211 results about "System under test" patented technology

The invention provides a system performance testing method and system based on user behavior simulation, and relates to the technical field of system testing, and the method comprises the steps: obtaining service nodes of a tested system and service logic describing jump paths between the service nodes; generating a user behavior sequence for describing a sequence of accessing each service node by a user by combining a random walk algorithm with heuristic weight and random disturbance on the basis of service logic; generating a dynamic flow peak curve conforming to the natural behavior of the user by taking the maximum bearing flow of the tested system as a constraint; scheduling a user behavior sequence according to the dynamic flow peak curve to carry out pressure tests of multiple test types on the tested system; collecting performance data in the test process; abrupt data in the performance data are extracted through a sliding window; and outputting the performance data and the corresponding mutation data as a performance test result of the tested system. Accurate and efficient system testing can be carried out through data conforming to actual behaviors of a user under the condition that historical data does not exist.

The invention relates to the technical field of chip verification, in particular to a verification method of a low-power-consumption narrowband communication chip based on a UVM (Universal Verification Model), which comprises the following steps: firstly, constructing a verification platform based on a UVM framework; based on the verification platform and the test function points, the environment configuration module configures the reference model and the input component; then, the to-be-tested system starts to run, and corresponding data or interrupt response is generated; at the moment, a monitor in the verification platform is inserted into a monitoring node, and the running state and data output of the to-be-tested system are captured in real time; and then, comparing the captured data with a calculation result of the reference model, recording the result, reading an interrupt register or other indication registers of the system to be tested at the same time, and comparing the result with an expected result to judge the correctness. In this way, the technical problems that in the prior art, a chip verification method is insufficient in function coverage rate, lacks system-level random verification and is low in verification efficiency are solved.

The invention discloses a software test expected result prediction method and system based on deep learning, and the method comprises the steps: obtaining multi-source heterogeneous data of a tested system, and carrying out the preprocessing and correlation fusion based on the multi-source heterogeneous data, so as to determine a multi-modal data set; performing feature extraction on the multi-modal data set through a text encoder, a structured data encoder and a time sequence feature encoder to obtain a plurality of feature vectors, and performing fusion processing on the feature vectors to generate a feature vector set; performing deep learning reasoning on the feature vector set, and generating a multi-dimensional prediction result through multi-head self-attention mechanism and cross-modal attention fusion; and carrying out dynamic verification and conflict resolution processing on the multi-dimensional prediction result to generate a test expectation result conforming to system constraints and business logic. According to the method, the multi-modal features are automatically extracted and fused through the deep learning technology, the accurate test expected result is generated, manual intervention is reduced, and the software test efficiency and accuracy are improved.

The invention discloses a distributed testing method and system based on micro-service architecture. The method comprises the steps of generating a test task and a fault injection task according to a test plan; distributing each test case to a plurality of test agents according to the test task, so that the plurality of test agents respectively execute the corresponding test cases, and the plurality of test agents and the plurality of micro-service instances of the tested system run on a plurality of service nodes in a one-to-one manner; injecting a corresponding fault in an execution environment of the tested system according to the fault injection task; and collecting and summarizing log data related to the test, and outputting a test result and the influence of the fault on the test result according to the log data. According to the embodiment of the invention, a complete test process from test plan generation to test result analysis of the tested system is supported, high cooperation of fault injection and test execution is realized through accurate control of fault injection, and meanwhile, the distributed test system constructed based on the micro-service architecture can be adaptively expanded according to the scale of the tested system.

The invention relates to a pressure test anomaly detection method and device, computer equipment and a storage medium. The method comprises the following steps: performing a pressure test on a tested system to obtain pressure test data and monitoring index data of the tested system; inputting the pressure test data into a trained pressure test data prediction model, predicting the pressure test data of the tested system in a preset time period, and obtaining a pressure test data prediction result; based on a pre-constructed dependency relationship graph, determining a dependency relationship and an edge weight between the pressure test data prediction result and the monitoring index data; determining monitoring index prediction data according to the monitoring index data, the dependency relationship and the edge weight; and according to the monitoring index prediction data, predicting whether the detected system is abnormal in a preset time period, and obtaining an abnormal detection prediction result. The method is beneficial to improving the reliability of pressure test anomaly detection.

