115 results about "Validation testing" patented technology

The invention discloses an industrial MBSE model generation and evaluation method based on a large language model, and the method comprises the steps: collecting and sorting an aerodynamic model, assembly design rules and related basic design parameters, and carrying out the formatted expression of the rules applied in MBSE, so as to form a special enhanced knowledge base in the industrial field; the method comprises the following steps: converting a demand described by a natural language of a user into a standard MBSE model code based on a large language model through a cue word engineering technology, selecting an optimization algorithm and a simulation algorithm through a function call technology, performing real-time optimization on the model according to a design target, and applying the generated MBSE model to propulsion device design and verification test. And determining availability and model consistency. Reconstruction and acceleration of digital design are realized through automatic configuration based on requirements and components. The method has remarkable advantages in the aspects of reliability, effectiveness and adaptability.

A computer-implemented method includes customizing artifacts of a large language model (LLM). A generative artificial intelligence (AI) (genAI) function of a software application that interfaces with the LLM and is instrumented for customer-side management is activated, as a genAI application. A verification test is activated to determine whether interactions between the LLM and genAI application generate expected results. Based on a result of the verification test, either adjusting the artifacts of the LLM or updating the genAI function of the software application. A new verification test is defined to determine whether interactions between the LLM and genAI application generate expected results. The new verification test is activated.

The invention discloses an AI auxiliary test case generation and verification method and a terminal, and the method comprises the steps: obtaining and analyzing game commodity data, filling a script template with the analyzed game commodity data, verifying and adjusting the script template through a trained AI model, recognizing a test scene according to the adjusted script template, and carrying out the verification of the test scene. A corresponding test case is generated; and executing the test case, verifying the execution result of the test case through database query and log analysis, and generating a test report, so that the method can adapt to tests of different scenes and requirements, and the reliability and execution efficiency of the test are improved.

The invention discloses an AI-based network log analysis processing method, which comprises the following steps of: firstly, collecting network equipment log information through a collection script supporting multiple protocol modes, and writing the collection script and deploying and starting after determining equipment and protocol types; thirdly, carrying out preliminary classification, data supplementation and formatting processing on the original logs; and then, sending the processed log data to an AI large model for semantic analysis, including deep understanding, basic classification, secondary analysis and providing of processing suggestions. An analysis result is stored in a data think tank for historical comparison, and an inconsistent result is manually confirmed and corrected; when the network equipment triggers a fault alarm, calling the data think tank to supplement alarm content; and meanwhile, equipment related data handbooks provided by manufacturers are uploaded regularly, the analysis accuracy of the AI model is corrected, verification and testing are carried out, it is ensured that the scheme can comprehensively and efficiently collect and process logs, accurately analyze faults and continuously optimize the model, and effective support is provided for network fault processing.

The embodiment of the invention discloses a security deployment method and device for enterprise-level applications and a medium, and relates to the technical field of security deployment, and the method comprises the steps: obtaining application topological graph data generated by a preset visual development platform, so as to split the application topological graph data into an atomized micro-service unit set through a quantization deconstruction engine, generating a micro-service dependency tree; scanning the target deployment environment, extracting an environment feature fingerprint of the target deployment environment, and generating an environment adaptation layer code according to the environment feature fingerprint; injecting an environment adaptation layer code into each atomized micro-service unit to generate an environment perception type micro-service package, and constructing a cross-cloud communication link matrix based on the micro-service dependency tree; and based on the environment-aware micro-service packet and the cross-cloud communication link matrix, executing a deployment verification test in the sandbox environment, generating a deployment verification report, and according to the deployment verification report, distributing the environment-aware micro-service packet to a target deployment environment node.

A method for providing an automated AI teaching and learning environment includes selecting an AI agent from an AI agent pool as a candidate AI agent according to performance of the AI agent after previous training and further according to capability requirements for capability-based training, and providing the capability-based training for the candidate AI agent. Providing the capability-based training includes acquiring first training data, generating a trained artificial intelligence (AI) agent by training the candidate AI agent to have at least one capability associated with using the first training data, generating synthetic validation data different from the synthetic training data, validating the trained AI agent by performing validation testing on the trained AI agent using first validation data that includes the synthetic validation data, and providing the trained AI agent as a validated AI agent after the validating the AI agent.

