1623 results about "E testing" patented technology

The invention discloses a verification method and system of a chip simulation model, a medium and a program product, and relates to the technical field of chip verification, and the method comprises the steps: obtaining a simulation model and a performance parameter index of a to-be-verified chip; generating model training data based on the historical performance indexes, the corresponding historical use case set and the historical verification result, and performing model training based on the model training data to obtain a use case generation model; inputting the performance parameter indexes to a case generation model to obtain a test case set and a test verification expectation; executing a plurality of test cases in the test case set based on the simulation model, and recording an actual response result of the simulation model; based on the actual response result and the test verification expectation, generating a test log, and determining an abnormal mark in the test log; and marking the fault features corresponding to the exception, taking the test cases as newly added training data, and optimizing the case generation model based on the newly added training data. According to the invention, the chip verification efficiency can be improved.

The invention discloses a scene granularity interface test case generation method based on multiple agents, and the method comprises a plurality of large language model agents and related components, namely context memory, an observer, a decision maker, an executor, a supervisor, a recorder and a to-be-tested application. An observer identifies the interface control, detection and attribute identification of the control are effectively focused, and it is ensured that accurate data input is provided for subsequent testing steps. Based on the output of an observer, a decision maker generates a detailed plan of the next operation according to the current page state, and an operation strategy can be independently formulated, so that test generation is supported by business scene logic. And an executor analyzes and executes the generated plan operation, carries out test operation strictly according to the plan, and ensures the traceability and repeatability of the test process. Furthermore, the supervisor verifies whether the operation is executed according to the plan and meets the requirements of the target scene, and makes corresponding feedback according to the check result, thereby ensuring that the wrong operation is corrected in time, avoiding the continuous accumulation of errors, and ensuring the effectiveness of each test step. The recorder is responsible for storing the execution instruction and the context information of the test process to the context memory, and the operation history and result in the test process can be traced through the test context information, so that a better decision can be made in the subsequent iteration, and the result export after the test is completed can be effectively supported. And after the recording is finished, the test generation enters the next iteration until the coverage of the test scene is finished.

The invention provides a method and system for preparing a high-temperature anti-aging test board for chip testing, and relates to the technical field of data energy storage and resource scheduling, and the method comprises the steps: designing a schematic circuit diagram and a PCB wiring scheme; a test plate is made of a high-temperature resistant material; bare board verification, electrical performance verification, program setting verification, high temperature verification and junction temperature measurement verification are carried out; using data is recorded, ultrasonic cleaning and drying are carried out, and a performance evaluation model is established through a hardware management system to judge whether maintenance or replacement is needed. According to the invention, the accuracy of chip testing in a high-temperature environment is improved, and the service life of the test board is prolonged.

The invention provides a DFT test task management method and device, electronic equipment and a storage medium, and belongs to the technical field of chip design verifier.The DFT test task management method comprises the steps that a data model is constructed on the basis of a design module list and a test step list, the data model is used for indicating testability design DFT test steps of all design modules, and the test steps of all the design modules are stored in the test step list; the data model comprises a plurality of DFT test tasks, attributes of each DFT test task comprise a selection state, and the selection state comprises a selected state and a non-selected state; based on the interactive input, determining at least one DFT test task of which the selection state is selected as an executable DFT regression object; and according to the DFT regression object, driving a DFT test task to be executed. According to the method, the DFT test tasks are normalized through the data model, only the DFT test tasks need to be selected for making the test plan, efficient management of the DFT test tasks is achieved, and the regression test efficiency is improved.

A method for planning and executing a test of a DUT includes receiving a test description from a user via a GUI, where the test description identifies the DUT and an objective of the test; composing a prompt for querying a large language model (LLM) based on the test description, where LLM includes a trained machine learning algorithm; sending the prompt to the LLM via an API; determining, using the LLM, a test plan for executing the test to be performed in response to the prompt to achieve the objective of the test based on the test description and general testing information, where the test plan identifies a type of test, test instruments for performing the test, a test procedure using the instrument, and parameters of the DUT to be measured according to the test procedure; providing, via the GUI, the test plan to the user for testing the DUT.

