229 results about "Test strategy" patented technology

The invention discloses a server component durability test system, method and device, a medium and a product, and relates to the technical field of computer hardware testing and reliability engineering.The server component durability test system comprises an intelligent control center used for sending an environment parameter control instruction to a test environment construction module, a load time sequence graph is sent to a load simulation module, so that the test environment construction module generates a composite test environment based on the environment parameter control instruction, and the load simulation module applies a network flow load and a calculation task load based on the load time sequence graph; and the data monitoring and analysis module generates a degradation evaluation result of the target server component through the collected data, so that the intelligent control center adjusts a test strategy based on the degradation evaluation result. Visibly, by adopting the technical scheme, the cooperative scheduling precision of the network flow load and the calculation task load can be improved, the test strategy can be dynamically adjusted, and the accuracy of the durability test of the server component is effectively improved.

The invention discloses an intelligent terminal function test method and system, and belongs to the technical field of intelligent terminal function test. The method comprises the steps that a dynamic demand tracking mechanism is constructed, UI elements are analyzed, and a topological graph marking resource occupation types and hierarchical dependence is generated; establishing a demand-use case-defect incidence matrix to realize automatic update of the use case state when the demand is changed; executing a test based on the topological graph and the matrix, traversing a path according to a preset strategy, collecting data and comparing an expected result; and optimizing iteration according to a test result, adjusting a node weight and a matrix mapping relation, and generating a new test path and a use case suggestion. The system comprises a dynamic demand tracking module, an incidence matrix management module, a test execution module, an optimization iteration module and a data storage module. According to the method, the test range is accurately adapted to the demand change, the test coverage and the defect tracing efficiency are improved, a test strategy closed-loop optimization mechanism is formed, and the test requirements of a complex system and an agile development mode are met.

The invention relates to the technical field of DC / DC power supplies, and discloses a DC / DC power supply function parameter testing method and system and computer equipment, and the method comprises the steps: injecting a preset disturbance signal into an input end of a tested DC / DC power supply through a signal conditioning circuit, and obtaining output voltage response data and output current response data; adopting a dynamic multi-parameter test system and collecting target test parameter data; calculating a characteristic parameter set, and executing voltage-temperature control model predictive control analysis to obtain a control parameter sequence; and controlling the power output value of the electric heating wire, the rotating speed value of the heat dissipation fan and the position value of the electric push rod in the test box body to obtain an intelligent adjustment test environment and generate an aging test result and a test strategy adjustment instruction, thereby overcoming the problem of parameter detection blind areas in a traditional test method, and improving the test accuracy even under the condition that the output voltage is accurately matched with the set value. And the power supply performance abnormity can be effectively identified, and the reliability and accuracy of the test are obviously improved.

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 relates to the technical field of vehicle software testing, and discloses a vehicle unit test code generation method and device, equipment, a medium and a product. The method comprises the steps of obtaining a tested code; the tested code and the intention prompt word are input into a large language model to be analyzed, a test intention is generated through the large language model, and the test intention is used for indicating the process needed for testing the tested code; inputting the test intention, the tested code and the template cue word into a large language model for analysis so as to generate a test strategy through the large language model, and recommending a code template and a parameter value required by the test from a template library; and integrating the recommended code templates according to the test strategy, and filling the parameter values into the code templates to obtain test codes. The AI illusion is reduced, and the accuracy of generating the test code is improved.

The invention relates to the field of network equipment test, discloses a test method and a test management system for intelligent PON gateway test, and solves the problems of insufficient test coverage, difficulty in equipment compatibility verification, low test efficiency, inaccurate test result analysis and the like in the existing intelligent PON gateway test process. High-quality and high-efficiency testing of the intelligent PON gateway is realized by establishing a testing historical database, dynamically generating a testing strategy, intelligently scheduling testing resources, accurately calculating a testing balance index and pointedly solving the problem of testing imbalance.

