311 results about "Test data generation" patented technology

The invention provides a solid state disk testing method and device, equipment and a medium, and relates to the technical field of hard disk detection. The test method comprises the following steps: in a process of writing data into physical areas of a to-be-tested solid state disk according to a physical address in sequence, randomly selecting at least part of areas from the physical areas written with the data at a preset time interval to perform reset operation until the physical areas reach a target area state, the target area state comprises a target filling state and a target fragmentation state; executing a read-write test on the physical region in the target region state to obtain test data; and generating a test report based on the test data.

The invention discloses a memory test system and method, a board card and a server. The memory test system comprises an out-of-band management unit and a target chip, the out-of-band management unit sends a pressure test instruction to the target chip through the preset communication interface, the pressure test instruction comprises dynamically adjusted test strength parameters, and the pressure test instruction is generated based on a preset instruction set; the target chip performs calibration verification on the target memory in response to the pressure test instruction; after the calibration verification is passed, a test load is applied to the target memory according to the test strength parameter, and a cyclic pressure test is executed; the target chip collects test data generated in the pressure test process and transmits the test data back to the out-of-band management unit through the preset communication interface, and the out-of-band management unit generates structured test data according to the test data. Compared with the prior art, the situation that normal services are interrupted due to testing in a traditional testing method can be avoided, and the availability and stability of the system in the testing process are guaranteed.

The invention discloses a method and device for automatically generating a Java method unit test case based on AI, and the method comprises the steps: building a structured annotation specification, carrying out the automatic traversal and intelligent analysis of a Java project, extracting a method signature, a parameter type and a parameter constraint condition, constructing a test data generation model, and carrying out the automatic generation of the test case. The model is transmitted to an AI service to analyze business logic and boundary conditions, test case codes are generated, test cases are optimized and integrated to a project test directory, finally, code areas with insufficient coverage are identified through coverage rate analysis, a test completion strategy is generated, and iterative perfection of the test cases is completed. The technical problems that in the prior art, project-level automatic test case generation cannot be achieved, the generated test case cannot fully cover business logic and boundary conditions, and context relation understanding is lacked are solved, full-automatic project-level test generation is achieved, and the test efficiency and quality are improved.

The invention provides an intelligent test platform, which comprises a test demand analysis module used for carrying out demand analysis on input test demand information based on a demand analysis agent to obtain a plurality of test demand items; the test case generation module is used for calling a case generation agent to generate a plurality of test cases corresponding to a plurality of selected target test demand items in response to a selection operation on the plurality of test demand items; the test script generation module is used for calling a script generation agent to generate at least one test script corresponding to at least one checked target test case in response to check operation on the test cases; the test data demand analysis module is used for calling a data demand agent to perform demand analysis on the test case to obtain a plurality of test data demands; and the test data generation module is used for calling a data generation agent to generate test data corresponding to the test data demand. According to the invention, the test efficiency and the test reliability are improved.

The invention relates to the technical field of data testing, in particular to a testing method and system based on large model multi-modal generation and dynamic verification, and the testing method based on large model multi-modal generation and dynamic verification comprises the following steps: initializing a testing system, deploying a pre-trained large model, and generating testing data through the large model; dynamically detecting the generated test data through a preset abnormal mode knowledge base, and feeding back an abnormal result; and according to an abnormal result, reasoning and positioning an abnormal root cause through a preset causal reasoning model, and carrying out interpretable causal analysis output. According to the method, an iteration mechanism of test data generation, detection and feedback can be established, the generation work of the test data is reversely optimized through the abnormal result, all the generated test data can be ensured to pass basic verification, the compliance of the test data is ensured, dynamic detection is performed through the preset abnormal mode knowledge base, and the test efficiency is improved. Compared with a traditional static model, the method is higher in detection accuracy and processing efficiency.

The invention discloses a method and device for generating mass database table test data based on configuration, and the method comprises the steps: obtaining table building information, extracting table metadata from the table building information, and carrying out the standardization processing of the table metadata, and obtaining the standardized table metadata; obtaining a data generation template based on the standardized table metadata; modifying the data generation template according to a target demand to generate a target data generation template; extracting global variable information from a global configuration node of the target data generation template; analyzing a data generation rule corresponding to each field based on the global variable information, and generating test data based on the field generation rule; and based on the test data, generating an insertion statement, and writing the insertion statement into a preset file to obtain a final test data file. According to the embodiment of the invention, full-process automation from table metadata extraction to test data files can be realized, real association of data is guaranteed, and test effectiveness is improved.