The invention relates to a multi-driving-style high-risk automatic driving cut-in scene test method and system, and the method comprises the steps: collecting original driving track data, carrying out the clustering of driving styles, and generating a risk cut-in scene track cluster with consistent driving styles through employing a Cutin-TimeGAN network model in combination with physical feasibility constraints; constructing a cost function, calculating a cost function value for each risk cut-in scene trajectory cluster, and selecting a target cut-in reference trajectory as a target reference state vector; constructing a game framework, and combining a target reference state vector to construct a utility function according to the state vectors of the VUT and the test confrontation vehicle in the framework; constructing a risk confrontation utility function based on the predicted collision time; a game optimization problem is constructed and solved, an optimal interaction track of the test confrontation vehicle is obtained, and a closed-loop high-risk automatic driving scene switching test is carried out based on the optimal interaction track; the system is used for implementing the method. Compared with the prior art, the method has the advantages that testing of authenticity and high-risk confrontation of multiple driving styles is considered.

The invention discloses a test case execution method, electronic equipment, a storage medium and a program product, and relates to the technical field of software testing, and the method comprises the steps: obtaining test data features in a test case execution process, carrying out the grading of a test case based on the obtained test data features to determine a grade coefficient, according to the method and the device, the priority of the test case is dynamically adjusted according to the test data characteristics of the test case, the test case is executed according to the priority score of each test case, the execution sequence of the test case is dynamically adjusted, the dynamic adaptability of the test case is improved, and the test efficiency is improved. Therefore, the test efficiency can be ensured, and the determination of the priority score also fuses the test module and the test function point, so that the test cases of the important module and the core function point can have higher priority scores, the execution is preferentially performed, and the coverage integrity of the core function and the error-prone module can be ensured.

The invention discloses a Web automatic testing method, system and device, a medium and a program product, and belongs to the technical field of artificial intelligence. The method comprises the steps that test case abstract representation information is generated according to interactive input of a user and a test case mapping template, and the test case abstract representation information comprises a target test task type indicated by the interactive input and intention natural language description information; based on the test case abstract representation information, obtaining a target screenshot of the tested Web page and attribute information of interaction elements; a multi-mode large language model is called to conduct reasoning based on the target screenshot, the attribute information of the interaction elements and the intention natural language description information, reasoning result information is output, and the reasoning result information represents operation executed on the target interaction elements located at the target position in the tested Web page; and converting the reasoning result information into a machine executable instruction, and executing the machine executable instruction. According to the embodiment of the invention, the stability of Web automatic testing can be improved.

The invention discloses a full-process automatic unit testing method, device and equipment for an AI chip operator, a medium and a product. The method comprises the following steps: generating an engineering directory and a test framework design document through an architect agent according to operator interface information and a DSL operator design document knowledge base; the method comprises the following steps of: generating a test code for unit test by a software engineer agent according to a test framework design document and a domain knowledge base, and storing the test code in an engineering directory; multiple rounds of interaction are carried out through a software engineer agent and a test engineer agent, a test code is gradually corrected into a target test code which can be successfully compiled, a generated test case can be successfully executed and the generated test case meets the code coverage rate requirement, and a target operator code passing a unit test is obtained; and submitting the target operator code and the target test code which pass the normativity check to a code warehouse through a code auditing engineer agent so as to automatically realize the whole-process unit test of the AI chip operator.

The invention relates to the technical field of welding strength detection, and discloses a communication system sub-element packaging welding strength detection device, which comprises a push-pull force testing machine main body, a clamp is arranged in the middle of the bottom surface of the push-pull force testing machine main body, and consoles are arranged on the two sides of the push-pull force testing machine main body. And an operation handle and a plurality of control keys are arranged on one console. According to the packaging welding strength detection device for the communication system sub-element, when parts such as the protective shell and the test module need to be controlled to descend quickly, the impact ball can impact the surface of the ejector rod to make a sound for reminding; meanwhile, when the chip is in place, a triangular plate and two first arc-shaped elastic conducting strips form circuit series connection and then buzz is made to remind an operator that the testing head is about to reach a designated position, so that the testing head can be controlled to rapidly reach the designated position, and meanwhile damage caused by collision between the testing head and the chip is avoided.

