141 results about "Test strategy" patented technology

The invention relates to the technical field of network security, and discloses a multi-agent cooperative RESTful API vulnerability test method. Constructing a parameter structured representation by analyzing an API interface specification, generating a test value conforming to service semantics by using a large language model, and establishing a semantic request baseline representing normal service logic; constructing a multi-agent collaborative architecture including vulnerability reasoning, case generation, test execution, test analysis and test management; all the agents are coordinated to carry out iterative vulnerability testing through a signal mechanism and a state transition table, and API service analysis, test strategy customization, use case directional variation and execution, result study and judgment and strategy optimization are achieved; and finally generating a structured vulnerability report. According to the method, the problems of semantic blind areas, shallow analysis layers, insufficient adaptive capacity and the like are solved, security risks such as business logic vulnerabilities and sensitive data leakage in the RESTful API can be deeply detected, and the accuracy and coverage rate of vulnerability detection are remarkably improved.

The invention relates to the technical field of optical communication, and discloses an optical module adaptive test and rapid convergence method based on reinforcement learning, which can dynamically generate and execute an optimal test sequence for optical modules of different types or states through an off-line trained RL intelligent agent module, avoids redundant steps in a fixed process, and improves the test efficiency. On the premise of ensuring the test coverage, the test time is obviously shortened, and meanwhile, the detection probability of potential defects is improved; through the closed-loop test of the integrated RL intelligent agent module, the test action can be adjusted in real time according to the state of a single optical module, so that the test process can quickly adapt to the individual difference and process fluctuation of the device, the online quick convergence of the test strategy is realized, and the accuracy and stability of the test are guaranteed; and through online iterative optimization of the RL agent module, new data and new changes in the production process can be continuously learned, so that a test strategy is continuously evolved, and the optimal performance is kept for a long time.

The invention belongs to the technical field of automobile testing, and particularly discloses a digital twinning-based automobile instrument testing method, which comprises the following steps of: automatically generating a structured test case through a large language model, constructing a double-digital twinning architecture by relying on a high-fidelity rendering engine and a real-time simulation engine, and realizing synchronization of a virtual model and a physical instrument; multi-modal data are collected through a multi-sensor array, and defect intelligent analysis and grade judgment are completed in combination with a large language model; and test strategy self-optimization is realized through reinforcement learning based on historical test data. According to the invention, a double-engine architecture in which a high-fidelity rendering engine and a real-time simulation engine cooperatively work is adopted, and the requirements of large-scale scene and refined rendering are met at the same time. And seamless switching and data fusion between the double engines are realized through the scene transformation matrix, so that the consistency of a virtual environment and a physical entity is ensured. Meanwhile, a large language model is introduced into a test case generation and result analysis link, and natural language understanding level test script generation and anomaly analysis are achieved.

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

The invention relates to the technical field of software engineering and automatic testing, and discloses a self-adaptive multi-dimensional risk testing strategy design method and system and a storage medium. The method comprises the following steps: triggering a risk assessment service through a CI / CD assembly line, preprocessing original data obtained by a plurality of heterogeneous data sources, and generating a risk feature vector; outputting a multi-dimensional risk index by using the risk assessment model, fusing the multi-dimensional risk index and the dynamic weight thereof based on an adaptive weighted fusion model, and calculating a comprehensive risk score; dynamically updating a risk score threshold value used for dividing the risk level, automatically generating a test strategy matched with the current risk level according to a preset mapping rule of the risk level and the test strategy, and calling the test suite to automatically execute the test strategy; and collecting a test execution result and online operation data as training data, and regularly optimizing parameters of the risk assessment model. The test strategy can be dynamically adjusted according to the real-time risk situation.

