88 results about "Test agent" patented technology

Systems and methods for an Artificial Intelligence (AI) agent evaluation framework include operating, in a test environment, an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; providing the AI agent with one or more requests; receiving a response to each of the one or more requests; and evaluating performance of the AI agent based on responses to each of the one or more requests. The one or more requests can be LLM-generated variations of a seed request either for testing the AI agent's ability to respond to queries or to test the ability of the AI agent to ignore malicious requests.

The invention discloses a software development method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: receiving a development demand of a user through a natural language interaction interface, and capturing context state information of a current development environment; and a historical solution of the development demand is retrieved based on the dynamic knowledge graph through the product agent. And when the historical solution based on the development demand is retrieved, generating an executable code by encoding according to the hierarchical code generation strategy based on the historical solution through the architect agent. And performing grammar verification, operation verification and demand conformity detection on the executable code by combining the context state information based on a hierarchical verification mechanism through a test agent to obtain a verification result. And when the test agent judges that the verification result meets the development requirement, summarizing the executable code into a final code implementation scheme through the summarizing agent, and pushing the final code implementation scheme to a user. And the reuse rate and the quality control capability of code development are improved.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

A topology-reprogrammable test environment is provided that can support the needs of CI / CD / CV in the field. The system disclosed provides a highly scalable network architecture to simplify the implementation of network slicing, TaaS and network CI / CD, and solves problems related to the complexity of cloud-native network (CNN) deployments. A Network Cell (NC), comprises or consists of a Containerized Network Function (CNF), a Containerized Digital Twin (CDT), and a Containerized Test Agent (CTA). The CDT has at least two personalities, e.g., an emulator of the CNF in the same NC or a nodal of the CNF. The choice of personality of the CDT is controlled by the CTA of the NC. A number of NCs use a 3D IP address to interconnect and form a new kind of CNN over the infrastructure of VRs.

The invention discloses a distributed testing method and system based on micro-service architecture. The method comprises the steps of generating a test task and a fault injection task according to a test plan; distributing each test case to a plurality of test agents according to the test task, so that the plurality of test agents respectively execute the corresponding test cases, and the plurality of test agents and the plurality of micro-service instances of the tested system run on a plurality of service nodes in a one-to-one manner; injecting a corresponding fault in an execution environment of the tested system according to the fault injection task; and collecting and summarizing log data related to the test, and outputting a test result and the influence of the fault on the test result according to the log data. According to the embodiment of the invention, a complete test process from test plan generation to test result analysis of the tested system is supported, high cooperation of fault injection and test execution is realized through accurate control of fault injection, and meanwhile, the distributed test system constructed based on the micro-service architecture can be adaptively expanded according to the scale of the tested system.

The invention is applicable to the technical field of interface testing, and provides an artificial intelligence-based interface testing optimization method, which comprises the following steps: constructing a multi-dimensional interface knowledge graph of a tested system, the multi-dimensional interface knowledge graph comprising but not limited to static structure information, business semantic information and running state feature information; based on the multi-dimensional interface knowledge graph, the driving state and the action space, deploying a test agent based on reinforcement learning; in the test agent, according to a preset multi-target reward function, executing a target-oriented exploration action, and generating a dynamic test case; according to the method, the twinborn test environment of the tested system is constructed for the test agent to perform exploratory test and generate the test case set, the value density of the test cases generated by the method is high, defects can be effectively found, the test cases are associated with interface service semantics and operation rules, and the reliability of the test cases is improved.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The invention relates to a Web-based CAN bus remote test system and a Web-based CAN bus remote test method. The system comprises an application server side, a Web side, a data server side, a test agent side, an equipment rack and a test sample, wherein the Web side performs real-time communication with the application server side based on a SignalR framework, and the equipment rack and the test agent side are mounted on a CAN bus together; wherein the Web side obtains a user instruction and sends the user instruction to the application server side, and the application server side sends corresponding rack configuration information, test case information and HMI configuration information to the test agent side according to the user instruction; and the test agent side controls the test equipment rack to simulate a communication scene of the test sample based on the CAN bus under different working conditions according to the received information, captures a CAN bus message in real time, analyzes and processes the CAN bus message into a format set in the HMI configuration information, and then pushes the CAN bus message to the Web side. According to the invention, efficient monitoring and remote control of CAN bus equipment testing can be realized.

