19 results about "Characterization test" patented technology

The invention provides a testing method and device based on a large model, an intelligent agent, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, large models, automatic testing and the like. According to the specific implementation scheme, in response to a received description document for a to-be-tested application, a large model is called to generate a test case for testing the to-be-tested application based on the description document, and the description document indicates a to-be-tested function of the to-be-tested application; performing function test on the to-be-tested application by utilizing the test case to obtain test information; calling a large model to carry out test abnormity positioning analysis based on the description document, the test case and the test information, and determining an analysis result; under the condition that the analysis result represents that the test case is abnormal, the large model is called to repair the test case, and an updated test case is obtained.

The invention provides a test data processing method and device, electronic equipment and a system, and relates to the technical field of data processing, and the method comprises the steps: obtaining at least one test picture and a test record table corresponding to a PCBA test; each test picture is used for representing the waveform change of the PCBA test; the test record table is used for representing a test value of the PCBA test; for each test picture, determining target naming data corresponding to the test picture based on a target test type of a target worksheet where a record file name corresponding to the test picture recorded in the test record table is located; renaming the test picture based on the target naming data; and filing the renamed test picture into a target folder corresponding to the target test type. According to the invention, automatic renaming and automatic filing of the test picture can be realized, and the processing efficiency and accuracy of the test data are improved.

The invention discloses a test case determination method and device and electronic equipment. The method comprises the steps that a to-be-verified target code unit is determined; the target code unit represents a code unit corresponding to the to-be-verified function point in the algorithm model; obtaining a coverage rate database, wherein the coverage rate database represents the coverage degree of each test case in the test case library on a preset granularity code unit in the algorithm model; and querying in a coverage rate database based on the target code unit to obtain a test case covering the target code unit as a target test case.

The application discloses a MOFs synthesis route multi-objective optimization method based on machine learning. The method is to collect the synthesis conditions of prepared Ce-UiO-66, and evaluate the defect content and thermal stability thereof through a thermogravimetric analysis curve, as initial data set; the data set is randomly divided into a training set and a test set, eight algorithms are adopted to model each performance of Ce-UiO-66 and select the proxy model for performance prediction; the target achievement probability (PA) value of each performance in the synthesis space is calculated and expanded into a multi-objective evaluation factor; the Ce-UiO-66 material is prepared; the obtained material is subjected to characterization test, if the test data does not meet the requirement, the data set and the proxy model are updated. The application has the advantages of low cost, short cycle and the like in optimizing the catalytic and stable performances of MOFs based on reliable experimental data and machine learning, and can be popularized to the design of synthesis routes of other materials.

The present disclosure provides a method for scheduling joint debugging tests, which relates to the field of software testing technology and can be applied to the field of financial technology. The method comprises: in response to a joint debugging test request, parsing a joint debugging test request message to determine a target test environment; and if it is determined that the environment identification information of the target test environment is ready, forwarding the joint debugging test request message to the target test environment based on the environment identification information, wherein the environment identification information is used to represent the state information and routing rule information of the test environment. The present disclosure also provides a scheduling device, equipment, and storage medium for joint debugging tests.

This application discloses a method and related equipment for detecting the execution results of test cases, to solve the problem in the prior art that it is impossible to accurately determine whether a test case has been truly completed and whether there are any execution omissions based on execution records. The method includes: obtaining the test steps of the test case to be detected and the execution time of the test case to be detected recorded in the test case management platform; determining a log query time window based on the execution time, and obtaining multi-source log records of the target business system within the log query time window; determining a query identifier for querying the operation object in the multi-source log records based on the operation object in the test steps; determining the matching feature information between the test steps and the multi-source log records based on the query results of the query identifier in the multi-source log records, the matching feature information being used to characterize the execution status of the test steps; and predicting the execution completion probability of the test case to be detected using a trained probability prediction model based on the matching feature information.

The embodiment of the invention provides a hardware-in-the-loop test case generation method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a test requirement; inputting the test requirements into the reinforcement learning model to obtain a test case set; the test case set is used for representing a test scene set corresponding to the test requirement; for each test case in the test case set, running the test case, and generating a test response; according to the test case and the test response, calculating to obtain a reward value; and if the reward value is smaller than a preset threshold value, performing iterative training on the reinforcement learning model according to the reward value until the reward value is not smaller than the preset threshold value, and outputting a test case set corresponding to the reward value not smaller than the preset threshold value. According to the scheme, the comprehensiveness of scene coverage of the generated test case is improved.

