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.

Abstract: Techniques for managing containerized workloads in a container computing environment are disclosed. For example, a method comprises the following steps. The method predicts a composite time delay value for initializing an instance of a containerized workload for executing a microservice within a container computing environment. The method then computes at least one target resource utilization parameter, based on the predicted composite time delay value, for use by the container computing environment.

Abstract: Arrangements for machine learning-based code development and release are provided. In some examples, a request to release code may be received from a developer portal and may include the code and a type of release. Based on the type of release, one or more requirements for release may be identified by a rules engine. Code testing data responsive to the one or more requirements may be retrieved from the developer portal and a machine learning model may be executed using the code, type of release and code testing data as inputs. The model may output a release authorization decision. If the release authorization decision includes approval of the release, the code may be deployed to a production environment. If the release authorization decision includes a denial of the request to release the code, the code may be returned to the developer portal for further modification or testing.

Abstract: A cloud code development system, method, apparatus and device, which relate to the technical field of computer, in particular to for cloud code development and online coding technology is disclosed. The system include: a client including an Agent and a Remote-Server; the Remote-Server is configured to obtain a code input by a user, and synchronize the code input by the user to the cloud server through stream synchronization; the cloud server is configured to establish a coding virtual machine corresponding to the Remote-Server; write the code synchronized by the Remote-Server through stream synchronization into the virtual machine; run the code via the coding virtual machine to generate a corresponding task; the Agent is configured to pull the task from the cloud server and send the task to the Remote-Server; the Remote-Server being further configured to execute the task.

Abstract: A method for testing software in an edge building device of a building, the method includes executing, by one or more processors of the edge building device, a first version of the software to perform a first set of one or more processing tasks for the edge building device, and testing, by the one or more processors of the edge building device, a second version of the software configured to perform a second set of one or more processing tasks for the edge building device, testing the second version of the software including: executing the second version of the software together with execution of the first version of the software on the edge building device, and evaluating, by the one or more processors of the edge building device or by causing a separate computing device to perform the evaluation, a performance of the second version of the software.

Abstract: The present disclosure discloses are an implementation method and apparatus for an application installation on a smart Point of Sale device. The method includes: starting, by an application providing a system interface, an installation service for the mobile management application when judging that a mobile management application update package path received in a broadcast is not empty. According to the saved mobile management application update package, it starts to silently install a new version of the mobile management application. If the installation is successful, the mobile management application update package is changed to the mobile management application successful update file, and the new version of the mobile management application is started. If the installation fails, the mobile management application update package is changed to the mobile management application update failure file, and the old version of the mobile management application is started.