229 results about "Evaluation testing" patented technology

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

Methods and systems for artificial intelligence (Al) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

The embodiment of the invention provides a model evaluation method and device, electronic equipment and a storage medium. The electronic equipment can obtain an evaluation test data set; based on the evaluation test data set, the to-be-evaluated model is evaluated through a preset evaluation mode, an evaluation result is obtained, and the preset evaluation mode comprises at least one of a consistency evaluation mode, an understanding ability evaluation mode and a knowledge mastery degree evaluation mode; the consistency evaluation mode is used for evaluating the consistency between prediction answers output by the to-be-evaluated model for the same test data based on different cue words, and the understanding ability evaluation mode is used for evaluating the accuracy of the prediction answers output by the to-be-evaluated model for multiple pieces of rewriting test data; the knowledge mastery degree evaluation mode is used for evaluating the accuracy of prediction answers output by the to-be-evaluated model for the in-depth test data corresponding to the test data. Therefore, the consistency, the understanding ability and the knowledge mastering degree of the large language model can be evaluated.

The invention discloses an artificial intelligence-oriented data set quality evaluation test method, which relates to the technical field of artificial intelligence, and comprises the following steps: S1, preparing a test data set; s2, initializing test conditions; s3, pre-detection is carried out; s4, carrying out post detection; and S5, calculating a data set quality evaluation score. According to the artificial intelligence-oriented data set quality evaluation test method, targeted innovative design, adjustment and expansion are carried out on artificial intelligence data set quality evaluation indexes, methods and technical processes according to unique requirements of artificial intelligence data set quality evaluation methods and technologies; it is ensured that the method not only can fit the specific stage and scene of AI model training and performance evaluation, but also can promote the iterative progress of artificial intelligence related technologies and the improvement of practical application efficiency, and high scene suitability and technical perspectiveness are embodied.

The present invention provides a fuzz testing system and method based on a virtual computer model. Traditional fuzz testing approaches primarily modify external inputs to detect security vulnerabilities, making it difficult to monitor internal state changes in real-time. This invention constructs a test environment using a virtual computer model and incorporates automatic assertion generation and detection functions, enabling precise analysis of both internal and external signals of the system. After executing fuzz testing, assertion data is analyzed to assess test coverage and identify untested code regions, allowing for an optimized testing strategy. This improves the detection rate of security vulnerabilities, enhances test automation and reliability, and can be applied in various fields such as networks, automotive ECUs, IoT, finance, aerospace, and semiconductors.

The invention relates to the technical field of chip performance evaluation and test, in particular to a chip performance evaluation and fault diagnosis optimization test method, which comprises the following steps of: generating a test vector library with an optimal coverage rate on the basis of functional module distribution and circuit structure characteristics of chip design; in a chip operation environment, sequentially inputting test conditions by using the test vector library, and simultaneously collecting dynamic performance parameters in real time; based on the dynamic performance data set, establishing a multi-dimensional performance characteristic model comprising performance fluctuation characteristics, a critical state threshold value and hidden fault characteristics; based on a multi-dimensional performance characteristic model, in combination with circuit structure characteristics, a specific fault function module is positioned by reversely tracing the relevance between chip function modules. According to the method, innovative technical support is provided for chip design optimization and operation stability improvement, the fault diagnosis time is remarkably shortened, and the diagnosis precision and reliability are improved.

The invention discloses an energy storage power station grid-connected test method and system and a readable storage medium, and the method comprises the steps: constructing an energy storage power station power grid joint simulation model, and pre-configuring a multi-dimensional test scene in the energy storage power station power grid joint simulation model; carrying out a multi-stage test by using a pre-configured multi-dimensional test scene; based on real-time data, multi-stage test parameter thresholds are dynamically adjusted, and a test result is evaluated through an intelligent algorithm. The performance and the safety of the energy storage power station after grid connection can be comprehensively evaluated by constructing the energy storage power station power grid joint simulation model, pre-configuring the multi-dimensional test scene, carrying out the multi-stage test and evaluating the test result, and it is ensured that the energy storage power station can be safely and reliably connected to the power grid.