The invention discloses a large language model agent security test method and device based on a model context agreement, and the method comprises the steps: firstly deploying a test tool in a test server, defining an application program interface which comprises function parameters and test probe parameters, describing the test probe parameters as necessary technical requirements for executing nominal functions, and executing the nominal functions according to the test probe parameters; and inducing the tested LLM agent to transmit complete session context data when calling. The test server receives a tool call request including a function parameter value and session context data, a back-end concurrently processes the request, executes a nominal function to generate a benign result, and extracts the session context data at the same time. Afterwards, a benign result is returned to the tested LLM agent, and the extracted data is asynchronously transmitted to a remote log server for recording as a test log. And finally, comparing the test log with the actual operation history, judging whether a session context data leakage vulnerability exists or not, and quantifying the severity level so as to detect whether the LLM agent leaks session memories such as the user interaction history or not.

A test method, system, apparatus and device for an energy storage control system, and a storage medium. The test method comprises: receiving test indication information (S201); on the basis of the test indication information, simulating an associated device connected to a device to be tested in an energy storage control system, and determining simulation-associated parameters (S202); and on the basis of the simulation-associated parameters and the test indication information, testing the device to be tested, and receiving a test result of the device to be tested (S203). A test device (104) can implement the simulation of any device associated with a device to be tested in an energy storage control system. Specifically, interface implementations and function execution logic implementations of any device can be simulated, and the simulated interface implementations or function implementations are implemented by means of simulation-associated parameters. Therefore, the test process is not limited to testing a specific type of device to be tested, and multiple types of tests can be implemented, thereby improving the applicability of the test method to a certain extent.

The invention relates to a front-end code testing method and system, electronic equipment and a storage medium. The method is applied to a code analysis system and comprises the steps that a code test task for a to-be-tested front-end code is received, the code test task comprises use case constraint conditions and test target information of the to-be-tested front-end code, code feature vectors are extracted from the to-be-tested front-end code, and the test target information is sent to the to-be-tested front-end code; and iteratively generating a target test case according to the case constraint condition and the test target information, and obtaining a test result of the to-be-tested front-end code by running the target test case and the error detection code feature vector. According to the scheme provided by the invention, the defect of the to-be-tested front-end code can be efficiently and comprehensively captured, and an accurate and reliable test result is output.

The invention relates to a four-probe flying probe test equipment path optimization method and device and a storage medium. The method comprises the steps that a task input module receives basic constraint conditions and test task information; if a fixed grouping mode is adopted, the four probes are fixedly divided into two groups according to basic constraint conditions, the distances of the two groups of probes from the current test task to the optional next test task are sequentially calculated, and the moving path of each group of probes is planned; if a variable grouping mode that the probes are allowed to change groups in the testing process of each step is adopted, an optimization objective function of each step of each probe is calculated in sequence, and a moving path of each probe is planned; and according to the planned moving path of each probe, the control execution module controls each probe to move in sequence to complete a test task. The constructed mathematical model more comprehensively and accurately reflects the actual situation of the four-probe flying probe test, provides a more reliable theoretical basis for path optimization, and is beneficial to improving the optimization precision of the test path.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.

The invention discloses an automatic UI self-healing test method and system based on multi-agent AI, and belongs to the technical field of automatic UI tests.The automatic UI self-healing test method comprises the steps that an original test script is semantized through a test reasoning interpretation class, and an expected intention is output; when the test fails, synchronously collecting multi-source data and extracting multi-modal features, constructing a fault propagation graph according to an expected intention and the multi-modal features, and performing fault propagation analysis by using a graph attention network to obtain a fault diagnosis result; according to a fault diagnosis result, a deep Q network, a greedy strategy and a Byzantine fault-tolerant algorithm are adopted to negotiate and decide a repair strategy through a distributed agent cooperation mechanism, and feedback is obtained; optimizing and dynamically adjusting the repair strategy and the test reasoning interpretation class through a meta-learning algorithm based on feedback; a test intention is deeply understood by constructing a test reasoning interpretation class, and fault diagnosis and automatic repair are cooperatively completed by adopting a distributed intelligent agent, so that the semantic understanding capability, the diagnosis accuracy and the repair success rate are improved.