The invention is applicable to the technical field of interface testing, and provides an artificial intelligence-based interface testing optimization method, which comprises the following steps: constructing a multi-dimensional interface knowledge graph of a tested system, the multi-dimensional interface knowledge graph comprising but not limited to static structure information, business semantic information and running state feature information; based on the multi-dimensional interface knowledge graph, the driving state and the action space, deploying a test agent based on reinforcement learning; in the test agent, according to a preset multi-target reward function, executing a target-oriented exploration action, and generating a dynamic test case; according to the method, the twinborn test environment of the tested system is constructed for the test agent to perform exploratory test and generate the test case set, the value density of the test cases generated by the method is high, defects can be effectively found, the test cases are associated with interface service semantics and operation rules, and the reliability of the test cases is improved.

The invention provides a testing method, a server and a storage medium, and relates to the technical field of testing. The test method is applied to a server, a control module and a tested system module are deployed on the server, communication connection is established between the control module and the tested system module, the current operation environment of the server is a pre-silicon environment, and the test method can comprise the following steps: adopting the control module to test a target main program of a test tool through the target main program; test task information is sent to the tested system module, and the main program is used for shielding the peripheral interface and providing simulation test data; the tested system module is adopted to perform test operation based on the test task information to obtain a test result, and the test result is sent to the control module; and receiving a test result by using a control module. The control module deployed on the server based on the pre-silicon environment and the tested system model are tested, so that the dependence on an external control terminal is thoroughly eliminated, the server of the pre-silicon environment is independently operated and tested, and a result is obtained.

Techniques for assigning scenario-based tests to test assets are described. In an example, a scenario-based test operable to test a key performance indicator (KPI) of a System Under Test (SUT), a component behavior exhibited by a first component of the SUT, and a scenario characteristic are received. Based on the component behavior and the scenario characteristic, a first plurality of behavior models associated with the component behavior are identified. Based on the scenario characteristic a characteristic value is extracted from the scenario-based test. Each behavior model of the first plurality of behavior models is executed using the characteristic value to generate a first plurality of predicted behavior outcomes. Based on the first plurality of predicted behavior outcomes, a first test asset type from a plurality of test asset types is selected and the scenario-based test is transmitted to a test asset of the first test asset type.

A system for creating a digital twin of a system under test may comprise processing circuitry and memory, with instructions stored thereon which, when performed by the processing circuitry cause the processing circuitry to receive multiple inputs with information corresponding to the system-under-test and provide the multiple inputs as a data input to a large language model. The processing circuitry may further configure an experiment with the large language model based on the multiple inputs, configure a process flow for the experiment, using data output generated from the large language model, and generate a training data set using the experiment and the process flow. The training data set may be used by the processing circuitry to create or configure the digital twin.

The invention discloses a method and device for recommending a test case, and the method comprises the steps: obtaining a change function for code change in response to the monitored code change of a tested system; determining a to-be-recommended regression test case for testing code change according to the multi-dimensional information, the multi-dimensional information comprises a dependency relationship and a mapping relationship of a change function obtained in advance, function semantic information of the change function, function semantic information of a dependency function, a historical execution record of the regression test case, and any multiple items in importance attributes of the regression test case; the dependency function refers to a function having a dependency relationship with the change function, and the mapping relationship comprises a regression test case and a function which can be tested by the regression test case; and performing regression testing on the tested system according to the to-be-recommended regression test case.

The invention discloses a vulnerability analysis method and device, electronic equipment and a computer program product, and relates to the technical field of artificial intelligence and information security. The method comprises the following steps: acquiring system feature information of a tested system, wherein the system feature information at least comprises basic system information, code feature information and business logic information; performing information arrangement on the basic system information, the code feature information and the business logic information based on the cue word template to obtain a system description text; the system description text is input into a vulnerability analysis model for task decomposition, a to-be-tested task list is obtained, and the to-be-tested task list comprises at least one to-be-tested task and a vulnerability analysis scheme corresponding to each to-be-tested task; and sequentially executing the vulnerability analysis scheme corresponding to each to-be-tested task to obtain a vulnerability analysis result of the tested system. According to the method, the vulnerability analysis efficiency can be improved, the manual analysis cost is saved, and vulnerability analysis is automatically performed.

The invention belongs to the technical field of automatic driving test, and particularly relates to an automatic driving vehicle key test scene generation method based on a large language model. The method comprises the following steps: 1, converting accident video data into semantic text description by a general large model, and establishing an accident video text library; 2, performing parameter fine adjustment on the open source large model by using the accident video text library, so that the large model has key features for identifying accident data; 3, inputting the design operation domain element conditions of the tested system into the fine-tuned large model, and generating a scene type meeting the test requirements according to the characteristics of the tested system; and 4, according to the scene type and the scene key elements output by the large model, performing parameter combination on the key elements by using a combination test method to generate key scene parameters. The invention solves the problem that the existing scene generation method is difficult to comprehensively consider the natural driving accident data characteristics and the design operation domain constraint of the tested system.