The invention discloses an autonomous evolution type 5G protocol fuzzy test system based on a large language model, and the system comprises a protocol extraction unit which employs the large language model to analyze a 5G protocol specification document, and extracts structured protocol knowledge elements containing a message format, state machine logic and parameter constraints; the vulnerability analysis unit generates a potential vulnerability description list through big language model reasoning based on the knowledge elements; the test strategy generation unit drives a large language model to generate a targeted test case based on protocol knowledge, vulnerability description and historical test knowledge in the dynamic test knowledge base; the test execution unit executes the use case and captures a result; and feeding back an analysis result of the internalization unit, extracting a causal rule, generating new test knowledge and feeding back the new test knowledge to the knowledge base. According to the system disclosed by the invention, test targets and resources can be autonomously scheduled according to continuous updating of the knowledge base, so that the test process has an iterative optimization capability, and full-process automation and intelligentization of 5G protocol fuzzy testing are realized.

The invention relates to the technical field of mainboard function test, and discloses a PCB mainboard function test strategy dynamic optimization method based on deep reinforcement learning. Comprising the steps of initializing a simulation environment, and obtaining an initial state vector; the actor network of the agent outputs a current action vector based on the state vector; the simulation environment executes the current action vector, updates the test probability of each test item, and simulates the test process; calculating a state vector at the next moment, and calculating a current reward value; storing the experience tuple in an experience playback buffer area; sampling an experience tuple from the experience playback buffer area, and updating parameters of an actor network and a comment network of the intelligent agent by using a near-end strategy optimization algorithm; the above process is repeated, and the test strategy is converged to a multi-target equilibrium solution under the maximum cumulative reward target. According to the method, the problem that an existing strategy cannot respond to the dynamic change of the production line in real time is solved, and multi-target collaborative optimization of the test cost, the defect omission ratio and the production line beat time is achieved.

The invention discloses an automatic penetration test path planning method and system based on agent collaboration, and relates to the technical field of network security. Performing initialization starting on the plurality of intelligent agents based on the penetration test target range; collecting the target system and writing the target system into a shared blackboard mechanism; matching the asset feature information with a preset vulnerability knowledge base according to the test strategy mode to generate vulnerability utilization conditions, and constructing a global attack graph according to the asset feature information and the vulnerability utilization conditions; generating an attack path based on the global attack map, executing an attack operation on the target system according to an attack step sequence of the attack path, when an execution result is failure, updating the global attack map according to a feedback failure result, and re-planning to generate a new attack step sequence; and when the execution result is successful, generating a penetration test report for the penetration test target range according to the success result. By implementing the method, the actual demand of deep security assessment of the complex heterogeneous system is met.

The invention discloses a large model security test method and device and a computer program product, and the method comprises the steps: firstly, screening out a target starting node with the highest semantic similarity with a target malicious query text from a pre-constructed state diagram database, and then when the value of the highest similarity is higher than a preset threshold value, starting the target malicious query text; a path multiplexing mode is adopted, a complete test path corresponding to the target starting node is directly multiplexed to test the target large model, and when the value of the highest similarity is not higher than a preset threshold value, a path exploration mode is adopted to search the target large model; and a new test path is generated by using a test strategy library, a guide large model, an attack large model and the like for testing (and is stored for subsequent direct multiplexing), so that effective accumulation and intelligent multiplexing of historical experience are realized, and repeated exploration is avoided. Compared with existing testing methods such as a static template, the testing efficiency is continuously improved along with experience accumulation, a complete closed loop of self-evolution is formed, and an ideal testing effect is achieved.

The invention relates to the technical field of computers, and discloses a test case generation method and device, equipment and a medium, and the method comprises the steps: receiving a software test demand through a demand analysis agent, extracting a test point of the software test demand, and constructing a target knowledge graph according to the test point; generating an initial test case based on the test target knowledge graph through the case generation agent; executing the initial test case through the execution verification agent to obtain a test result; the test result comprises validity judgment and judgment basis of the initial test case; and in response to the test result, judging that the initial test case is an invalid test case, optimizing strategy parameters of each agent in the agent system based on the judgment basis through the evolutionary learning agent, and generating a new test case. The application can respond to the change of the test demand in real time, automatically adjust the test strategy, and improve the test quality and efficiency.

