Abstract: Simulation data associated with a simulation test performed with respect to a first set of training data is obtained. Responsive to a determination that the obtained simulation data satisfies one or more criteria, a second set of training data is obtained, where a size of the second set of training data meets or exceeds a size of the first set of training data. A machine learning model is trained using the second set of training data.

Abstract: An embodiment monitors a code repository for changes made to source code. The embodiment detects a change made to the source code, and upon detecting the change made to the source code, identifies a potentially impacted workflow node of a workflow based on the change made to the source code. The embodiment identifies an execution tree of the workflow that contains the potentially impacted workflow node. The embodiment identifies a set of test cases corresponding to the execution tree. The embodiment generates a test case recommendation based on the set of test cases identified. The embodiment transmits the test case recommendation to a user device.

Abstract: A method of attesting, examining and quickly tracing a software bill of materials includes: registering with a coalition chain to gain access thereto; generating, by an e-wallet of a decentralized application module, a software coalition chain address; changing, by the decentralized application module, an initial software bill of materials into an integration software bill of materials including a software artifact information, software coalition chain address, correlated software information and correlated software coalition chain address; creating a hash tree tracing data by a tracing data creating module; assigning the software coalition chain address to the integration software bill of materials and the hash tree tracing data which are then uploaded to an InterPlanetary File System for acquiring a software content identifier; and assigning the software coalition chain address to the software content identifier to write the software coalition chain address and the software content identifier to the coaliti

Abstract: A system and method for testing an interaction evaluation system may execute a process to control an e-mail application, CRM application and/or a communications application, the communications application hosting one or more of audio and video communications; where the process is provide an audio stream to the communications application, the audio stream including a simulated conversation among a plurality of simulated participants. The process may cause the communications application to conduct a meeting or interaction and may cause an interaction evaluation system to provide a summary for the meeting or interaction. The process may evaluate the functioning of one or more of the e-mail application, CRM application and/or video communications application. A prompt control process may create and provide to an LLM (large language model) prompts to cause the LLM to create text output, and the text output may be provided to a text-to-speech process to produce the audio stream.

Abstract: A method includes receiving, by a microcontroller, a live firmware update (LFU) command from an external host; and downloading, by the microcontroller, an image of a new version of firmware responsive to the LFU command. During a first time period, the method includes initializing only variables contained in the new version that are not contained in an old version of firmware. During a second time period, the method includes updating one or more of an interrupt vector table, a function pointer, and/or a stack pointer responsive to the new version. The second time period begins responsive to completing initialization of the variables.

Abstract: A method of information processing includes obtaining a traffic request for a target service. The traffic request includes a request tag. The method further includes determining a target environment that matches with the request tag and obtaining first deployment information of the target service. The first deployment information of the target service indicates one or more test versions of the target service to be tested, and one or more corresponding environments respectively deployed for the one or more test versions of the target service. The method also includes determining, from the one or more test version, a target test version that is deployed to the target environment and accessing the target service in the target test version in the target environment. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: Stateless caching for code generation using generative artificial intelligence (‘AI’), including: receiving a sequence of input tokens that may include: a prefix comprising one or more input tokens occurring before a reference point in a computer program, a suffix occurring after the prefix in the sequence and comprising one or more input tokens occurring after the reference point in the computer program, and retrieved data occurring after the suffix in the sequence and comprising one or more input tokens based on information accessed from one or more data sources; loading a portion of an embedding of a previously received sequence corresponding to a leading portion of the previously received sequence that matches a leading portion of the sequence; and generating a prompt for suggested code to be inserted into the computer program at the reference point.

Abstract: Provided herein is technology relating to selecting an element in a software application and particularly, but not exclusively, to systems and methods for identifying a target element for testing a software application using artificial intelligence.

Abstract: An apparatus comprises a memory communicatively coupled to a processor. The processor is configured to receive an evaluation request for one or more portions of application data, determine multiple role parameters configured to indicate multiple testing operations corresponding to a portion of the application data, and obtain a server profile from one or more role directories. The server profile is associated with multiple entitlements in the one or more role directories. Further, the processor is configured to determine whether the entitlements at least partially match the role parameters and identify a managed server associated with the server profile. The managed server comprises one or more pods in response to determining that the entitlements at least partially match the role parameters. The processor is configured to assign testing operations to pods in the managed server and instruct the pods to perform the testing operations.

Abstract: A computer-implemented system and method for processing log data in a multi-tenant software environment processes logs from distributed software modules through an automated pipeline. The system implements developer-configured event definitions to identify discrete events using start and end message pairs, while extracting reference text patterns to correlate logs across different modules. A hybrid database structure combines database and vector database capabilities to maintain both filtered data and complete textual context, enabling semantic processing of natural language queries. The system performs automated cross-module correlation through language model processing that extracts structured information based on developer-specified parameters. Two-stage query processing uses language embeddings for semantic matching and automated database query generation to provide contextualized responses.

Abstract: Technologies for verifying operations of software components include a device with a processor and a memory coupled with the processor, the memory including instructions that, when executed by the processor cause the processor to obtain design data indicative of a model of interconnected components of a software system. The instructions also cause the processor to obtain test sequence data indicative of rules pertaining to a sequence of messages to be communicated between a target component of the software system and one or more other components of the model defined in the design data. Further, the instructions cause the processor to perform, in a test environment that emulates the model in which the target component is defined, a set of verification operations to determine a response of the target component to one or more messages generated as a function of the test sequence data.

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.