The invention discloses a high-concurrency pressure test method based on elastic expansion and contraction and a related device. The method comprises the following steps: determining a test target; designing a test scene; configuring an elastic telescopic environment; under the designed test scene, a pressure test task is executed on the to-be-tested system, in the process of executing the pressure test task, performance indexes of the to-be-tested system are detected, resource configuration of the test scene is automatically adjusted according to the detected performance indexes of the to-be-tested system and a preset elastic scaling strategy, and a test result is obtained; the test result is compared with the test target, the high-concurrency pressure test of the to-be-tested system is completed, and the method and the related device can improve the accuracy and the reliability of the test.

The invention provides a distributed performance test method and system based on dynamic load prediction, and the method comprises the steps: periodically collecting the resource utilization data of a current slave node as the data of a current time point, and predicting the resource utilization data of a next time point; judging whether the resource utilization data at the next time point is abnormal or not based on a dynamic threshold verification algorithm, and starting a process migration mechanism based on a priority migration strategy; a bidirectional buried point probe is deployed at the slave node and the tested system, a density clustering algorithm is adopted to analyze and cluster the associated flow and resource data, and an abnormal problem mode is identified according to a clustering result; and displaying the relationship between the pressure request quantity of each test interface and the resource index as well as the abnormal problem mode in real time, and forming a full-link diagnosis view of pressure input-system response. According to the method, the response speed of the distributed performance test and the slave node resource scheduling efficiency are greatly improved, second-level time alignment is carried out on the flow data and the resource data, and bottom layer data support is provided for upstream evaluation analysis.

The present invention discloses a performance testing method and system for large amounts of industrial software big data. Among them, the performance testing method includes deploying an API request capture tool; simulating user access to the system under test, collecting API request information to generate a performance testing script; deploying a script management center tool for importing test cases, parsing the imported test cases, storing the parsed content in a use case maintenance table, and connecting to the database through a data testing script; deploying a test management center tool, including a message management center module for sending message tasks to the script management center, sending tasks for performance testing of large amounts of data; the performance testing script calls the data testing script, and the data testing script inserts test data into the database based on the status flag of the use case maintenance table, and the generated test data is used for performance testing of large amounts of data; for different business scenarios of industrial software, quickly create test data with large amounts of data.

Method for testing a system. A computer-implemented method for testing a system, in particular a computer program, a hardware system or an embedded system, is presented. Here, a first part of an execution structure for the system is examined using static analysis in terms of whether the system runs error-free when executing an execution path corresponding to the first part of the execution structure. If an error-free execution is determined in the static analysis for the first part of the execution structure, an examination of the system is carried out using dynamic analysis, which skips the execution path corresponding to the first part of the execution structure.

To provide a test precondition data derivation apparatus capable of easily deriving test precondition data corresponding to a test case.SOLUTION: Teacher data storage means stores teacher data used when generating, by machine learning, a model for deriving test precondition data that is either an SQL statement for registering data in a database of a system under test before execution of a test, or a file to be stored in a predetermined folder of the system under test before execution of the test, or both. Machine learning means generates a model for deriving the test precondition data by machine learning using the teacher data. Test precondition data derivation means derives test precondition data corresponding to a test case by applying the input test case to the model.SELECTED DRAWING: Figure 4

The present invention discloses a method for identifying a graphics rendering mechanism based on rendering pipeline alignment. The method pre-executes a benchmark test program in CPU mode and GPU mode respectively, completes pre-alignment of the key stage processing interval and the stable working interval in the two modes to form a first sampling interval to achieve alignment of the rendering pipeline. The second sampling interval is formed based on the pre-alignment of frame complexity. The target sampling interval is obtained by finding the intersection of the first sampling interval and the second sampling interval. During actual testing, CPU load data of each of the two modes is obtained, the CPU load data is sampled according to the target sampling interval, and the correlation between the two sampling data is calculated to determine the graphics rendering mechanism of the tested system. The unified rendering action ensures that the sampling data covers the complete rendering cycle. The reasonable distribution of data density under different load scenarios is ensured by analyzing the frame complexity. The combination of the two improves the representativeness and statistical effectiveness of the data, and effectively improves the accuracy of identifying the graphics rendering mechanism.

