16683results about "Error detection/correction" patented technology

Activity monitoring of a cloud compute environment based on container orchestration data may be performed by a data platform. For example, the data platform may obtain audit log data generated by a container orchestrator within a cloud compute environment, generate a data model based on the audit log data, and use the data model to monitor the activity for a security issue. The data model may be indicative of activity occurring with respect to one or more containerized applications executing within the cloud compute environment and the monitored activity may be activity with respect to the one or more containerized applications. Corresponding methods, systems, and products are also disclosed.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions. Preventative measures may involve updating behavioral policies and adjusting agent parameters to disincentivize problematic behaviors.

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

A method is provided for optimizing regression testing in a software development environment. The method includes analyzing source code to create a structural dependency graph that maps dependencies between code elements; identifying changes in the source code between a current version and a previous version; mapping the identified changes onto the structural dependency graph to determine affected code elements; selecting a subset of regression tests based on the affected code elements identified in the dependency graph; and executing the selected subset of regression tests to validate the changes in the source code.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Embodiments of the present disclosure include a software solution for generating a connectivity file that forms the connections between signals in a design under test (DUT) and signals in a verification IP (VIP) interface. The connecting signals from the DUT may form a DUT interface and the DUT interface may follow a protocol. The software solution may utilize a generative AI-based tool to identify example connections that also follow the protocol. These example connections may be included in a query to a large language model (LLM) for purposes of improving the results generated by the LLM.

The invention discloses a parameter feature-based test case generation method and a related product. In the scheme, a parameter document of target simulation software is obtained; and extracting parameter feature information of the target simulation software based on the parameter document. And on the basis of the parameter feature information, matching a test case template and a code generation rule in a pre-constructed code knowledge base, and generating an initial test case code. And dynamically optimizing the initial test case code according to feedback data generated in a simulation test process to generate a target test case code. According to the technical scheme, the parameter feature information is automatically extracted, the complex dependency relationship between the parameters is accurately analyzed, the test case is automatically generated based on the knowledge base matching template and rule, software changes are adapted in real time in combination with a dynamic optimization mechanism, dynamic and efficient generation of test case codes is achieved, and the test efficiency is improved. The coverage degree and the adaptability of the test case codes are improved, and then the accuracy, the comprehensiveness and the efficiency of the test are improved.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

Handling of certificates by intermediate actors, including: receiving, by a proxy and from a client, a client certificate and a first private key; generating, by the proxy and based on the client certificate and the first private key, an intermediate certificate; generating, by the proxy and in response to a request from the client to connect to a destination, an alternate certificate for the destination; and providing, to the client, a certificate chain comprising the alternate certificate, the intermediate certificate, and the client certificate.

The embodiment of the invention provides a test case generation method and device.The method comprises the steps that a test case generation task is determined, and the test case generation task carries a target document, a task type and a document type of a to-be-generated test case; analyzing the target document according to the document type by using an information extraction agent to generate an analysis result of the target document; generating an intelligent agent by using a test point, and generating an initial test point according to the task type and the analysis result; and generating a first test case corresponding to the test case generation task by using a test case generation agent according to the initial test point and the analysis result. The analysis logic of a qualification test expert is simulated through a plurality of agents, and the efficiency and integrity of test case output are greatly improved.

The invention discloses a software test result analysis system which comprises a multi-source test data acquisition module, an intelligent anomaly detection engine, a cross-project association analysis module, a visual decision support terminal and a self-adaptive feedback optimization module. The multi-source test data acquisition module integrates heterogeneous test tool data and performs standardization processing; the intelligent anomaly detection engine realizes dynamic anomaly detection by using an LSTM model and a reinforcement learning algorithm; the cross-project association analysis module completes cross-project data association by means of code tracking, a knowledge graph and the like; the visual decision support terminal visually displays an analysis result in the form of a three-dimensional thermodynamic diagram and the like; and the adaptive feedback optimization module forms closed-loop control and dynamically adjusts a test strategy. The system effectively solves the problems that according to a traditional method, multi-source data cannot be fused, anomaly detection is not sensitive, and visual analysis is lacked, efficient analysis of software test results and optimization of the test process are achieved through cooperative work of all the modules, and the test efficiency and the software quality are remarkably improved.

The invention discloses a multi-agent-based programmable logic controller structured text generation method. The method comprises the following steps: constructing a programmable logic controller programming task solution knowledge base; five large model agents are constructed, including a knowledge retrieval agent, a task planning agent, a structured text generation agent, a verification agent and an error correction agent. According to the method, through mutual cooperation of multiple agents and flexible calling of various tools and models, the knowledge deficiency of a large language model in the programming aspect of the programmable logic controller is made up by utilizing a retrieval enhancement generation technology, the illusion problem of the large language model in the process of generating the structured text of the programmable logic controller is effectively reduced, and the reliability of the structured text of the programmable logic controller is improved. And the success rate and the expansibility of automatically generating the structured text of the programmable logic controller are improved.