A component die validation built-in self-test (VBIST) engine is presented. In an aspect, a component die includes component circuitry for performing a component function, interface circuitry for communicating with another die, and a VBIST circuit. The VBIST circuit includes a traffic generator that generates test data streams, a tracker that receives and validates test data streams, and a configurable switching matrix for coupling the traffic generator to at least one of the component circuitry, the interface circuitry, or the tracker, and for coupling at least one of the component circuitry, the interface circuitry, or the traffic generator to the tracker. The VBIST circuit can send traffic to and from the component circuitry directly, or indirectly via the interface circuitry in loopback mode, and can be used for memory initialization and test.

The invention discloses an automatic testing method and device based on a front-end UI and a terminal, and belongs to the technical field of front-end development testing. Comprising the following steps: detecting a first test instruction of each set of front-end UI program, controlling to carry out demand analysis, determining a function module which needs to be subjected to UI automatic test according to a demand analysis result, and determining a test target; performing automatic technology type selection, and selecting a specified test tool and a continuous integration tool; generating a compiled test case according to the determined function module needing to be tested and the test target; running the test case, and generating a test script according to the test case and an automation technology type selection result; when the test script is verified to be correct, integrating the test script into a continuous integration tool, and automatically running a test through the test script when the code of the subsequent version of the front-end UI program is submitted; and generating a test report, positioning problems and giving repair suggestions. According to the invention, the testing efficiency is improved, and the labor cost of testing is reduced.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence algorithm model accuracy verification test method comprising the following steps: S1, data set preparation: collecting diversified data sets to cover more scenes and conditions; s2, whether cross validation is carried out or not is judged, if yes, a cross validation method is adopted, a data set is divided into a plurality of mutually exclusive subsets, a part of the subsets are cyclically used as a test set, the rest of the subsets are used as a training set for multiple times of verification and testing so as to evaluate the performance and stability of the model, and if not, the next step is carried out; and S3, selecting a proper evaluation index, and if cross validation is not selected, selecting a proper evaluation index. According to the method, the accuracy of the artificial intelligence algorithm model can be comprehensively and systematically evaluated, developers are helped to accurately understand the performance of the model in different data sets and scenes, and the model with better performance can be screened out by comparing the performance of different models in the same test set.

The invention provides a stress evaluation method and system for a TSV packaging structure, and relates to the technical field of semiconductor packaging, and the method comprises the steps: constructing a global coarse mesh model, cutting to generate a local TSV sub-model, and generating a failure feature mapping table; based on the failure feature mapping table and a preset process temperature curve, coupling solution is carried out on the local TSV sub-model, and a multi-physical field data set is output; training a failure probability prediction model by combining the failure feature mapping table based on the multi-physics field data set, and generating a digital failure atlas template; performing dynamic risk assessment based on the digital failure atlas template, the real-time process parameters and the dynamically calculated electrothermal coupling parameters, and generating a failure risk report; analyzing the failure risk report, and generating a mode-based optimization guide; and performing spatial deviation comparison based on the verification test data executed by the mode-based optimization guide, and when the spatial deviation exceeds a preset threshold value, outputting a failure map version updating mark and triggering the failure feature mapping table to iteratively update.

Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI / ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI / ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone“locked” AI / ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability. The parallel environment AI / ML testing using randomized learning, validation, testing sets drawn from calibration quality adjudicated standard reference signal data signature libraries provides for validation, verification and test to failure procedures.

The systems and methods disclosed herein enable evaluation of machine learning model outputs within a virtual environment. The disclosed model validation platform enables testing of code generated for detection of malicious or anomalous outputs. For example, the model validation platform can construct a virtual machine isolated from the system and test model-generated code for validation of LLM-generated outputs. In some implementations, the model validation platform determines parameters of the virtual machine and / or associated validation test based on an evaluation of the machine learning model's output and / or the associated underlying prompt. For example, the parameters of the validation test depend on an evaluation of the user or the provided input (e.g., depending on the presence of sensitive data within the prompt). By doing so, the system enables dynamic evaluation of machine learning model outputs to improve the security and robustness of associated generated code.