This invention discloses a microservice chaos testing method and apparatus based on Istio and distributed tracing, relating to the field of cloud-native technology. The method includes: Step S1, submitting a test task; Step S2, initializing the test environment; Step S3, tracing the microservice-level call chain of this request using the distributed tracing function, and generating a request tracing tree by combining the request definition and the microservice-level call chain; Step S4, implementing a bottom-up testing strategy: starting from the leaf node of the request tracing tree, implementing test cases from bottom to top. The execution of a single test case includes injecting candidate faults, initiating a specified request, and triggering the Envoy filter to obtain a preset response; Step S5, generating the final test result. The generation and execution of test cases in this invention are both automatically completed by the system, greatly reducing the complexity and labor intensity of manual operations, and achieving efficient tracing data collection and processing.

The application discloses a stability test method, device, equipment and storage medium of a DRAM particle. The method comprises the following steps: restarting a test system for a preset number of times, executing a preset test strategy in a universal boot loader stage of each test system starting process, the preset test strategy comprising the following steps: enabling a CPU cache, and configuring the CPU to access the DRAM particle in a non-burst transmission mode; based on hardware configuration information of the DRAM particle, allocating at least two test areas in an address space of the DRAM particle; traversing each working frequency supported by the test system, testing the read-write stability of the DRAM particle in direct read-write under each working frequency based on the at least two test areas, and testing the read-write stability of the DRAM particle after the test system enters a sleep mode and wakes up under each working frequency. The application has the technical effect of providing sufficient test pressure by using the CPU cache and avoiding external interference after the system is completely started, and effectively improves the comprehensiveness and accuracy of the DRAM particle stability test.

The application relates to a network security penetration testing method and device of a power system and computer equipment, and relates to the technical field of network security testing. The method comprises the following steps: determining the network security penetration testing strategy of each test intelligent agent based on the network security penetration testing instruction of the power system; performing the network security penetration testing on the power system through the execution of the network security penetration testing strategy of each test intelligent agent; obtaining the observation information of each test intelligent agent for the power system in the process of the network security penetration testing; the observation information is obtained through the observation of the running state of the power system by the test intelligent agent; and the network security penetration testing strategy corresponding to each test intelligent agent is dynamically updated based on the preset reward function and the observation information of each test intelligent agent, so that the updated network security penetration testing strategy corresponding to each test intelligent agent is obtained. The method can effectively evaluate the network security performance of the power system.

The invention provides a method for scheduling a test task, and relates to the technical field of computers, and the implementation scheme is as follows: obtaining a to-be-scheduled test task, a test data set and the state of computing power and bandwidth resources; based on the test task, the states of the test data set and the computing power and bandwidth resources, calculating an algorithm feature vector, a data set feature vector and a test strategy feature vector; constructing a ternary relation graph based on the algorithm feature vector, the data set feature vector and the test strategy feature vector; performing feasibility judgment and cost evaluation on each ternary relation graph; based on feasibility judgment and cost evaluation, selecting a target ternary relation graph to generate a task execution scheme; and according to the task execution scheme, binding the test task to the matched resource slot and data set for execution, so that algorithm features, data set features and test strategies can be brought into a decision together, coverage of key scenes can be completed preferentially, the resource utilization rate is improved, and queuing delay and data handling cost are reduced.

The invention provides an automatic test method and device for a low-cycle load shedding device. The method comprises the following steps: analyzing a configuration file of a to-be-tested device, extracting key parameter information, establishing station level communication with the to-be-tested device, reading a current constant value parameter on line, and automatically matching test configuration; generating a test strategy covering the whole scene according to a preset multi-dimensional test model; and finally, realizing automatic checking of the low-cycle load shedding logic by stoping an outlet signal of the device to be tested. According to the invention, through the modularized test architecture, the construction of a test scene covered by a full frequency band is realized, and the test efficiency is improved; a multi-round constant value closed-loop verification mechanism reduces manual intervention errors, ensures that a protection device cuts off corresponding loads according to a set low-cycle load shedding strategy when the power grid frequency is abnormal, and improves the stability of a power system.