The invention provides a GISSMO model parameter calibration method and system based on a local formability characterization test. The method comprises the steps that fracture strain data and a load-displacement curve of a material in four stress states of shearing, plane strain, uniaxial stretching and equal biaxial stretching are obtained through the local formability test; based on the obtained fracture strain data and the corresponding stress three-axis degree, fitting to obtain a stress three-axis degree-fracture strain curve of the material; inputting the obtained stress triaxial-fracture strain curve and a large deformation equivalent stress-strain curve obtained through a shear test into a material model of finite element simulation software; and taking the obtained load-displacement curve as a target curve for simulation optimization, and calibrating damage parameters of the GISSMO model by using an optimization algorithm to obtain GISSMO damage model parameters. According to the method, a complete solution is formed through process optimization, and finally, the accuracy, reliability and efficiency of parameter calibration of the GISSMO model are comprehensively improved.

The invention relates to a characterization test specimen. According to the invention, the specimen is externally shaped into an oblong form and is internally hollowed out by an oblong orifice (4). The invention also relates to a mechanical testing method using such a test specimen. FIGURE IN ABRIDGED DIAGRAM: Fig. 1

Disclosed is a process for characterizing a fixture component of a test fixture. The fixture component comprises a test instrument interface and a device under test, DUT, interface. The process comprises connecting a test instrument to the test instrument interface; sequentially establishing a plurality of characterization setups for the fixture component; and characterizing at least one scattering, S, parameter of the respectively established characterization setup. The establishing respectively comprises one of: providing an open circuit at the DUT interface, providing a short circuit at the DUT interface, providing a further fixture component of the test fixture at the DUT interface via mated connectors, and providing a DUT at the DUT interface. This avoids inaccuracies, high cost and availability issues in connection with lead-out structures of test fixtures.

The application discloses a test case analysis method and device, a processor and an electronic device. It relates to the field of artificial intelligence, and the method comprises the following steps: obtaining time sequence data, text data and attribute data of a failed test case, wherein the time sequence data is data representing the stability of the execution process of the test case, the text data is data representing the particularity of logs in the execution process of the test case, and the attribute data is data representing the uniqueness of the test case; inputting the time sequence data, the text data and the attribute data into a target model to obtain a failure cause, wherein the target model is trained by a plurality of sets of training samples, and each set of training samples comprises time sequence data, text data, attribute data and a failure cause of a historical failed test case; and adjusting the execution method of the test case based on the failure cause. Through the application, the problem of low efficiency in analyzing the failure cause of a failed test case in the related art is solved.

The invention discloses a serial test method, device and system, electronic equipment and a storage medium, and relates to the field of vehicle-mounted network testing, and the method comprises the steps: analyzing obtained test configuration information in response to a test task instruction, and obtaining to-be-executed test projects and an execution sequence of each test project; and on the basis, testing each test project according to the execution sequence, and determining an execution file and an environment configuration file of the test project from the industrial personal computer based on the test project identifier representing the test project during each test. And then an environment configuration project in the industrial personal computer is called to set the operation environment of the test project according to the operation environment requirement indicated by the environment configuration file. And finally, in response to a running environment configuration completion signal, starting the industrial personal computer to execute the execution file for engineering testing. Testing can be continuously executed according to the testing queue, human participation is not needed in the testing process, and the testing efficiency can be effectively improved.

The invention discloses a multi-MCU software continuous integration method and device based on a hierarchical configuration management architecture and a medium, and relates to the technical field of automobile electronic software development and integration, and the method comprises the steps: obtaining a to-be-integrated code set and integration information corresponding to a plurality of MCUs corresponding to a first user authority; executing a compiling test process on the to-be-integrated code corresponding to the current MCU; under the condition of determining that the test result represents that the test is passed, switching to a to-be-integrated code corresponding to the next MCU, and executing a compilation test process until the to-be-integrated codes corresponding to the plurality of MCUs all pass the test; and generating a corresponding installation package for each MCU, respectively deploying the installation package to a test environment corresponding to each MCU for testing, and performing software release under the condition that the test is passed. The method is used for solving the problem that in the prior art, the integration efficiency is low when software of multiple MCUs is continuously integrated, and continuous integration of the software of the multiple MCUs is automatically, efficiently and rapidly completed through a distribution configuration management architecture.