The invention relates to a verification and test integrated platform for a probe processor, and belongs to the technical field of processor verification and test. A probe processor adopts a self-defined probe instruction and a special probe operator unit, and a traditional verification and test mode is difficult to consider the requirements of module-level verification and system-level test. The UVM verification system, the test system and the management module are integrated, and unified management of verification and test is achieved. The UVM verification system is based on a UVM architecture, a virtual interface is associated with a to-be-tested unit (such as a probe operator, GPIO, UART and the like) of a probe processor through a forced binding mechanism, and module-level verification is achieved. The test system is responsible for compiling test codes into machine codes and loading the machine codes to the probe processor by starting the read-only memory, and system-level test is executed by the probe processor. And the management module adopts an automatic script to realize intelligent switching between verification and test modes, so that the probe processor is suitable for different test scenes, the test efficiency is improved, and the environmental redundancy is reduced. According to the method, the reusability of the test case is improved, the test consistency is ensured, the resource utilization rate is optimized, the automation degree and efficiency of verification are improved, and the method is suitable for efficient verification and test of probe processors and similar architecture chips.

Systems and methods for bias testing and remediation are disclosed. Decentralized Web Application Testing Systems use a Holochain framework to distribute testing workloads across Full Nodes and Lightning Nodes. A Holochain Node Management Application for configuring nodes, a UI Application creates test cases, and a Version Management System tracks changes. Test results are stored and analyzed in a Test Result Store, with a Bias Intelligence module detecting biases and generating additional test cases. A Consensus Algorithm validates test cases through decentralized nomination. An AI-Driven Defect Remediation System automates defect detection, root cause analysis, and remediation using AI modules. Machine learning algorithms identify patterns and anomalies, while NLP techniques generate code fixes. Predictive maintenance monitors application performance to preemptively address issues. A feedback loop mechanism continuously improves AI models through reinforcement learning. Together, these systems provide a holistic approach to web application testing and maintenance.

The invention discloses a chip parameter verification method and device, equipment and a storage medium. The chip parameter verification method comprises the steps of sampling a to-be-tested parameter space of a chip to obtain a parameter point file; performing a regression test on the parameter point file to obtain verification test results, and associating each verification test result with the corresponding parameter point to obtain a data pair set; performing parameter point iteration prediction on the set through a model according to the data; and when it is determined that the predicted parameter points meet the optimization target, taking the predicted parameter points as to-be-applied target parameter points of the chip. The parameter point file is obtained by sampling from the to-be-tested parameter space, and the sampling result is used for the regression verification test, so that the number of tasks needing to be executed is reduced, the overall verification investment is reduced, fitting and prediction are carried out through the model according to the verification test result, the coupling effect and hidden defects among parameters are conveniently found, and the verification efficiency is improved. And the accuracy of the determined target parameter is improved.

A diversified validation test suite application (DVTSA) for a generative AI powered tool or large language model (LLM) tool includes at least first, second, and third control logics. The first control logic receives a seed test input or user input, analyzes the seed test input or user input, and extracts key elements for variations. The second control logic performs a coverage measurement of outputs of the first control logic. The third control logic causes a human validator to evaluate outputs of the second control logic relative to predefined coverage metrics and selectively and continuously iterate to cause outputs of the second control logic to increase LLM tool input robustness and output robustness from a first level to a second level greater than the first level, in both production and pre-production LLM tool processes.

This disclosure describes systems, methods, and devices related to enhanced validation of video codecs. A device may receive validation results for a video codec using a first computer programming language, the validation results indicative of invocations of functions and conditions of the code; identify, from the validation results, a first validation test that invoked a first function or condition of the code; identify, from the validation results, a second validation test that invoked the first function or condition of the code; determine, from the validation results, that the first validation test and the second validation test overlap in invoking the first function or condition of the code using the first computer programming language; and generate, based on the overlap, a first subset of the validation tests with which to validate code of the video codec using a second computer programming language.

The present application provides a method and related equipment for evaluating the application level of automated testing. The method includes: obtaining multiple test processes of automated testing applications; performing script mapping on each test process, and determining the verification automation rate of each test process based on the script mapping results; calculating the maximum automation rate of all test processes based on the verification automation rate of each test process; constructing a verification test statistics table based on the verification automation rate, maximum automation rate and test data of the verification test of each test process; performing actual testing on each test process, and calculating the actual automation rate of each test process in the actual test within a predetermined time period, and constructing an actual test statistics table based on the actual automation rate of each test process and the test data of the actual test; determining the evaluation results based on the verification test statistics table and the actual test statistics table. It can accurately and truly reflect the application level of automated testing.