This invention relates to the field of computer technology and provides an automatic chip testing method, system, computer equipment, and storage medium. The method includes the following steps: planning the movement trajectory of probes, optical fibers, or wafer stage according to the distribution of the chips to be tested; testing the chips one by one according to the set movement trajectory and a testing strategy, and outputting the test results; the testing strategy is: calling the corresponding test program to test the current chip under test, while simultaneously acquiring the image information of the next chip under test; identifying the acquired image information of the next chip under test and determining whether the model of the next chip under test is the same as the model of the chip currently being tested; until all chips under test have been tested. The beneficial effects of this invention are: it can realize mixed testing of different models of products, greatly improving testing efficiency and reducing costs.

The embodiment of the invention discloses a distributed force load loading control test system for an electric aircraft. The distributed force load loading control test system comprises a flight simulation subsystem, a loading control subsystem and a measurement and control subsystem, the flight simulation subsystem comprises a flight simulation module and a communication module; the flight simulation module is used for performing flight simulation operation and generating a force load loading test strategy, and the communication module is used for transmitting the force load loading test strategy to the loading control subsystem; the loading control subsystem comprises a loading computer, a servo controller, a servo driver and a servo electric cylinder; the loading computer is used for generating a control instruction according to a force load loading test strategy; the servo controller is used for loading control instruction transmission between the computer and the servo driver; the servo driver is used for driving the servo electric cylinder to operate, and the servo electric cylinder is used for generating a force load loading effect required by a force load loading test strategy on a target component of the electric aircraft through operation of the servo electric cylinder; the measurement and control subsystem is used for collecting and processing test data.

The invention belongs to the technical field of vehicle testing, and particularly relates to an intelligent analysis method, system and device for intelligent network connection vehicle test data and a medium, initial test data is received, and a test item identifier is printed based on a test event; generating a candidate test item option set according to a user test demand, and aggregating related data to form a target test item; generating a test strategy including test scenes, steps and evaluation indexes in combination with the knowledge base; analyzing data required by the strategy, and outputting an execution scheme; monitoring the environment and the data flow in real time during execution, and automatically repairing and recording when the environment and the data flow are abnormal; and if the test case fails, re-checking the associated exception record, and retrying after repairing if it is confirmed that the test case is caused by the exception. According to the invention, the intelligent and automatic formulation of the test scheme is realized, the reuse rate of historical test data is improved, and the reliability of test execution and the result accuracy are improved through environment monitoring and failure analysis.

The embodiment of the invention provides a page self-adaption testing method and device and electronic device.According to the method, current interface data of a to-be-tested dynamic page are monitored in real time, and under the condition that the current interface data are different from historical interface data, the current interface data are updated according to the interface updating content of the current interface data relative to the historical interface data; and automatically updating page content of the to-be-tested dynamic page and automatically updating execution logic and verification logic associated with the interface updating content in the test case, namely, automatically adjusting an execution path and a verification point in the test case based on the interface updating content to ensure that the test process is matched with the latest data and page state, so that the test efficiency is improved. And a dynamic self-adaptive test strategy is realized, the technical problem of poor adaptability when interface data and UI layout are frequently changed in related technologies is solved, and the test quality and the test efficiency are remarkably improved.