Systems and methods for error mitigation in a software pipeline. In some aspects, the system receives a request for evaluating a pipeline at a recurrent interval of time. The request comprises a first version of software, a second version of software, evaluation tests to execute on the second version of the software at intermittent intervals, and the recurrent interval of time. The system, in response to determining the recurrent interval of time elapsed, executes the evaluation tests on the second version of the software. The system determines that the output of the evaluation tests does not match one or more target values. The system terminates deployment of the second version of the software. The system generates instructions to replace the second version of the software with the first version of the software and transmits a notification to a remote device.

The present invention relates to equipment that can realistically reproduce various types of airflow occurring in environments such as an urban area with tall buildings or regions with mountains and valleys, thereby enabling verification of the flight safety of a multicopter-type drone. The present invention comprises: a housing that provides a tunnel-shaped test space in which a plurality of blowers, together with controllers, are installed along the longitudinal direction of both sidewalls of the interior, the ceiling, and the floor, to generate airflows such as crosswinds, updrafts, downdrafts, and vortices; and an integrated control unit that selectively or simultaneously controls some or all of the plurality of controllers installed in the housing so as to form airflows in pattern corresponding to user input or settings.

A computer-implemented method including quantifying pre-defined technical porting goal of a porting project into a technical value model, receiving selected decisions regarding the target system and porting tasks and current version of the porting system, executing traceable porting steps based on the selected decisions, porting tasks and current version of the porting system and creating software artefacts of the target system, evaluating software artefacts created or converted by each of the executed porting steps and / or evaluating test results by applying and / or simulating each ported software artifact, outputting references to the versions of created and / or converted and evaluated software artifacts into a porting log, and training the creation and evaluation component of the porting system with data of the porting log based on the decisions and the outcome of the porting project using porting mechanism to reach the pre-defined goal for the porting system.

The invention provides a personnel cognitive competence assessment and recovery method based on big data, and the assessment method comprises the steps: collecting basic data of a to-be-assessed target personnel, and sequentially carrying out a plurality of assessment tests under each cognitive competence assessment classification on the target personnel based on a plurality of preset cognitive competence assessment classifications; obtaining test data, a single capability evaluation value and completion time of each evaluation test, inputting the basic data and each test data into a prediction model, and calculating a big data analysis index of each evaluation test based on a big data analysis capability evaluation value output by the prediction model; and calculating a final cognitive competence assessment result of the target person according to the single competence assessment value of each assessment test, the completion time and the big data index analysis index. According to the method, a computer technology and a big data analysis technology are utilized to carry out multi-dimensional comprehensive assessment on the cognitive competence of the operator, and the accuracy, intelligence and comprehensiveness of assessment are improved.

The invention relates to the technical field of software testing, and discloses an agent-based multi-dimensional test sample automatic generation method and system.The agent-based multi-dimensional test sample automatic generation method comprises the steps that code changes are captured, interface definitions are extracted, and multi-source data are fused for consistency verification; constructing a multi-dimensional dependency graph and analyzing the change influence to obtain an associated interface list; generating a multi-dimensional test scene; performing parameter traceability and consistency derivation, and initializing test data and environment to obtain a complete test data set; generating a use case code, and performing hierarchical optimization and executable verification; test arrangement optimization and automatic execution are carried out, and faults are monitored and diagnosed in real time; carrying out multi-dimensional analysis and defect identification, dynamically evaluating the value of a test case and optimizing a test strategy to obtain a comprehensive test report; according to the invention, full-process automation of test case generation is realized, the test efficiency and quality are improved, and the test maintenance cost is reduced.

A computer-implemented method, according to one approach, includes: receiving a new set of test data and evaluating the test data using a pre-trained deep neural network. In response to evaluating the test data, activations are extracted from layers of the deep neural network. Compressed histograms are further used to determine p-values for the extracted activations. The p-values are evaluated and portions of the test data that are determined as being anomalous, based at least in part on the evaluation of the p-values, are retained.