The invention relates to the technical field of embedded systems, in particular to a factory test detection method and system for firmware burning. According to the method, firmware burning is carried out on a terminal to be tested, whether firmware burning is complete or not is detected in real time during burning, and when it is detected that firmware burning is complete, a factory test trigger signal is sent to a control end; after receiving the factory test trigger signal, the control end tests the terminal which completes firmware burning by a layered test method; and after the test is completed, generating a corresponding test report, combining the MAC address of the terminal, the test timestamp generated during the test and the CRC check value to generate a data identifier, binding the data identifier with the test report, and storing the data identifier and the test report. By means of the mode, when firmware burning is completed, the factory test trigger signal can be automatically sent to the control end, follow-up factory test is carried out, and therefore full-automatic closed-loop control over burning, verification and testing is achieved.

The invention discloses an automatic generation method of an API process scene based on a gateway and a large model, and relates to the technical field of API testing. The method comprises the steps of marking and executing a test case, capturing and verifying gateway data, exporting a JMX test script, exporting request response details, intelligently analyzing a large model, storing returned content as a standard JMX file, and executing verification. Through cooperative work of the gateway and the large model, automatic capture, data association analysis and test script generation of an API test scene are realized, the generation time of the API test script is shortened from an hour level to a minute level, and the efficiency is improved by more than 10 times; non-professionals can generate complex test scripts through simple operation, and dependence on professional testers is reduced; a large model automatically analyzes data association, and omission and errors caused by manual analysis are avoided; full-process automation from interface calling and capturing to test script generation is achieved, and tool switching is reduced.

A computer implemented method of generating a software service for providing required software functionality can include accessing a software component having functionality including and exceeding the required functionality; defining verification test for verifying that the software component includes the required functionality; applying a genetic algorithm to the software component to iteratively adapt the software component over a plurality of generations, wherein each generation of the software component is adapted by removal of one or more portions of the software component of a preceding generation, wherein the adaptation for a generation is selected from a set of candidate adaptations based on a determination of a fitness of the component so adapted, the fitness being determined by the verification test, wherein the iteration of the genetic algorithm ceases in response to a stopping condition such that, on cessation, the latest generation of the software component constitutes the software service.

A server system sends, via a linearly ordered communication orbit, to computational machines at a first subset of nodes in a computer network, a set of local environment verification tests and a set of mappings that map results of the local environment verification tests into a set of risk scores. Requests sent by the server system cause the computational machines at the plurality of nodes to: locally evaluate the set of local environment verification tests to produce test results, and locally map the test results using the set of mappings into a set of risk scores. Queries sent by the server cause the computational machines at the plurality of nodes to return to the server system at least a portion of the test results and risk scores. The server, identifies, based on the received test results and risk scores, computational machines and / or control categories having risk scores satisfying predefined criteria.

According to the test method for the high-low priority scheduling mechanism of the A664 switch, a minimum test environment is established, an A664 tester with four physical ports is used, and verification cases of high-priority data frame priority forwarding, priority data integrity and the number of high-low priority buffer areas in a congestion scene are designed. The integrity and accuracy of high-low priority transmission function verification are ensured, and the problem of verification test of the high-low priority working mechanism of the A664 network in the prior art is effectively solved. The method is high in universality and can be suitable for various A664 network devices and application scenes.

The invention discloses a network security vulnerability discovery method, a verification test system, a network equipment security detection method based on an automatic Fuzz test and a test system thereof, and relates to the field of security test and vulnerability mining. The method comprises the following steps: establishing an equipment protocol database, activating a powerful data variation engine, executing an abnormal input test of an equipment protocol, and generating a first test result; calling an intelligent exception backtracking analysis module, performing deep backtracking analysis on the equipment protocol by using the module, and establishing a second test result; performing static and dynamic fusion analysis on the equipment protocols in the database, generating joint function similarity, and establishing a third test result; executing an equipment protocol version change test, and establishing a fourth test result; and performing vulnerability detection and management of the device protocol according to the four test results. According to the method, the technical problem that the equipment security test efficiency and the vulnerability mining accuracy are relatively low due to high difficulty of a network equipment protocol in a high-complexity test is solved, and the test accuracy and the vulnerability positioning efficiency are greatly improved through combination of a data variation engine and intelligent backtracking analysis.