The invention discloses a communication protocol automatic analysis and test data generation method and system, and the method comprises the steps: analyzing an unstructured protocol document, recognizing a fixed field and a variable field in the document, and outputting structured data; automatically generating test data based on the structured data, dynamically calculating a checksum according to a specified check algorithm type in the structured data, and filling the checksum into the original message to form a complete test message; and generating a standardized test data set according to the complete test message. According to the method, manual intervention is not needed from document analysis to test data generation, the analysis efficiency is improved, and most protocol scenes are covered through the boundary value, the abnormal value and the orthogonal combination strategy. The method supports various document formats, data types and verification algorithms, adapts to protocol standards of different industries, facilitates problem positioning and regression testing through standardized output documents, and reduces the maintenance cost.

The invention relates to the technical field of electric vehicle charging equipment testing, in particular to direct-current charging and discharging gun interoperation portable testing equipment and method, and the equipment comprises a direct-current charging gun plug; the charging and discharging control circuit is arranged in the direct-current charging gun plug and is used for controlling the switching of the connection states of the timing plugging gun, the fixed-frequency plugging gun and the charging gun of the direct-current charging gun; and the controller is connected with the charging and discharging control circuit, controls the charging and discharging control circuit to simulate one or more target test scenes of the direct current charging gun, and generates a test result of the direct current charging gun according to test data in the target test scenes. Therefore, the problems that in the charging process of an electric vehicle, due to the fact that an effective charging gun electronic lock unlocking and locking function testing means is lacked, whether the electronic lock function meets the national standard requirement and the actual use requirement of a user cannot be comprehensively and accurately verified, and then charging potential safety hazards and poor user experience are possibly caused are solved.

The invention relates to the field of software testing, in particular to a financial payment transaction testing method based on recording and playback. The method comprises the following steps: recording an interaction behavior of a user on a front-end interface, capturing back-end request and response data, generating an original operation sequence, and converting the original operation sequence into a parameterized test script; processing parameter variables in the parameterized test script through a dynamic test data generation algorithm, generating test parameters, and further obtaining a dynamic test parameter set; executing an operation instruction based on the parameterized test script and the dynamic test parameter set, and recording actual output; a verification result is generated for the test parameter based on the actual output, and an overall pass rate of the automated test is calculated. The problems that a traditional software test method depends on manual design of test cases and manual test execution, time consumption is long, efficiency is low, and key test scenes are prone to being omitted are solved; all input conditions and abnormal scenes are difficult to cover; a unified automatic process is lacked, and the test process is difficult to reuse.

The invention relates to the field of artificial intelligence large models, and particularly provides a test method and device for large model performance transverse evaluation, and the method comprises the following steps: S1, intelligent test data generation and management; s2, performing streaming performance monitoring based on a Token timestamp; s3, dynamic concurrency and flow simulation; s4, uniformly accessing and executing multiple models; s5, analyzing and visualizing a depth result; and S6, resource monitoring. Compared with the prior art, the method has the advantages that detailed performance comparison data can be efficiently and automatically generated, and a scientific basis is provided for large model selection, performance optimization and resource scheduling.

The invention discloses an application (App) and method capable of intelligently memorizing words, which can generate a personalized memory scene by acquiring personal interests and hobbies of a user, dynamically optimize an individual forgetting curve based on machine learning, and further refine optimal learning or review time according to a personal emotion curve. The system supports multi-language and multi-device adaptation, and comprises a personal interest scene generation module for constructing a personalized learning scene and generating example sentences and multimedia contents associated with interests by using natural language processing (NLP); a dynamic forgetting curve module; a two-stage modeling mechanism is adopted, and a fine-grained word-level review strategy is generated in combination with a group forgetting rule and user real-time test data; an emotion curve and a forgetting curve are combined, and review time is optimized through a double-layer algorithm; the multi-mode reward module is linked with the psychological health service platform through a point exchange mechanism; and the psychological consultation service is linked for the users with poor word reciting scores.

The invention provides an intelligent test data generation and verification method and system based on a hierarchical architecture, and relates to the technical field of data generation, and the method comprises the steps: constructing a cross-layer constraint dependency graph by analyzing each layer of data mode and constraint and conversion logic of a hierarchical architecture software system; generating candidate test data meeting the constraint based on the dependency graph in a back-stepping manner, and verifying the candidate test data through projection; and iteratively optimizing the dependency graph and the test data by monitoring the difference between the actual execution and the prediction form. According to the method, cross-layer data consistency verification is realized, the test coverage rate and the defect detection efficiency are improved, and the quality risk of a software system is reduced.