The invention discloses an interface testing method and device and electronic equipment. The method relates to the technical field of computers, and comprises the following steps: receiving an interface test request sent by a test user; analyzing the interface test request to obtain a test item and test data for testing the target interface; a target test agent corresponding to the test item is determined from the multiple test agents, and the test agents are used for executing interface test operation; and calling the target test agent to execute the test item according to the test data to obtain a test result of the target interface. Through the interface function testing method and device, the problems of low accuracy and efficiency of manual interface function testing in related technologies are solved.

The invention relates to the technical field of testing, in particular to an unmanned vehicle group collaborative testing system based on middleware and interface definition, which adopts a layered architecture design and comprises a testing application layer, a testing core layer, an interface definition layer, a middleware layer and a testing agent layer. By adopting the testing system which is defined based on middleware and an interface and takes an interactive command line interface as a core, the efficiency and the accuracy of command lines and the flexibility of automatic and interactive debugging are ingeniously combined, and challenges caused by isomerism are effectively solved by utilizing a standardization technology; therefore, in multiple dimensions such as test efficiency, user experience, control precision, automation degree, system compatibility, expandability, repeatability and reliability, the method is obviously superior to the prior art.

The invention belongs to the technical field of automobile electronic testing, and particularly relates to an intelligent OTA automatic testing method and system in a multi-dimensional environment, and the method comprises the steps: a testing management platform configures a testing scene and a testing case, schedules a testing task to a cloud server to simulate a real OTA cloud service, and cooperates with a multi-dimensional environment simulation cluster to construct a virtual testing environment; deploying a real vehicle test agent to execute a test case and collect real vehicle data, and utilizing a digital twin synchronization engine to realize bidirectional data synchronization of a simulation environment and a real vehicle environment; collecting heterogeneous data of each key node of the simulation environment and the real vehicle environment in real time, and uploading the collected heterogeneous data to the big data platform for storage; and based on heterogeneous data stored by the big data platform, intelligent analysis is performed by using an intelligent analysis and evaluation engine, and system closed-loop optimization is performed according to an analysis result.

Genetically modified zebrafish, in which mutation combinations frequently identified in human AML are stably expressed in the stem cell population of the fish, are provided. The combination of mutations result in morphologic, cytochemical and molecular changes of its blood cells that are remarkably similar to those in human AML. The zebrafish model provides a foundation for the study of AML initiation and progression and a high throughput in vivo drug screening platform to identify personalized therapies for AML based on specific mutation combinations. The method of drug screening includes contacting embryos or adult fish containing mutations as disclosed herein, with a test agent, at test concentrations and test intervals to determine the therapeutic effect if any, of the test agent.

The present invention addresses the problem of providing a cancer inspection technology in which an olfactory receptor is used. The problem is solved by a cancer test agent containing an insect olfactory receptor protein B' which demonstrates reactivity to multiple types of cancer with the same reactivity direction.

A topology-reprogrammable test environment is provided that can support the needs of CI / CD / CV in the field. The system disclosed provides a highly scalable network architecture to simplify the implementation of network slicing, TaaS and network CI / CD, and solves problems related to the complexity of cloud-native network (CNN) deployments. A Network Cell (NC), comprises or consists of a Containerized Network Function (CNF), a Containerized Digital Twin (CDT), and a Containerized Test Agent (CTA). The CDT has at least two personalities, e.g., an emulator of the CNF in the same NC or a nodal of the CNF. The choice of personality of the CDT is controlled by the CTA of the NC. A number of NCs use a 3D IP address to interconnect and form a new kind of CNN over the infrastructure of VRs.

Aspects of the subject disclosure may include, for example, identifying one or more rules in a policy rulebase, wherein the one or more rules correspond to a subscriber associated with a network, instructing a test agent to derive one or more scripts that, when executed by a client, cause the client to generate test traffic and send the test traffic to the network, after the instructing and after the test traffic is sent to the network, accessing data records that are outputted by the network based on the test traffic, based on the accessing, correlating at least one record of the data records to the test traffic, and analyzing the at least one record to validate conformance with the one or more rules. Other embodiments are disclosed.