The invention relates to the technical field of computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

The invention discloses a log quality detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a multi-modal input source; performing reverse anchoring operation in a dynamic analysis stage of the running log to establish direct mapping between the running log and the source code; compiling the abstract syntax tree based on the source code, establishing a grammar dependence graph according to the abstract syntax tree, and traversing the abstract syntax tree to construct a structured data set; based on the running log, obtaining a quality detection result through structured analysis, and generating a semantic detection result by using a large language model; performing cooperative operation on the structured data set and the grammar dependency graph to generate a code log coverage score, and performing arrangement according to a quality detection result and a semantic detection result to obtain an abnormal mode feature; and performing feedback adjustment based on the code log coverage score and the abnormal mode feature in combination with a preset correction rule. The method can efficiently and accurately realize log quality detection, and can be widely applied to the technical field of data processing.

The invention discloses a test case generation method and device, equipment and a medium, and relates to the technical field of software testing, and the test case generation method comprises the following steps: storing request data subjected to structured processing into a seed case library; the request data are various types of request data in the operation process of the service system; determining a cue word according to the target information based on the large model, and generating an initial test case through the cue word and a generation rule; the target information comprises characteristics of request data in the seed case library, an interface document of the tested software and a business logic specification; executing the initial test case according to the test request of the tested software, performing statistical analysis on the code coverage rate of the tested software in the execution process, and screening the initial test case according to an analysis result to obtain a screened test case; and after the execution is finished, filtering the screened test cases according to the first execution result of each test case to obtain a target test case. Therefore, the testing efficiency and quality can be improved, and automatic testing is achieved.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The subject technology includes an evaluation system for agentic applications. The evaluation system may use one or more evaluation applications to grade the performance of an agentic application based on one or more performance metrics. Scores determined for individual metrics may be combined using a set of weights to tailor the importance of each metric in the overall performance evaluation to a particular industry or application. An optimization engine may improve the performance of target agentic applications that are deficient in one or more metrics by training a portion of the agent application on a training dataset that includes example responses that score well for the one or more metrics where the target applications are deficient.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The invention discloses an intelligent element positioning method and system based on AI and a dynamic feature library, and the method comprises the steps: carrying out the anomaly detection and abnormal data capture, executing an element positioning operation through an automatic script, and recording a page DOM tree structured snapshot when the positioning is judged to be failed; multi-modal feature extraction: analyzing the DOM tree, and extracting an XPath / CSS path and semantic attributes of a target element; performing dynamic feature library retrieval and matching, calculating DOM structure similarity, and taking a high-similarity rule as a correction basis if the high-similarity rule exists; an AI intelligent rule is generated, and candidate positioning rules are generated through the classification model; performing script correction and real-time feedback, replacing the failure positioning statement and updating the dynamic feature library; and generating a structured report, and recording abnormal data. According to the method, the AI is combined with the dynamic feature library, so that the intelligent correction of element positioning in the automatic test process is realized, and the stability and efficiency of the automatic test are improved.

An illustrative storage management system is configured to provide persistent data storage to workloads managed by a container orchestration platform deployed in a cluster of nodes. The storage management system is configured to receive a request comprising identifiers indicative of a plurality of layers associated with a virtual machine to be run in the cluster, generate, based on the request, a virtual machine disk image comprising the plurality of layers, the virtual machine disk image configured to be used to run the virtual machine in the cluster, and provide the virtual machine disk image in response to the request. In some implementations, the virtual machine disk image is used by a virtual machine handler associated with the container orchestration platform to run the virtual machine in a virtual machine type pod that is managed by the container orchestration platform.

The invention discloses a software development task intelligent allocation method and system based on a multi-dimensional capability portrait, and the method comprises the following steps: 1, obtaining multi-source development data generated by a developer in a development process and demand description data of a to-be-allocated software development task, and carrying out the preprocessing, and forming a structured data set; according to the method, a multi-dimensional ability portrait covering technical ability, project experience, collaboration attributes and performance is constructed, a privacy-protected distributed learning mechanism is adopted for dynamic updating, dominant and implicit requirements of tasks are analyzed in combination with natural language processing, complexity and dependency are calculated, and the performance of the performance is improved. A dynamic task feature vector corresponding to a capability feature vector dimension is constructed, and meanwhile, a self-adaptive adjustment mechanism based on real-time data monitoring and online learning is designed to form data closed-loop feedback, so that the problems of one-sided capability evaluation, staticizing task demand analysis and lack of the self-adaptive adjustment mechanism are comprehensively solved; and accurate and intelligent distribution of software development tasks is realized.