The invention relates to the technical field of software engineering, and discloses a path coverage test data generation method based on an SA-BKA hybrid optimization mechanism, and the method comprises the steps: generating a control flow diagram through a program, recognizing each decision node through the control flow diagram, and deducing a set of all possible execution path sets as a target path set according to the decision nodes; the paths are grouped through path similarity, different optimization strategies are selected for the paths with different similarities, a simulated annealing algorithm is adopted for the groups with high path similarity, and an adaptive disturbance strategy is introduced, so that the algorithm diversity is enhanced; for low-similarity groups, a BKA algorithm is adopted, Levy flight and a dynamic step length adjustment strategy are fused, and meanwhile balance of the search process is achieved through dynamic step length adjustment. Compared with the prior art, the method has higher search efficiency under the condition of different population scales, and verifies the validity in path coverage test data generation.

The invention relates to the field of semi-physical simulation, in particular to a semi-physical simulation resource scheduling method and device and a storage medium. The method comprises the steps of obtaining a plurality of simulation models, a plurality of simulation devices and a plurality of test tasks in a semi-physical simulation resource library; combining the simulation models with different simulation devices to obtain semi-physical simulation nodes, and generating a plurality of cooperative combinations to cooperatively execute a plurality of test tasks to obtain performance test data of each simulation model; analyzing performance test data of each simulation model, and generating a performance parameter set of each simulation model, the performance parameter set comprising a plurality of scene sensitive indexes and a plurality of static basic indexes; and selecting a target simulation model and target simulation equipment from the semi-physical simulation resource library based on the simulation model requirements and the performance parameter set of each simulation model so as to execute a target simulation task. The scheduling strategy is highly adaptive to the actual operation environment, and the resource utilization rate and the scheduling accuracy are improved.

The invention relates to the technical field of automobile part information security evaluation, and discloses an automobile part information security evaluation system based on a multi-dimensional penetration test, and the system comprises a data collection module, a vulnerability analysis module, an event monitoring module, a parameter matching module, a response execution module and a self-learning module. The data acquisition module acquires test data in real time, and generates a dynamic adjustment set of vulnerability severity feature values and security parameters; the vulnerability analysis module generates a personalized security parameter and a dynamic penetration control strategy based on the feature value; the event monitoring module screens abnormal event data; the parameter matching module determines a target adjustment parameter through multi-dimensional matching; the response execution module drives the safety equipment to correct parameters; and the self-learning module updates the security parameters by using the feedback data. The system realizes dynamic evaluation and self-adaptive protection of automobile part information safety, improves the safety protection level, and is suitable for the technical field of automobile part information safety evaluation.

The invention relates to the field of testing, and particularly discloses an Internet of Vehicles testing system and method, so as to improve the testing accuracy. The system comprises a sensing layer used for acquiring device state data of a plurality of Internet of Vehicles devices in a current time period; the edge computing layer is used for acquiring test context information corresponding to a current time period through a pre-trained first network model, generating a first test case based on the equipment state data and the test context information, and distributing the first test case to an edge computing module; the platform layer is used for generating a second test case according to the historical test data through a pre-trained second network model, and distributing the second test case to the edge computing module and / or the cloud equipment; the application layer is used for generating and displaying a visual test result according to test data generated by the edge calculation module and / or the cloud equipment for the test case; wherein the model parameters of the network model are dynamically adjusted by adjusting the penalty coefficient.

The embodiment of the invention provides a method, device and system for testing automatic driving based on artificial intelligence, and a storage medium, and relates to the technical field of automatic driving. The method comprises the steps of collecting driving data of an automatic driving vehicle; controlling to start and stop recording test data according to the driving data through the artificial intelligence large model; generating a test report according to the recorded test data through the artificial intelligence large model; wherein the test report comprises root cause analysis, scene performance differences and / or optimization suggestions. According to the method, the recording package is automatically triggered according to the driving data through the artificial intelligence large model, manual triggering of the recording package is not needed, recording omission of key events can be reduced, root cause analysis, scene classification and performance evaluation are carried out through the artificial intelligence large model, retrieval of massive multi-modal data one by one and dependence on manual annotation and experience judgment are not needed, and the efficiency is improved. And the test efficiency and the reliability of the test result are improved.