The utility model discloses a dynamic safety performance testing device for an automobile safety belt. The dynamic safety performance testing device for the automobile safety belt comprises a trolley body; the seat assembly comprises a seat body and a mounting assembly; and the collision waveform simulation assembly is arranged on the trolley body and is used for providing collision impact environments for different vehicle types and sending out collision signals, and the mounting assembly performs active pre-tightening action based on the collision signals. According to the dynamic safety performance testing device for the automobile safety belt, key performance indexes of the developed active pre-tightening safety belt can be tested repeatedly in the development stage, the simulation precision and the real automobile test consistency of the evaluation testing device and method are high, the operation is convenient, the practicability is high, the cost is low, and the testing device and method are suitable for popularization and application. Powerful support can be provided for the development of the active pre-tightening safety belt.

The invention belongs to the field of semiconductor life evaluation, relates to a data analysis technology, and is used for solving the problems that in the prior art, feature analysis cannot be performed according to life test results of different types of semiconductor devices, and links such as sample extraction and key test parameter optimization cannot be performed by combining feature analysis results. The intelligent service life evaluation system adapted to the power semiconductor device design comprises a sampling analysis module, a balance evaluation module, a tendency evaluation module and an evaluation analysis module, the sampling analysis module is in communication connection with the balance evaluation module and the tendency evaluation module, and the evaluation analysis module is in communication connection with the balance evaluation module and the tendency evaluation module; according to the method, evaluation samples of the power semiconductor device can be extracted, and evaluation modes of the power semiconductor device can be marked according to evaluation test results of different device types in an evaluation period, so that an acceleration model can be reasonably distributed in a next evaluation period.

The invention relates to the technical field of computer storage, and discloses a test strategy for improving the read-write performance stability of an SSD (Solid State Disk), and the strategy comprises the following steps: obtaining the current available capacity of an SLC cache of the SSD; based on the obtained current available capacity, setting a total data volume used by a performance stability test, so that the total data volume is smaller than the current available capacity; and executing the performance stability test on the solid state disk by adopting the set total data volume so as to obtain performance data capable of reflecting the hardware stability of the solid state disk. The real-time available capacity of the SLC cache is obtained through the standard interface before the test, and the test data volume is set by taking the real-time available capacity as the rigid basis. By means of the method, precise stripping and evaluation of the performance stability of the solid state disk hardware are achieved, and the test result can truly reflect performance fluctuation caused by hardware components such as a master control or flash memory particles.

A method for carrying out a setting mode of a container inspection apparatus in which a transport device transports containers to be inspected along a predefined transport path and at least one sensor device detects spatially resolved sensor data with respect to the containers to be inspected, in which a plurality of spatially resolved sensor data is provided in a non-volatile memory device, which is retrieved by a setting device in the setting mode; —the setting device is provided with a test container-inspection model of machine learning, which includes a set of parameters set to values and evaluated with respect to working mode and the test container-inspection model is set as the real-time container-inspection model in the real-time analysis device; —for the evaluation of the test container-inspection model, the setting device determines at least one statistical evaluation variable retrieved plurality of spatially resolved sensor data.

The invention discloses a production command agent test and evaluation method, device and equipment and a medium, and belongs to the technical field of agents, and the method comprises the steps: combining any evaluation dimension in an evaluation dimension set with any test scene type in a test scene type set, and obtaining an evaluation factor combination set; performing parameter matching in an agent configuration parameter pool according to the evaluation dimension and the test scene type corresponding to the evaluation factor combination, and generating a plurality of agent configuration parameter groups corresponding to the evaluation factor combination; the production command agents corresponding to the agent configuration parameter sets are evaluated and tested, and evaluation index values corresponding to the agent configuration parameter sets are obtained; and according to the evaluation factor combination set, the agent configuration parameter group and the evaluation index value, generating total evaluation test information, and according to the total test information, testing and evaluating the to-be-evaluated production command agent. Therefore, by implementing the method, the evaluation reliability of the production command agent can be improved.

The invention discloses a multi-point explosion hazard effect evaluation test method and a multi-point explosion hazard effect evaluation test system. The method comprises the following steps: uniformly distributing a plurality of explosion sources with prefabricated fragments on the same circumference and synchronously detonating; determining abdominal line distribution based on a shock wave superposition principle, and dividing a reference ring, an inner ring and an outer ring by taking an explosion central point O as a circle center according to a predicted overpressure peak value; and cake-shaped and penholder-type free field pressure sensors, impulse targets, simulation dummies, pine targets and speed measurement targets are systematically arranged on each ring layer along the abdominal line direction. By synchronously collecting multi-parameter data such as shock wave pressure-time history, fragment speed, target deformation, human body reflection overpressure and the like, quantitative analysis of shock wave superposition and attenuation characteristics, fragment power field distribution, human body damage risk and explosion impulse aftereffect is realized. The problem that the multipoint explosion hazard effect lacks a standardized test means is solved, and data support is provided for protection equipment research and development and safety standard formulation.