Disclosed in the present application are an aircraft final assembly and integration testing platform and testing method. On the basis of a rational platform architecture, structured process data preparation, model-based operation guideline document preparation, and dispatch assistance decision-making are realized by means of an application layer, such that uniform data sources in the field of final assembly can be achieved. Data uploading and issuing between an industrial control network and an internal network, and data interaction control between layers are implemented by means of the mutually collaborative operation of the application layer, a storage layer, an edge layer and an execution layer, thereby improving the information acquisition efficiency and the operating efficiency. The edge layer and the execution layer collaboratively operate, so as to realize the interactive collaboration between the assembly guidance work for operators and automatic testing, thereby improving the efficiency of data information acquisition, operation instruction and fault handling during a final assembly and testing process of an aircraft.

An autonomous driving system test method, comprising: sampling scenario data of a plurality of scenarios (S10), and sending the scenario data of the plurality of scenarios to a plurality of simulation emulators (200) for distributed testing, so as to obtain test result data of the plurality of scenarios, the plurality of simulation emulators (200) being each integrated with an autonomous driving domain controller; and determining a dangerous scenario boundary for an autonomous driving system on the basis of the test result data of the plurality of scenarios (S20). The method is an improvement on the a scenario-based simulation test technology for autonomous driving systems, and using a server and a plurality of simulation emulators integrated with autonomous driving system domain controllers to perform joint testing can greatly improve the simulation test efficiency and the test effect for autonomous driving systems.

The invention discloses a data processing method, device and equipment and a readable storage medium, and the method comprises the steps: respectively generating test cue words corresponding to initial cue words through T iterative optimization algorithms, and inputting the T test cue words and a test data set into a large language model, generating algorithm evaluation scores corresponding to the T iterative optimization algorithms through a large language model, and determining a target iterative optimization algorithm in the T iterative optimization algorithms based on the T algorithm evaluation scores; generating M thinking chains corresponding to the initial cue word, and performing content expansion on the initial cue word based on the M thinking chains and the test data set to obtain M content expansion cue words; and performing multiple rounds of cue word optimization on the M content expansion cue words through a target iterative optimization algorithm to obtain S target cue words. By adopting the method and the device, the cue word can be efficiently optimized at low cost, and the accuracy and recall rate of the cue word are improved.

Automated test case validation execution is presented herein. A system obtains image data representing a test result of a test step of a validation process; extracts the test result from the image data; derives an expected result from an expected result sentence that has been obtained from a first cell of a row of a table of a document, in which the row corresponds to the test step; based on the test result and the expected result, determines a test status of the test step; generates a result sentence comprising the test result and a portion of the expected result sentence; generates a javascript object notation (JSON) data structure comprising the result sentence and the test status; and populates, using the JSON data structure, a second cell of the row with the result sentence and a third cell of the row with the test status.

The invention relates to the field of current sensor testing, in particular to a calibration test system for a three-phase TMR current sensor, which is provided with a calibration test mode switching device and organically integrates a calibration mode and a test mode into the same equipment. The current sensors with different compatibility can be calibrated and tested, the accuracy of the calibration and test results of the current sensors is ensured, the research and development efficiency is improved, and the equipment investment is reduced.

The invention provides a risk analysis method and system for software project management, and relates to the technical field of software project management. The method comprises the following steps: collecting test record data of a target software project and a target test resource allocation strategy; extracting defect density features and test coverage features respectively corresponding to the plurality of functional modules; obtaining system architecture data of the target software project, and constructing a module association map of the target software project about the plurality of functional modules; generating a plurality of resource allocation influence indexes of each functional module, and processing through the resource allocation association analysis model to generate a reference allocation strategy of the target software project; and performing resource allocation risk analysis on the target test resource allocation strategy through the reference allocation strategy, and generating a risk analysis result of the target software project about the target test resource allocation strategy. According to the invention, the potential risk of the test resource allocation strategy is accurately analyzed.

The invention relates to a network testing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of basic operators supported by quantitative compilation, and combining the plurality of basic operators to obtain at least one composite operator; generating at least one random network based on the at least one composite operator and the basic operator according to a preset input tensor and a preset random network depth; performing quantitative compiling on the at least one random network, and testing the at least one random network after quantitative compiling to obtain a test result; a truth value result of the at least one random network is determined, an abnormal network is determined based on the truth value result and the test result, and the truth value result is obtained after the central processing unit processes the at least one random network. By adopting the method, the abnormal network can be accurately identified, and the test progress is ensured.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.