The invention provides an intermodulation test network and a control method thereof. The method comprises the following steps: sending a test instruction to a plurality of intermodulation test devices through a switch; and receiving test data uploaded by the plurality of intermodulation test devices based on the switch, and generating an intermodulation test report based on the plurality of test data. According to the method, a test instruction is sent to a plurality of intermodulation test devices through a switch, so that the plurality of intermodulation test devices can work simultaneously, and a signal source is respectively controlled to generate carrier signals in a target frequency band. Compared with a traditional mode that a single device is tested one by one, the multi-device collaborative testing mode greatly shortens the testing time and improves the testing efficiency.

The invention discloses a multi-channel on-resistance performance test system and method, relates to the technical field of electronic component performance measurement, and solves the technical problem of low detection accuracy of a performance test system caused by the fact that an existing component performance test system adopts a fixed delay strategy to carry out performance test. The test module is used for carrying out performance test on elements according to a test scheme; the test temperature and the corresponding recording time are obtained in real time; the acquisition signal generation module is used for generating initial delay duration according to the characteristic data and the test scheme; generating an adjustment delay duration according to the initial delay duration and the test temperature recorded in real time, and generating an acquisition signal according to the adjustment delay duration; the data acquisition module is used for acquiring performance test data of the test element through data acquisition equipment connected with the data acquisition module according to the acquisition signal; the performance analysis module is used for generating an element performance test report according to the performance test data; the accuracy of test data is ensured, and the detection accuracy of the performance test system is improved.

The invention discloses a suspension testing system and method, computer equipment and a storage medium. The system comprises an upper computer, a cabinet and a rack control system, the upper computer is used for receiving the target test data and respectively sending the target test data to the rack control system and the cabinet; the rack control system is used for receiving the target test data, determining a target rack driving signal corresponding to the target test data, and driving the rack based on the target rack driving signal so as to simulate the motion of the tested whole vehicle under the target test data; the cabinet is used for receiving the target test data, generating a target test signal based on the target test data, sending the target test signal to a suspension controller of the tested whole vehicle, obtaining target whole vehicle data generated when the suspension controller executes the target test signal, and returning the target whole vehicle data to the upper computer; and the upper computer is also used for receiving the target whole vehicle data and generating a test result corresponding to the target test data based on the target whole vehicle data.

The process validation platform disclosed herein enables automated validation of data transformation processes based on simulation-based data. For example, the process validation platform can generate metadata associated with a data transformation environment, evaluate the metadata, and generate test data based on the metadata. The process validation platform can generate test cases (e.g., test records) and associated code samples based on the test data and enables updates and review of the test cases. The process validation platform can transmit the code samples to the data transformation pipeline for execution of the associated data validation tasks.

The invention discloses an intelligent test data generation and optimization method and system based on a low-code platform, and belongs to the technical field of software engineering and artificial intelligence, and the method comprises the steps: analyzing a business rule and data model definition information of a low-code application; according to the business rule and the data model definition information, utilizing a generative model to generate initial test data; performing quality evaluation and fault prediction on the generated initial test data; collecting test execution feedback information, including fault types caused by data, performance bottleneck positions and the like; formulating a data generation parameter adjustment strategy according to the test execution feedback information and the service target; and regenerating test data according to the adjustment strategy, and outputting the optimized test data for a subsequent iterative test. And the optimized test data is subjected to effect evaluation, so that the test quality and efficiency are continuously improved. According to the invention, the test data dynamically adapts to business demand changes, the test coverage rate and the fault discovery efficiency are improved, and the labor cost is reduced.

The present invention discloses an intelligent software test data generation method and system based on publicly available Internet information. The method includes: collecting data from the Internet based on software requirements and test scenarios and preprocessing it; enhancing the data using multi-objective region Bayesian optimization technology; processing time-series data using time enhancement and variational Bayesian methods; reading software development documents and extracting key information; performing semantic understanding and extension based on a hierarchical classification Gaussian extended semantic model; generating preliminary test data that conforms to business logic; applying the test data and collecting results; and dynamically adjusting the generation strategy using a reinforcement learning algorithm. The present invention also provides a corresponding system. The method and system can automatically generate high-quality test data, improving software test efficiency and quality.

Methods, system, and computer program product are disclosed for enhanced system level testing of an AI hardware accelerator comprising Artificial Intelligence (AI) primitives. The system level testing provides uncompromised test coverage of the AI hardware accelerator while enabling efficient and effective testing performance. Enhanced test data generation, result prediction, and test execution are provided for system level testing of AI hardware accelerator including the AI primitives.