The invention discloses a complex system test strategy adaptive optimization method based on deep reinforcement learning, and particularly relates to the technical field of intelligent test and artificial intelligence. According to the method, historical abnormal events are modeled by constructing a risk density function, actual risk distribution and risk deviation are calculated in combination with a state trajectory, and dynamic feedback of risk information is realized; on the basis, constructing a test environment model and a reward function containing a risk weight, carrying out iterative training on the strategy network, and generating an optimized test strategy; further guiding the test process to extend to an abnormal area through a distribution offset detection method, and identifying a weak state transition path; continuously updating the risk density function according to a test result to form a closed-loop optimization mechanism; according to the method, the identification capability of low-probability high-risk paths in a complex system can be effectively improved, and the test coverage and efficiency are improved.

The invention discloses an interface testing method, system and device and electronic equipment. Relates to the field of software testing. The method comprises the steps that a test task is received through an agent decision engine, and the test task is used for requesting interface testing of a to-be-tested system; the test task is responded, parameters of a test environment are obtained, environment parameters are obtained, and the test environment comprises an environment used for conducting interface testing on the to-be-tested system; the environment parameters are input into a target model, a test strategy is obtained, the test strategy comprises a test case set and an execution sequence of multiple test cases in the test case set, and the type of the target model comprises a large language model; and performing interface testing on the to-be-tested system based on the testing strategy, and adjusting the testing strategy based on testing data generated in the interface testing process. The technical problem that the maintenance cost of a test script is high when the test script is adopted to automatically test the interface in the related technology is solved.

The invention discloses an end-to-end self-adaptive generation type artificial intelligence compliance auditing method and system based on a dynamic regulation atlas. The method comprises the steps of constructing a global regulation knowledge graph fusing multiple multi-source and multi-level laws and regulations, receiving business scene attribute description of tested generative artificial intelligence and generating adaptive scene test strategy configuration, executing adaptive compliance auditing testing in an isolation environment, generating a compliance auditing packet with law article traceability and tamper-proofing performance, and sending the compliance auditing packet to a server. And generating a feedback signal based on an audit result to drive continuous self-optimization of the system capability. The system comprises a regulation knowledge graph management layer module, a scenarized strategy configuration layer module, a compliance auditing execution layer module, an auditing evidence chain generation layer module and a closed loop feedback control layer module. According to the method, the compliance auditing efficiency can be improved, the consistency of auditing results is ensured, the auditing results have supervision-level proof effectiveness, and the method can be widely applied to compliance auditing and supervision inspection of generative artificial intelligence.

The invention relates to a test corpus acquisition method and device based on a function call sequence, computer equipment, a computer readable storage medium and a computer program product, relates to the field of software testing, and can improve the generation efficiency of a corpus database. The method comprises the steps that calling track information of function calling in the running process of a target program is obtained, and a function calling sequence is determined based on the calling track information; obtaining a target execution state of the target program, and determining a coverage rate identifier corresponding to the target execution state in the target program; and based on the function call sequence and the coverage rate identifier, performing a fuzz test to obtain a coverage rate test result, adjusting a test strategy for the current function call sequence according to the coverage rate test result, and performing the fuzz test again until the coverage rate test result meets a preset coverage rate condition, and adding the function call sequence to a test corpus corresponding to the target execution state.

The invention discloses a highway cut-in scene simulation test method based on risk grading pruning. The method comprises the following steps: extracting a vehicle cut-in scene fragment based on real vehicle driving data; extracting six-dimensional kinematics parameters according to each vehicle cut-in scene segment; generating an initial scene pool according to the six-dimensional parameters corresponding to the vehicle cut-in scene segments; inputting the six-dimensional parameters corresponding to the scene samples in the initial scene pool into a risk classifier, and calculating a risk value; dividing the scene samples into different grades according to the calculation result of the risk values; differentiated test strategies are adopted for the scenes of different grades, and the scenes needing to be tested are obtained; and inputting the scene needing to be tested into the automatic driving simulation platform for testing, and generating a test report. According to the highway cut-in scene simulation test method based on risk grading pruning, high-risk scenes can be automatically identified and preferentially tested, so that the test quantity is remarkably reduced, and the test efficiency is improved.

