input Plugin is installed into Logstash. Logstash with the custom SNMP Input Plugin installed is deployed and tested in the IPv6 environment. Upon discovering further issues, the custom SNMP Input Plugin is debugged. In response to discovering no further issues, Logstash with the custom SNMP Input Plugin installed is released for use in the IPv6 environment.

Abstract: Test cases written to test a software application can be dynamically distributed among a set of software application instances such that different sets of test cases can execute simultaneously in parallel, thereby speeding up testing relative to executing the test cases sequentially. To avoid database conflicts that may occur when different test cases are executed in parallel, each software application instance can be associated with a different database instance. Accordingly, a first test case executing in association with a first database instance can avoid interfering with a second test case executing in association with a second database instance.

Abstract: A data processing system defines a unit test for a dataflow graph comprising a plurality of executable components. The system receives an indication of a portion of a dataflow graph for testing. The system receives a parameter set indicating expected output data to be generated by execution of a component of the graph. The system receives input data for the component. The input data are indicated by the parameter set and configured to test a functionality of the component. The system defines a unit test of the component based on the parameter set. The unit test provides the input data to inputs of the dataflow graph, causes processing of the input data by the component of the dataflow graph to generate output data, and generates results data indicating a correspondence between the output data and the expected output data indicated by the parameter.

Abstract: Storing a set of emulated devices, the set of emulated devices including a plurality of different emulated devices. Obtaining one or more filter parameters. Selecting, based on the filter parameters, a subset of emulated devices from the set of emulated devices. Initiating testing of an application on a particular emulated device of the subset of emulated devices. Obtaining, at run-time while the application is being tested, first error log data from the testing of the application on the particular emulated device of the subset of emulated devices. Parsing, at run-time while the application is being tested, the first error log data, thereby generating a set of first error log data components. Adjusting, at run-time while the application is being tested, one or more weights of a machine learning model based on the set of first error log data components. Providing, at run-time while the application is being tested, the set of first error log data components to the machine learning model.

Abstract: Described herein are systems utilizing a DevSecOps model comprising a CI/CD template, framework interfacing with a CI/CD pipeline that can integrate the assemblage of software components that collectively automate the building, testing, and delivery of applications within various hosting environments. Also described herein are methods utilizing the aforementioned systems.

Abstract: A data processing system includes an array of reconfigurable units and a compiler configured to generate one or more configuration files for an application for execution on one or more reconfigurable processors. The data processing system further includes an execution flow logic which is configured to cause execution of the configuration files on the reconfigurable processors to be dependent upon one or more breakpoint conditions. The data processing further includes a runtime logic configured to execute the configuration files depending upon the breakpoint conditions. A corresponding method is also disclosed herein.

Abstract: Computing systems, methods, and non-transitory storage media are provided for receiving a script to be executed, in response to the script being in a non-executable format, converting the script into an executable script, receiving one or more breakpoints corresponding to the executable script, generating a window to illustrate one or more dependencies among components corresponding to the breakpoints, receiving an input regarding a correction at a breakpoint, and executing the executable script or a portion of the executable script according to the received input.

Abstract: One embodiment comprises a stateless container of binaries and a broker. The stateless container of binaries includes a code memory having stored thereon code for a first version of a first functional component of a content management system, the first functional component executable to provide a first version of a service. The broker may be executable to: receive a request for the service from a client application, the request associated with a user of the content management system; determine that the first version of the service is accessible with regard to the user; determine an available first server that hosts the first version of the service; provide an indication of the first version of the service to the client application; and provide an IP address and a port number associated with the available first server to the client application.

Abstract: A computer can receive, by a software program executing on the processing device, a specification file drafted by a developer of a software application, the specification file having a declarative format and indicating a series of data retrieval tasks to be performed as part of a debugging process for debugging a problem with the software application. The computer can determine operations based on the series of data retrieval tasks indicated by the specification file, the operations being executable to perform the tasks. The computer can execute the one or more operations to perform the data retrieval tasks. By performing the data retrieval tasks, the computer can retrieve debugging information associated with one or more containers associated with the software application. The computer can transmit the retrieved debugging information to a debugging entity for use in debugging the problem with the software application.

Abstract: A system for automating information technology software actions using advanced AI techniques including a processing system, storage medium, a communications interface, a user interface, a natural language processing model or neural network, operable to interface with at least one of an embedded prompt or chatbot prompt and hosted on a control node which communicates with one or more nodes comprised by a node network via the communications interface, and program instructions on storage medium that direct the processing system to receive an instruction from the user interface, process the instruction using the one or more natural language processing model or neural network, along with one or more AI agents, and execute the instruction one of locally or on a node of the node network via the communications interface.