The invention relates to the technical field of PLC programming language testing in the field of industrial automation control, in particular to an IEC 61131-3-based PLC programming software language function full coverage testing method, which comprises the following steps: establishing a structured test table based on functions of five PLC programming languages; based on the content of the structured test table, a plurality of different function points to be tested are determined, and a test program complete set is determined based on the function points to be tested; the test program complete set is iteratively updated according to a test standard; importing the test program complete set into to-be-tested PLC programming software, and testing grammar analysis, compiling results and runtime behaviors to obtain test data; generating a technical list based on the test data; according to the invention, the test comprehensiveness of the PLC programming language can be improved.

The invention discloses a test data generation and synchronous triggering method and system for stability control joint debugging, and belongs to the technical field of power systems and automation thereof. The method comprises the following steps: a test master station and each test terminal are subjected to time synchronization to realize clock synchronization; the main station decomposes the test into a plurality of state sequences and durations, and sets terminal test parameters; the master station sends state sequence test parameters and a test starting instruction containing a synchronous output moment T to the terminal through a network, the terminal receives the parameters and then distributes the parameters to an internal functional module to prepare data, and when the moment T is reached, the terminal converts electrical quantity data DA and generates switch deflection to be output to the stability control device to start testing; and the terminal sequentially executes each state sequence and collects action results of the stability control device, the action results are sent to the master station after testing is finished, and the master station comprehensively judges and generates a test report according to the results. According to the invention, the test terminal synchronously triggers the fault simulation in a wide area according to the state sequence parameters issued by the test master station, and the problem that the field joint debugging of the stability control system is difficult to synchronously test is solved.

The invention provides a test case generation method and device, equipment, a medium and a program product, and relates to the technical field of artificial intelligence, the method comprises the following steps: detecting a database based on a target demand text to obtain target test data matched with the target demand text, the target test data comprising a test case and defect mode data; summing the M feature index values of the test case according to the corresponding weights to obtain a score; summing the N feature index values of the defect mode data according to corresponding weights to obtain a score; and generating a first test case based on the first test data by adopting a pre-trained agent model, wherein the first test data comprises a test case of which the score is within a first preset range and defect mode data of which the score is within a second preset range. According to the embodiment of the invention, the test case is automatically obtained based on the target demand text, and the first test case is generated in the defect mode, so that manual intervention is reduced, and the test case generation efficiency is improved.

The invention relates to the technical field of folding screen testing, and discloses a reliability testing method and system for a folding screen mobile phone. The system comprises a test partition decision module, a dynamic test strategy module, an abnormal response analysis module and an adaptive enhancement module. The test partition decision module obtains physical structure parameters and historical deformation data of the folding screen and divides the folding screen into a screen center flexible area, a hinge stress concentration area and an edge packaging area; the dynamic test strategy module generates current and target test strength levels for each test partition; the abnormal response analysis module calculates the partition test deviation degree according to the target and actual test strength grade difference; the adaptive enhancement module generates a predicted test strength level for a next test period based on the test deviation and historical test data. The system can improve the test accuracy and efficiency, and meets the reliability test requirements of each region of the folding screen.

The invention relates to a distributed test data generation and sharing system based on privacy computing, and belongs to the technical field of big data, the system comprises a data generation module used for fragmenting acquired user data to a plurality of computing nodes for k-anonymization processing to obtain local data blocks output by each computing node; the privacy calculation module is used for extracting statistical characteristics of the local data blocks and generating an encrypted data packet based on the statistical characteristics; the data storage module is used for carrying out fragmentation storage on the encrypted data packet based on the data type of the encrypted data packet; the data sharing module is used for constructing an access strategy tree based on the encrypted data element information of the encrypted data packet; the access strategy tree is used for generating a decryption key containing the timestamp and the operation authority for the authorized user. According to the system provided by the invention, only the compliance access operation is allowed to trigger the data decryption process, data sharing is realized under the condition of ensuring that the user data is safe and not leaked, and the risk of data leakage in the data test is reduced.

A validation circuit is placed in vicinity of a critical path for testing the critical path. The validation circuit receives test data from the control circuit for testing the critical path. The test data is indicative of a delay value that is associated with the critical path. The validation circuit generates multiple setup signals and an enable signal to facilitate the testing of the critical path based on the test data. The validation circuit generates a first test signal based on the enable signal, and a second test signal based on the first test signal and the setup signals. The second test signal is a delayed version of the first test signal. The validation circuit compares the first test signal and the second test signal. A mismatch between the first test signal and the second test signal indicates deviation from the delay value.