The invention discloses a method and a system for evaluating a test coverage rate based on a Java call link, and relates to the field of software testing. The method comprises the following steps: identifying a difference function according to a first comparison result, and constructing a call chain tree structure by taking the difference function as a root node based on a code call relation graph; analyzing the difference function to obtain a direct code change line of the difference function, based on a call chain tree structure, identifying a related code line on a call chain where each call point is located as a call point code line, and summarizing the direct code change line and the call point code line; and analyzing the code coverage rate data to obtain an actually covered code line, comparing the to-be-tested code line with the actually covered code line, determining coverage rate index data based on a call chain according to a second comparison result, and determining a test coverage rate evaluation result according to the coverage rate index data. The test coverage rate can be evaluated more comprehensively and accurately.

The embodiment of the invention relates to a processing system for sensing performance testing and sensor deployment scheme evaluation. The system comprises a high-precision map library, a basic element library, a scene modeling module, a scene template library, a scene simulation module, a scene simulator, a data labeling module, an intelligent model library, a test database, a scene evaluation module, a perception test module and an evaluation / test report storage module. Through the method, a large amount of test data can be constructed, the scene complexity of the test data can be improved, the evaluation efficiency of a sensor deployment scheme can be improved, and the test sufficiency of a tested person can be improved.

The invention discloses an unsupervised road obstacle detection method based on reverse distillation, and the method comprises the steps: 1), collecting road data through a road monitoring camera, carrying out the division of a training set and a test set, enabling an image without an obstacle on a road to be a normal sample, enabling an image with an obstacle to be an abnormal sample, carrying out the pixel-level marking, and carrying out the detection of an abnormal image; the training set only contains normal samples, and the test set contains normal samples and abnormal samples; 2) sending the training set into an improved reverse distillation network for training; and 3) inputting the test set into the trained reverse distillation network, calculating the cosine similarity between the multi-scale features of the student network and the teacher network as an abnormal score to evaluate test pictures in the test set, taking the pictures with the abnormal scores greater than a threshold value as abnormal samples, and taking the pictures with the abnormal scores smaller than the threshold value as normal samples. According to the method, the problems of insufficient abnormal samples, characteristic domain deviation and excessive generalization of the student network in road obstacle detection are solved.

The invention provides an intelligent auxiliary system and method for gastrointestinal nursing teaching, and relates to the field of intelligent assistance, and the method comprises the steps: firstly, extracting a part where a user answers mistakenly from an evaluation test report, and carrying out the content segmentation and semantic coding of the part through employing a semantic analysis and processing algorithm based on artificial intelligence; and meanwhile, semantic coding is performed on the learning demand description, so that the type of the learning material is intelligently recommended according to rapid scanning semantic query representation between the semantic coding features of the learning demand of the user and the semantic coding features of the wrong content segments answered by the user. Therefore, the learning demand of the user and the specific content of the answer error can be deeply understood, and the semantic interaction information between the learning demand and the answer error can be captured, so that the knowledge blind spot and the learning demand of the user can be accurately identified, the learning material suitable for the user can be accurately recommended in real time, and the pertinence and effectiveness of recommendation are improved.

The invention discloses an electronic contract signature verification system based on a built-in intelligent auditing algorithm, relates to the technical field of artificial intelligence, and solves the problems that an existing electronic contract signature verification system is lack of an effective means to verify the identity of an owner of an electronic contract, an electronic seal cannot be prevented from being used in an unauthorized manner, and the verification efficiency is high. And the content of the electronic contract file cannot be completely ensured not to be tampered after being signed. The electronic contract signature verification system comprises an intelligent identification module, an electronic contract management module, a data storage supervision module, an electronic seal management module, an intelligent contract signature verification module and a verification test module. Identifying a seal signature image of the electronic seal and extracting key contract data of the electronic contract through a multi-modal segmentation model; performing hash operation on the electronic contract through the data storage supervision module to obtain an original hash value; the accuracy of the contract signature verification result is detected and evaluated through the evaluation test model, and the anomaly positioning algorithm is optimized according to the accuracy detection and evaluation result, so that electronic contract signature verification is completed; the authorization security of the electronic seal is improved, and the content of the electronic contract file is prevented from being tampered after being signed.