The invention provides a multi-agent-based network security distributed test cooperation method and system, and relates to the technical field of network security testing, and the method comprises the steps: dividing a test task into sub-tasks, selecting a test agent from an agent capability library, constructing an agent task grouping graph, and carrying out the task grouping of the sub-tasks; a directed acyclic graph is constructed in a digital twin environment to calculate a side effect propagation chain, a task time sequence chain is generated, and an entropy incidence matrix is constructed based on the task time sequence chain for collaborative planning. The test automation degree can be improved, the test cooperation efficiency can be enhanced, the test resource consumption can be reduced, and the side effect risk in the test process can be reduced.

The invention provides a Matter equipment automatic testing method and system based on cloud edge collaboration, and belongs to the technical field of Internet of Things equipment testing. The method aims at solving the problems that in the prior art, Matter equipment production testing depends on manual work, efficiency is low, burning and testing processes are disjointed, and missing detection risks exist. The method comprises the steps that a cloud service creates and distributes burning batches and test cases; the burning client obtains the task from the edge service, carries out burning on the Matter equipment and reports a result; after receiving the burning result, the edge service automatically triggers a test agent to test the equipment; and the test agent executes the test and reports a result to the edge service. The invention also provides a system for realizing the method. According to the method and the device, automatic closed loop of burning and testing processes is realized, 'burning is testing 'is realized, the risk of missing detection is avoided, and the testing efficiency, the standard uniformity and the system expansibility are improved by utilizing the cloud edge collaborative architecture.

Provided are nucleic acids encoding kinase-modulated bioluminescent indicator (KiMBI) polypeptides. In certain embodiments, a nuclei acid of the present disclosure encodes a KiMBI polypeptide comprising a first bioluminescent enzyme fragment, a phospho-binding domain, a 5 second bioluminescent enzyme fragment capable of forming an active bioluminescent enzyme with the first fragment via enzyme fragment complementation, and a kinase substrate bound by the phospho-binding domain when phosphorylated. KiMBI polypeptide may be fused to one or more fluorescent proteins, e.g., which exhibit resonance energy transfer (RET). Also provided are KiMBI polypeptides encoded by the nucleic acids of the present disclosure. Cells that express 10 a KiMBI polypeptide are also provided, as are non-human animals comprising such cells. Also provided are methods of assessing activity of a kinase of interest in a non-huma animal, and methods of assessing a test agent for the ability to inhibit a kinase of interest in a non-human animal.

A system may utilize a large language model (LLM). The system may train and test agents via an LLM by using the LLM to generate a set of scenarios and a set of training data and then training the agents using the training data and executing the agents accordingly. The system may also perform evaluations of data within a data table via LLMs and evaluations of prompt templates using a set of data from the data table. In one example, the system may receive a set of instructions for data evaluation, evaluate the set of data via the LLM, and obtain a set of output data from the LLM. In another example, the system may receive a prompt template for the LLM, execute the prompt template via the LLM, obtain a set of output data from the LLM, and evaluate the performance of the prompt template via the LLM.

The invention relates to the technical field of game testing, and provides an automatic testing method and system for game software, and the method comprises the steps: obtaining a behavior target sequence of a testing path of a testing agent in a video game; the behavior target sequence comprises a plurality of behavior targets, the behavior targets are used for indicating test tasks needing to be completed by the test agent, and the behavior targets further comprise task physical environment information corresponding to the test tasks; when the test agent is controlled to execute a behavior target in the behavior target sequence, an operation instruction is determined according to the state information of the test agent and the task physical environment information, and the operation instruction is used for guiding the test agent to complete the behavior target; and controlling the test agent to execute the operation instruction. The test efficiency of the game software can be improved.

The invention discloses an intelligent processing method for automobile data, and relates to the technical field of automatic driving. Comprising the following steps: S1, constructing a simulation model: constructing a dynamic parallel simulation system through multi-source data obtained by an automatic driving fleet; s2, determining a test case: setting at least one antagonism test agent in the dynamic parallel simulation system, and performing an interaction test through the antagonism test agent to determine an effective test case; and S3, case verification: obtaining a corresponding comprehensive score according to a test result of the effective test case, screening the effective test case according to the comprehensive score, and performing live-action verification on the screened effective test case. Therefore, simulation-live-action closed-loop iterative optimization is formed, improvement of the performance of the automatic driving system can be continuously promoted, and large-scale safety application is accelerated.