The application relates to a pipeline inter-module state transmission method and system, electronic equipment and a readable medium. The method comprises the following steps: an upstream test module obtains a running parameter through non-user interface interaction with a system under test, and writes the running parameter into an in-process shared storage area; a downstream test module detects a data ready state of the in-process shared storage area before execution; if the data ready state is ready, the downstream test module directly reads the running parameter from the in-process shared storage area; if the data ready state is not ready, the downstream test module obtains the running parameter by identifying user interface display information of the system under test; and the downstream test module executes a test task by using the obtained running parameter. The downstream test module can quickly obtain parameters from the shared storage area, and automatically switches to a user interface identification mode when the shared storage area is unavailable, thereby improving test efficiency and independent execution capability of the module.

The invention discloses a multi-target test selection method and system under a test uncertainty condition based on E domination guidance. The method comprises the following steps of: firstly, explicitly modeling uncertainty in a test process by using a detection probability matrix and a false alarm probability matrix; secondly, establishing a multi-objective optimization model which takes the minimum test cost, the minimum missing report rate and the minimum false alarm rate as objectives and takes the fault detection rate and the fault isolation rate as constraints; introducing an E domination relation fused with the preference of a decision maker to improve a traditional Pareto domination criterion; on the basis, designing an E-GPSO algorithm fusing a genetic algorithm and particle swarm optimization to perform iterative search, and dynamically maintaining a non-inferior solution set by adopting an external file based on an E domination criterion; and finally, outputting an optimal test scheme set meeting the constraint and the preference. According to the method, the test cost and the diagnosis reliability can be effectively coordinated and optimized, the decision space is remarkably reduced, and the efficiency and the quality of testability design are improved.

The invention discloses a test data generation method and device, a medium and a program product. The method comprises the following steps: configuring attribute information of a tested system database in a data processing platform through a database configuration component, and establishing data connection with the tested system database through a preset connection mode; inputting test demand information of the tested system database through the test demand input component, and sending the test demand information of the tested system database to the test data generation component; and in response to the test data generation instruction, based on the data connection with the tested system database, obtaining the service test data from the tested system database through the test data generation component according to the test demand information of the tested system database so as to generate the service test data. According to the embodiment of the invention, the preparation of the service test data can be realized without knowing the query logic and technical architecture of the internal interface of the system, so that the generation efficiency of the test data can be improved.

The invention discloses a GJB289A bus ICD missing-oriented test method. The GJB289A bus ICD missing test method comprises the following steps: acquiring messages on a GJB289A bus in a normal working mode of a tested system through a first GJB289A terminal module, and forming a recording file; extracting a non-broadcast message in the recorded file, converting the extracted message into a BC instruction, and sending the BC instruction to a GJB289A bus of the tested system; obtaining the address of the remote terminal from the instruction word converted from the current instruction according to the response condition of the remote terminal to the sent instruction; obtaining a simple network message of the remote terminal; extracting an instruction from the obtained simple network message of the specified remote terminal, and judging whether the GJB289A bus communication of the tested system is normal or not; and when the GJB289A bus communication of the tested system is normal, testing the state of the specified remote terminal according to the comparison condition of the current response and the response of the pre-recorded simple network message. According to the invention, the problem that the function of the tested product cannot be tested under the condition that the ICD of the GJB289A bus is lacked is well solved.

The embodiment of the invention provides a vehicle communication intelligent fuzzy test system and method and a related device, relates to the technical field of vehicle communication test, can accurately hit the security key of a vehicle protocol, can continuously optimize a model according to an actual test effect, and improves the security vulnerability discovery efficiency. The vehicle communication intelligent fuzzy test system comprises a reinforcement learning agent, a test execution unit and a response analysis feedback unit, and the reinforcement learning agent is used for generating a semantic test case according to a current protocol session state and historical test feedback; the test execution unit is used for issuing the semantic test case to the tested system through the adaptive interface; the response analysis feedback unit is used for extracting characteristics related to vehicle safety and protocol states from related response behaviors generated after the tested system receives the semantic test case, and standardized feedback signals are formed; and the reinforcement learning agent is also used for updating parameters of the reinforcement learning agent according to the feedback signal.

To automatically set an appropriate determination rule in a test case regardless of the number of combinations of attribute values.SOLUTION: The generation device includes a request extraction unit that extracts a request from a test case including the request to be transmitted to a system to be tested and a determination rule for determining pass or fail of a test, a test execution unit that executes a test on the system to be tested by transmitting the extracted request to the system to be tested and receives a response to the request, a determination rule extraction unit that extracts the determination rule from the test case, a test result determination unit that determines pass or fail of the executed test based on the request, the response, and the determination rule, and a new rule derivation unit that generates a new rule as a determination rule to be newly applied to the test case based on the request, the response, the determination rule, and the pass or fail of the test determined by the test result determination unit.SELECTED DRAWING: Figure 2

System testing is described. An example system testing method includes: acquiring module relationship information for a system under test, the system under test including a plurality of modules, the module relationship information indicating a logical relationship between each two of the plurality of modules; and testing at least two modules in the system under test based on the module relationship information, the at least two modules being associated with each other for the same function implemented by the system under test. By use of the technical solution of the present disclosure, both system and module perspectives can be applied to system integration testing, so as to achieve a more comprehensive system testing coverage and find problems in the system under test as soon as possible, which can help to improve the system testing coverage and system testing efficiency.