Some embodiments comprise a vehicle for conducting non-destructive evaluation (NDE) tests of storage tanks, especially storage tank bottoms. Vehicles may be autonomous, semi-autonomous, or manually driven ROVs. At least one NDE instrument probe is onboard as well as at least one high and / or low-pressure fluid nozzle which is either repositionable or fixed on an area in front of the NDE probe. Thus, as the vehicle advances, the nozzle cleans a path in front of the probe to improve the probe's coupling to the tank bottom. Repositionable nozzles may be motorized and under computer control. The nozzle receives fluid flow from a pump configured in recirculation mode. Thus, the pump draws in tank fluid and outputs a high or low-pressure high-volume stream to the nozzle, which directs the flow.

The present application provides an artificial intelligence-based simulation test method, apparatus, electronic device, and storage medium. The artificial intelligence-based simulation test method includes: collecting historical test requirements and historical test data, and encoding the historical test requirements to obtain encoded data; classifying the historical test requirements to obtain the category of each historical test requirement, and dividing the encoded data and the historical test data into multiple training data sets according to the category; training multiple test data generation models; querying the encoded data to be evaluated and the communication protocol; classifying the test requirements to be evaluated to obtain the corresponding category of the test requirements to be evaluated, and selecting a target model from the test data generation models; and inputting the encoded data to be evaluated into the target model multiple times to obtain multiple batches of test data for multiple simulation tests. This method can automatically generate multiple batches of test data for different test requirements, thereby improving the accuracy of data testing.

The invention relates to a protocol fuzz testing method and system based on an improved SeqGAN, and belongs to the technical field of protocol fuzz testing. The method comprises the steps that basic data are collected and preprocessed, a generator G alpha for generating a test case and discriminators D gamma and D lambda for evaluating the passing rate and diversity of the test case are designed, a mixed reward is designed, and the two discriminators are adopted to evaluate the test case based on the mixed reward; corresponding loss functions are designed for the generator and the two discriminators, gradient updating is carried out, and training is carried out and completed. A test case generation module of the system is used for executing a protocol fuzzy test method to obtain a test case, then a test is executed through a fuzzy test execution module, and a log module records a test result and an intermediate process response. According to the method, the accuracy of the generated sequence is ensured, and the diversity of the generated use cases is remarkably improved, so that the boundary condition of the protocol is more effectively covered, and the efficiency and the coverage rate of the protocol fuzz test are enhanced.

We provide the system. [Solution] Means for collecting user operation data, Means for acquiring video and audio data, A means for analyzing the acquired data using a multimodal generation AI model and automatically generating suggestions for improving the user experience, A means for automatically generating different user interfaces based on multiple improvement proposals that have been generated, A means for conducting evaluation tests targeting users using the aforementioned different user interfaces and analyzing the results, A system that includes this.

The invention discloses a software testing method of a credential and credential system and electronic equipment. The method comprises the following steps: acquiring feature information of a to-be-tested credential system; according to the feature information of the credential and credential system and a preset hit standard of each standard test domain, determining a target standard test domain matched with the credential and credential system in each standard test domain; based on each target standard test domain, generating a test model corresponding to the credential system; and testing the credential system based on the test model. According to the method and the device, missing or redundancy of the test domain is avoided, the test quality is improved, quantitative evaluation of the test quality can be realized, and the test quality is effectively guaranteed.

The embodiment of the invention discloses a script quality evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: testing a target software function containing positioning instrumentation by running a to-be-evaluated test script, and outputting an instrumentation log through the positioning instrumentation; determining a multi-dimensional quality index of the to-be-evaluated test script based on the instrumentation log; and performing quality evaluation on the to-be-evaluated test script based on the multi-dimensional quality index. According to the embodiment of the invention, the evaluation accuracy when the test script is evaluated can be ensured, and the evaluation efficiency is improved.