The invention discloses an intelligent advertisement putting service optimization system and a putting platform. The intelligent advertisement putting service optimization system comprises a Plan module, a Test module, an Optimization module, a Regeneration module and a Scale module, wherein the Plan module, the Test module, the Optimization module, the Regeneration module and the Scale module are connected in sequence and are subjected to loop iteration. The system adopts a multi-agent collaboration architecture, a planning agent, a test agent, an optimization agent, a regeneration agent and an expansion agent corresponding to each module are configured, and each agent shares information and makes a collaborative decision through a data flow pipeline to form a closed-loop iterative optimization mechanism. According to the system, intelligent collaborative management of the whole advertisement putting process is achieved, the problems of strategy making, effect verification, budget optimization, creativity regeneration, scale expansion and the like in advertisement putting are efficiently solved through module circulation and intelligent agent cooperation, the putting efficiency and effect are remarkably improved, and the full-link advertisement putting requirements of small and medium-sized enterprises are met.

This invention discloses a method and system for evaluating the intelligence level of an agent based on a Restricted Boltzmann Machine (RBM), relating to the field of artificial intelligence evaluation technology. First, it acquires replay data from pairwise battles between multiple tested agents in an experimental task. Then, for each tested agent, multiple evaluation indicators are calculated based on its battle data from each replay. Using these indicators as input, a trained RBM network predicts the ELO score of the tested agent. The final ELO score is then obtained by averaging the ELO scores from multiple replays. Finally, for each tested agent, its intelligence level is determined based on its final ELO score. This eliminates the need for subjective level classification standards, achieving an objective and universal intelligence level evaluation.

A test method, apparatus, medium, device and computer program product, the method comprising: receiving a description text of a test case, the description text being a text written based on a natural language, the test case comprising at least one test step; based on the description text and a first model, an execution agent and execution parameters corresponding to the test step are determined from multiple test agents, and the test agents are used for calling execution interfaces corresponding to the test agents; and determining an execution result of the test step based on the execution agent, the execution parameter and the execution interface. Therefore, a user can compile the test case in a natural language form, a large amount of human input for understanding and coding is not needed, the description text is analyzed through the model to call the test agent, the manual workload of testers is effectively reduced, and the test efficiency is improved.

Provided herein are systems and methods for conducting covalent target engagement assays. In particular, cysteine-reactive tracers are provided for covalent binding to labeled target proteins, and signals produced by the interaction or proximity of the tracers and labeled target proteins are used to detect / quantify binding of a cysteine-reactive test agent.

The invention relates to the technical field of software development, and discloses an AI agent-based system development system, which comprises a plurality of agent modules: a demand analysis agent module, which can automatically analyze user demands and convert the user demands into a structured model, introduce sentiment analysis to assist priority ranking, and customize a template to generate a demand document; the system designs an agent module, and a design scheme is generated by taking the structured model as input; the code development agent module adopts a layered architecture; the test agent module is of a modular architecture, and standard interfaces of all sub-modules interact; the deployment and operation and maintenance agent module adopts a layered architecture, and the automatic execution layer comprises a deployment and monitoring sub-module; the continuous optimization agent module analyzes user behavior data and provides support for function iteration; the technical problem that in existing system development, the development process is low in efficiency and prone to errors is solved.

The application provides a modified form of DEPTOR, DEPTOR K106 lactylation, as a biomarker in the preparation of a test agent for the preliminary evaluation or prediction of liver cancer. The application also provides a kit for quantitatively or semi-quantitatively detecting a protein, which contains an antibody capable of specifically binding to DEPTOR K106 lactylation. The application can more effectively preliminarily evaluate and predict whether a subject has a tumor by detecting the expression level of DEPTOR K106 lactylation. The application will help improve the diagnostic accuracy of liver cancer and provide an important basis for early treatment of patients. The kit of the application can be used for the diagnosis of early, medium and late liver cancer.

A system for providing an API-driven continuous test platform is disclosed. The system may include one or more processors, a test engine, one or more test agents, and a database. The system may prepare (according to a configuration file) a first test configuration comprising a first selection of the one or more test agents, execute (using the test engine) the first test configuration to produce one or more first test results, and store (using the database) the one or more first test results. Finally, the system may process (using a continuous integration and continuous delivery (CI / CD) pipeline) the first test results by performing at least one of the following CI / CD processes: updating a central code base of an enterprise production environment, rejecting at least one code snippet processed by the test engine during execution of the first test configuration, and marking the first test results as inconclusive.