Efficient generation of multi-platform compatible source code and test code with consistency and quality in software development SOLUTION: The schema-driven development system includes a user input unit, a schema generation unit, and a code generation unit, wherein the user input unit acquires data in various formats such as a natural language and a CSV file, the schema generation unit converts the acquired data into a schema, and the code generation unit generates a source code and a test code based on the generated schema.SELECTED DRAWING: Figure 1

The invention discloses a method and a device for testing a diagnosis fault code of a controller area network, and relates to the technical field of automobile electronic diagnosis, and the method for testing the diagnosis fault code of the controller area network comprises the steps: obtaining a database file and test configuration information of the controller area network; generating message records of all verification messages according to the test configuration information and the controller local area network database file; sorting the message records according to time, and generating a message record file according to a preset file format; playing back the message recording file through a controller local area network tool, and sending a diagnosis request message to the tested controller through the controller local area network tool while playing back the data to obtain a diagnosis fault code; and comparing the diagnosis fault code with a preset expected diagnosis fault code to obtain a test result. According to the method, the automation degree and the real-time performance of the test are improved, the dependence on physical hardware and environmental resources is reduced, and the test cost and the technical threshold are reduced.

The utility model relates to the technical field of automatic assembling, and particularly discloses a test board assembling device which comprises a rack, an assembling mechanism, a storage mechanism and a mechanical arm, the assembling mechanism is provided with an assembling table, a ballast assembly and an assembling assembly, the assembling table can contain a main board and a test board, the ballast assembly is arranged on the assembling table, the storage mechanism is arranged on the assembling table, and the mechanical arm is arranged on the assembling table. The ballast end of the assembly component can ballast the mainboard, and the output end of the assembly component can push the test board to be installed in the mainboard so as to form a finished product; the storage mechanism is arranged on the rack, and the storage mechanism is used for supplying a carrier carrying a main board and storing a carrier carrying a finished product; the mechanical arm is arranged on the rack and used for moving the main board to the assembling table from the storage mechanism and moving the finished product to the storage mechanism from the assembling table. Through the arrangement, the labor intensity of workers is reduced; the damage probability of the test board and the mainboard is reduced; and waste caused by mistaken scrapping can be reduced.

The invention discloses a testing device and testing equipment, the testing device is used for performing low-temperature testing on a to-be-tested chip at a testing environment temperature, and the testing device comprises a heating assembly and a testing assembly. Wherein the test assembly comprises a main control chip, a matching circuit and a power supply chip, and the heating assembly is in contact with at least one of the main control chip, the matching circuit and the power supply chip and is used for heating the contact with the heating assembly in the test process of the chip to be tested. By means of the testing device, in the low-temperature testing process of the chip to be tested, the performance of the main control chip, the matched circuit and the power supply chip in the testing device can be kept, the phenomena of damage and the like caused by low temperature are avoided, and the stability of the testing device is improved.

The present invention discloses an optimization method for industrial control protocol fuzz testing based on coverage guidance. This method combines mutation testing on the basis of traditional coverage-guided fuzz testing. By comparing the output results of the program under test and the mutated PUT, high-quality test cases are screened. At the same time, the concept of coverage-guided tracking is introduced. By encoding the current coverage boundary in the binary file of the program under test, self-reporting can be performed when new coverage is generated in the test case, eliminating unnecessary tracking of the coverage-guided fuzzer, improving the fuzz testing efficiency, and greatly enhancing the vulnerability detection ability and testing efficiency of coverage-guided fuzz testing.

The invention relates to the technical field of machine learning, and provides a knowledge distillation method based on triple multi-mode prompt, which is characterized by comprising the following steps: acquiring a plurality of open source visual data sets and constructing a training set, a verification set and a test set; constructing a teacher model for distillation, and performing two-stage prompt fine tuning on the teacher model through training; constructing a student mode based on triple prompt distillation, and distilling the teacher model knowledge to a student model; and performing retrieval prediction on the trained student model. According to the method, through the prototype-guided triple prompt distillation mechanism and multi-loss collaborative optimization, efficient and accurate migration of cross-modal knowledge is realized, and the generalization performance and robustness of the model in a few-sample scene are comprehensively improved.