The invention relates to a product development process management method and system, and the method comprises the steps: obtaining demand information, carrying out the project creation stage review of the demand information, and determining whether to carry out project establishment or not according to a project creation review result, so as to generate a corresponding project establishment project; performing product design development review on the project approval project, and determining whether to perform a proofing application or not according to a design development review result so as to generate a corresponding sample; performing engineering verification test stage review on the sample, and determining whether to apply for mold opening according to a verification test review result so as to generate corresponding part data; performing design verification test stage review on the part data, and determining whether to perform trial production verification according to a design verification review result so as to generate corresponding trial production verification data; and carrying out production verification stage summarization on the trial production verification data, and determining whether to carry out mass production according to a summarization result of the production verification stage summarization so as to generate a corresponding product. The method has the effect of improving the product development efficiency.

The application discloses a PLC code generation and verification method based on a large language model, which comprises the following steps: generating multi-stage multi-modal input data, wherein the multi-stage multi-modal input data is used for representing user requirements and formulating a system design scheme according to a requirement analysis result; the multi-stage comprises a requirement analysis stage and a design stage of a complete engineering cycle; the multi-modal input comprises a P&I D diagram and natural language; then, PLC code is generated according to the multi-stage multi-modal input data; then, the generated PLC code is verified and tested, wherein the verification and testing comprises code verification and compiler verification; finally, the PLC code is deployed after the verification and testing of the PLC code are completed. The embodiment of the application has high accuracy and high automation degree, can provide a network security researcher with an efficient and accurate PLC code deployment method, and can be widely applied to the technical field of computers.

The embodiment of this application belongs to the field of R & D management and is applied to the field of automated testing. It involves a method for optimizing test code based on automated testing, including obtaining a source test code set and function code to be tested and inputting them into a pre-constructed automated testing tool; generating corresponding problem codes for the function code based on a preset problem code generation model; obtaining a primary selection test code set and an optimized selection test code set; determining the effectiveness of the source test code set; judging whether the effectiveness can be optimized; if it can be optimized, perform the optimization; if it cannot be optimized, terminate the automated testing program. It can not only reduce the workload of testers through automated testing, but also verify the effectiveness of the test code and automatically optimize it, so that the optimized test code not only has a reasonable test scope, but also can detect BUGs in time when defects occur in the program.

The present application provides a vehicle usage scenario simulation test method, device, equipment, medium and program product, which obtains and parses the simulation configuration information input by the developer; according to the parsing result of the simulation configuration information, loads the scenario configuration data corresponding to the simulation configuration information, and determines the scenario simulation data according to the scenario configuration data and the preset scenario simulation protocol; according to the transmission configuration information in the parsing result, sends the scenario simulation data to the test engine to change the vehicle usage scenario in the test engine, and tests the various preset services corresponding to the vehicle usage scenario. It solves the technical problem in the prior art that the vehicle usage scenario is uncontrollable during vehicle research and development testing, resulting in long research and development time and high testing costs. It achieves the technical effect of providing vehicle development and testing personnel with a simple and fast testing method when verifying scenario services, reducing the waiting time for test scenarios, and reducing the cost of development and testing.

An apparatus and method is provided for simulating a physical computer system using virtualization. The disclosed system virtualizes devices of a physical computer system by modeling hardware and software components that are physically present within the physical computer system. The system simulates changing at least one of a piece of hardware or software in the virtualized computer system and assesses an effect of the change in the virtualized computer system to determine a potential effect of the change if the change were implemented on the physical computer system.

Techniques are described for a data recovery validation test. In examples, a processor receives a command to be included in the validation test that is configured to validate performance of an activity by a server prior to a failure to perform the activity by the server. The processor stores the validation test including the command on a memory device, and prior to the failure of the activity by the server, executes the validation test including the command responsive to an input. The processor receives results of the validation test corresponding to the command and indicating whether the server performed the activity in accordance with a standard for the activity during the validation test. The processor provides the results of the validation test in a user interface.