The embodiment of the invention provides an intelligent learning machine seating wake-up pressure test method and system, and the method can respond to a test instruction, generates a test plan, controls a mechanical arm to execute a seating simulation action according to the test plan, and synchronously collects test data. And obtaining an ideal data logic chain of a seating track mode in the test plan, thereby generating pressure test result information according to the ideal data logic chain and the test data. Wherein the pressure test result information comprises a seating wake-up result generated according to the screen state event and a key test event determined by comparing the test data with the ideal data logic chain. According to the method, a mechanical arm can be used for executing multiple pre-stored seating track modes to simulate the seating behavior of a user, and multi-dimensional test data are combined to perform seating wake-up test and root cause analysis, so that multi-modal data cooperative verification and adaptive test strategy adjustment are realized, logic defects in the wake-up process are accurately found, and the test efficiency is improved. And the accuracy of the test result and the test efficiency are improved.

The invention relates to a security protection software testing method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: hooking a Sleep function through a target process, wherein the target process is a process for executing an attack code; traversing a plurality of heap memories of the target process, encrypting the heap memories, and executing a Sleep function; and running to-be-tested security protection software to scan the heap memories of the target process so as to output a test result. By adopting the method, a test strategy of dynamic memory operation can be realized, and the detection capability of the security protection software can be tested.

This disclosure provides a code generation method, a code generation device, and related products. The code generation method includes: generating code to be tested using a first model based on prompt information; generating a test strategy corresponding to the code to be tested using a second model based on the prompt information and / or the code to be tested; testing the code to be tested based on the test strategy to obtain test results; wherein the test results are used to perform adversarial updates on the first and second models; and obtaining target code based on the test results and the code to be tested. This method can generate code with strong generalization ability, conforming to code rules, high execution efficiency, and robustness.

The invention provides a performance test method, device and equipment. The performance test method provided by the invention comprises the following steps: in response to a configuration operation of a user, generating a test model based on a gradient pressurization test strategy configured by the user, interface information and a logic control; carrying out gradient pressurization test on the tested object by adopting the test model, and monitoring test data in each test stage in the test process; taking the concurrency in each test stage and the test data in the test stage as a group of performance data to be stored; determining the optimal concurrency and the maximum concurrency according to all groups of performance data; and according to the optimal concurrency, or according to the optimal concurrency and the maximum concurrency, determining the stable throughput of the tested object during stable operation. According to the performance test method, device and equipment provided by the invention, the stable throughput of the tested object during stable operation can be accurately determined.

The invention provides an automatic interface testing method and system, electronic equipment and a program product, and the method comprises the steps: obtaining interface document data, carrying out the information extraction processing of the interface document data, and obtaining a structured data set; according to a preset cue word template, inputting the structured data set into a large model for case generation processing to obtain a test case set; screening the test case set through a preset test strategy rule to obtain a to-be-executed test case list; constructing an interface calling request according to the test case list, and carrying out automatic interface calling processing to obtain a calling result; and according to the calling result, carrying out automatic comparison and analysis processing on the expected response result in the test case set to obtain an automatic test result. The scheme can improve the automation degree of interface testing, and can be widely applied to the technical field of artificial intelligence.

This disclosure provides a resource scheduling method for model evaluation tasks, relating to the field of computer technology. The implementation scheme is as follows: acquiring risk data of the target model in the scenario to be evaluated, whereby the risk data indicates the degree of risk in the scenario to be evaluated; inputting the risk data into a preset model for risk classification to obtain the risk level corresponding to the scenario to be evaluated; matching the corresponding test strategy template from a preset strategy template library including one or more test strategy templates based on the risk level; constructing an evaluation dataset adapted to the risk level according to the dataset coverage rules in the test strategy template; and allocating computing resources to the model evaluation task according to the resource scheduling strategy in the test strategy template. This method can quantify risk and thus solve the problem of risk and resource mismatch in model evaluation tasks.