The application relates to a system testing method and device, computer equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: according to the function type of a system to be tested, dividing the system to be tested into at least one test service module; according to the importance of each test service module, determining the test mode of each test service module; according to the test flow acquisition strategy corresponding to the test mode of each test service module, determining at least one target test flow group corresponding to each test service module, and determining the test case link and flow group execution sequence of the at least one target test flow group; according to the flow group execution sequence of each target test flow group corresponding to each test service module, calling the test case link of the corresponding target test flow group to test the system to be tested. The method can improve the test efficiency, reduce the test cost, and provide guarantee for accurately, efficiently and comprehensively testing the test system.

This invention discloses a method for dynamic virtual testing and evaluation of power grids based on digital twins: a virtual power grid dynamic simulator is connected in series between the power grid signal sensors and the controller of the system under test; the host computer transmits the dynamic parameters of the power grid to be simulated to the simulator; the simulator modulates the parameters with the actual power grid signal to generate a power grid signal containing virtual disturbances, which is sent to the controller of the system under test to disturb the operation of the system under test; the simulator can also receive control signals from the system under test and generate control signals containing disturbances, which are sent to the controller of the system under test; simultaneously, the control and output responses under disturbance are collected and analyzed to evaluate the system's stability, controllability, or resilience. The dynamic parameters of the power grid are generated by a digital twin model of the actual power grid; the response of the system under test is fed back to the digital twin model, reflecting the interaction with the power grid after connection. This invention integrates digital twins and virtual simulation to achieve safe, efficient, and low-cost testing and evaluation of grid-connected equipment under dynamic and fault conditions of the power grid.

The invention discloses a rack testing device of a vehicle intelligent system and an acceleration safety evaluation method thereof, and belongs to the technical field of automobile safety, and the device comprises a vehicle testing cabinet, a wire control movable rack, a wire control steering rack, and a self-driving scene display system. According to the method, a tested system is iteratively fitted through a Gaussian process and a logistic function, and an agent model efficiently approaching the real tested system is generated to predict a security test result of the system; three acquisition functions are introduced to respectively explore the whole multi-dimensional continuous scene parameter space, namely, the safety performance of the system is represented by a small number of test scenes; accurately positioning a system security boundary; unsafe areas can be quickly identified, and dangerous scenes can be quickly found; and finally, giving the unsafe probability of the tested system in the test scene. The device and the method provided by the invention have relatively high test efficiency on the premise of ensuring the reliability and the accuracy of the vehicle intelligent system bench test.

The invention provides a test data generation method, a program product, electronic equipment and a storage medium. The method comprises the following steps: acquiring request data, aiming at a target function, of a user on a tested system and response data corresponding to the request data; the request data comprises a request field; the response data comprises a response field; comparing the request data with the response data, and determining an interface field mapping relation of the tested system; the interface field mapping relation is used for describing a corresponding relation between a response field of the tested system and a request field used for generating response data; obtaining configuration list data of the tested system, and converting the configuration list data of the tested system into test request data for the target function based on the interface field mapping relation; wherein the configuration list data comprises at least one piece of data expressed in the format of the response data. The process of independent development and data configuration for automatic testing is omitted, and the efficiency of automatic testing is improved.

The invention discloses a system-level test strategy generation method and system based on a finished product test strategy. The method comprises the following steps: importing a finished product model and a corresponding signal development-oriented test strategy into a test platform; forming a finished product set A based on model selection, and forming a finished product level test strategy set T based on strategy selection; and screening related finished products of the tested system, screening all source and sink end names which are names of finished products at the other end of the non-current finished product according to source and sink information in an electrical interface in a finished product model, and forming a set B which is equal to a set B1 + a set B2. And according to the set B1 and the set B2, dividing the finished product level test strategy set T into a set C1 and a set C2, and performing system level test strategy conversion processing on the set C1. According to the method, the problem that a scheme from a current finished-product-level test strategy to a system-level test strategy is missing is solved, the system-level test strategy can be formed by comprehensively and quickly utilizing the existing finished-product-level test strategy, and convenience is provided for integrated testing of a complex system.