The invention discloses a data sandbox resource isolation automatic verification method, which belongs to the technical field of computer security, and comprises the following steps: constructing a simulation instruction, executing a resource instantiation operation in an isolation space of a target data sandbox based on the simulation instruction, and generating a source end test resource set; traversing a system interface and a network port of the external host environment, and performing feature matching and searching based on the unique feature identifier of the source end test resource set to obtain an external environment resource mapping snapshot; according to the existence incidence relation between the external environment resource mapping snapshots and the source end test resource set, cross-border resource items are recognized, and isolation failure state descriptors are generated; and analyzing the isolation failure state descriptor, carrying out risk quantification and vulnerability classification, and generating a data sandbox isolation efficiency verification report. By adopting a mode of constructing characterization test resources in an isolation space and actively detecting an external environment for matching and comparison, a resource isolation failure point can be identified, and a risk can be quantitatively evaluated.

The invention discloses a transient characteristic quantitative characterization and test system and method for a power converter, and the method comprises the steps: building a characterization and test system which comprises a main control module, a test host and a function test module through defining the type of a transient characteristic quantitative characterization test; a starting characteristic test module, a surge current test module, an audio signal suppression characteristic test module and an input stepping voltage response test module are utilized. Through selection and switching of the function test modules of the load current transient response test module and the short circuit recovery characteristic test module, test waveform generation and parameter calculation of different test task types are realized, and output judgment of qualified or unqualified display marks is carried out according to a parameter calculation result.

This application relates to a method, system, apparatus, device, storage medium, and product for error injection testing. The error injection testing method includes: a programmable execution unit, in response to receiving a start operation from a main control processor, reading test case configuration information from a first preset address in shared memory; executing an error injection instruction sequence corresponding to the test case configuration information to trigger a target exception; generating a status code representing the test conclusion based on the matching result between the actual exception information of the target exception and the expected exception information corresponding to the test case configuration information; and triggering a fast interrupt to the main control processor in response to the status code indicating that the test has passed, thereby driving the main control processor to load the next test case. This method improves the efficiency and repeatability of test execution and achieves a high degree of automation and closed-loop management of the error injection testing process.

Method for generating a classification neural network (CNN) model for reliability states of a flash memory device (33) that can be read by sending a threshold voltage shift read instruction (24) to the flash memory device (33) which includes a threshold voltage shift offset (TVSO) value for each threshold voltage region required to read the flash memory device (33), wherein the method comprises: Receiving (102) an input specifying a framework for the reliability state CNN model, wherein the framework includes test criteria and a training algorithm; Receiving (103) training data files that specify for each of a plurality of different word lines a Flash Characterization Test Error (FCT-ERROR) value, which specifies a number of Flash Characterization Test Errors, wherein a marker specifies one of a plurality of reliability states and a set of minimum threshold voltage shift offset (TVSOmin) values; Performing (105) the training to generate the reliability state CNN model using a training dataset that includes the received training data files, wherein the reliability state CNN model is configured to predict the reliability state of the flash storage device (33); Testing (106) the reliability state CNN model to determine whether the reliability state CNN model can predict each of the reliability states using the training data set, the testing including determining whether the test criteria are met for each of the reliability states; If the trained neural classification network model fails to meet the test criteria for a particular reliability state, remove (108) from the training data files corresponding to the reliability state that fails to meet the test criteria in a particular TVSO region, from training data files with a non-zero TVSO value to form an updated training dataset, and repeat the training and testing using the updated training dataset; and If test criteria for each of the reliability states are met, save (109) configuration files for the reliability state CNN model.

This application provides a software testing method, apparatus, electronic device, and storage medium. The method includes: acquiring multiple types of test cases for the software under test and the initial priority of each test case; executing at least some test sub-cases in each test case to obtain execution result data and execution process data; determining the priority adjustment value of the corresponding test case based on the execution result data and execution process data; determining the target priority of the corresponding test case based on the initial priority and priority adjustment value of each test case; the target priority is used to characterize the execution order of the test cases; and executing multiple types of test cases according to the execution order of each test case to obtain the test results of the software under test. By executing the sub-cases in each test case and re-determining the priority of each test case based on the execution data, test cases that are more valuable for discovering software defects can be executed first, improving the efficiency and accuracy of software testing.

This disclosure relates to an automated interface testing method and system, comprising: sequentially executing test steps in test cases for a target interface to obtain a first response result for each test step; when executing any target test step in the test cases, independently and in parallel with the execution of the target test step, initiating at least one verification operation to the endpoint involved in the target test step to obtain a second response result including each verification operation; the type of verification operation is dynamically determined based on the characteristics of the target test step; for each target test step, obtaining a test result based on the consistency comparison between the first and second response results of the target test step; wherein, when the test result indicates a test failure, the test result at least identifies the root cause of the failure as an inherent product defect or an occasional environmental problem. This can reduce the false alarm rate and improve the reliability of the test results.