The invention relates to the technical field of railway traffic control and software quality detection, in particular to a railway signal safety system function performance testing method which comprises the following steps: S1, knowledge extraction: extracting multivariate entities and semantic relationships thereof from text data related to railway signal safety to form a triple set; s2, constructing and optimizing a graph, constructing a heterogeneous knowledge graph, and optimizing the heterogeneous knowledge graph through a graph neural network and an embedded learning method; s3, constructing an intelligent dialogue mechanism; s4, strategy generation and grade evaluation; s5, task scheduling and execution; s6, test data analysis; and S7, result generation and write-back. According to the method, knowledge graph construction, natural language understanding, man-machine collaborative interaction, automatic test strategy generation and graph self-evolution updating technologies are comprehensively applied, and the verification efficiency and result credibility of the special software for the railway signal safety system in the aspects of function integrity and performance stability are effectively improved.

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 provides an automatic management interaction method, system and device of a whole vehicle software version and a medium, and relates to the technical field of vehicle software engineering.According to the method, functions of version monitoring, change analysis, test management and version management are provided through an integrated main interface, and a user can check the software version state and tasks to be done in real time; and performing multi-dimensional version comparison and risk assessment to generate a detailed change and risk report. In addition, the method supports configuration of a test strategy, execution of an automatic test and generation of a test report and a defect positioning list, and the test efficiency and accuracy are effectively improved. And in the aspect of version management, the whole process operation from creation to release is realized, the timeline and historical report of the whole life cycle can be displayed, and the transparency and traceability of version control are ensured. On the whole, the method effectively improves the efficiency and reliability of whole vehicle software version management, simplifies the operation process, and enables the version control to be more efficient, more accurate and easier to manage.

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.

A data processing system implements after determining that a test in a target branch of a code repository starts failing, receiving a pull request (PR) onto the target branch and blocking the PR (invoking a build policy and a test policy); after determining that the failing test in the target branch is disabled, tracking the disabled test in a disabled test file; in response to a request to requeue the test policy, determining whether artifact in the build policy is identical with artifact in the test policy; when not identical, fetching all versions of the file within a time period, generating a union content of all versions of the file, and applying the requeued test policy using the union content, the time period being between a start time of the build policy and a start time of the requeued test policy; and unblocking the PR.

The invention provides a storage equipment testing method and device and electronic equipment, and can be applied to the technical field of computers. The method comprises the steps that an initial test script is executed, target configuration parameters and target test strategies of the storage device are obtained, the initial test script comprises a diagnosis script and a test strategy matching script, the target configuration parameters are obtained by executing the diagnosis script, and the test strategy matching script is used for selecting the target test strategies based on the target configuration parameters; according to the target configuration parameters and the expected use duration of the equipment, a target test period is determined, and the target test period is a test period obtained after an initial test period in the initial test script is compressed; and obtaining a test evaluation report by using test execution information obtained by testing the storage device based on the target test period and the target test strategy.

The invention relates to the field of interface testing, and particularly provides a cross-type interface testing method and system based on a console, a terminal and a medium, and the method comprises the steps: packaging a bottom layer function of an operating system through a dynamic link library; interface configuration information is analyzed from the interface registration request, and a unique ID is allocated to an interface and registered to a console; receiving an interface test request, selecting an execution mode according to a test strategy identifier carried in the test request, if the execution mode is a single-interface test, directly calling a target interface and introducing a test parameter to carry out an interface test, and if the execution mode is scripted, analyzing an interface calling sequence and a logic control rule in a test script to carry out the interface test; calling a corresponding adapter according to the interface type in the testing process; and generating a structured test report for display. According to the method, a lightweight and high-expansion cross-type interface test scheme is realized, and the test coverage rate and efficiency are improved through dynamic interface adaptation, scripted process control and visual result analysis.

The invention discloses an MQTT protocol fuzzy testing method and system based on a large language model guiding sequence generative adversarial network, relates to the field of fuzzy testing, effectively combines the advantages of the large language model and the sequence generative adversarial network, and analyzes basic training data by introducing an intelligent analysis technology of the large language model. And the quality of the training sample of the sequence generative adversarial network is obviously improved. According to the method, the initial seeds can be enriched through the large language model, the discriminator can be assisted to perform multi-dimensional evaluation on the data generated by the generator, and the authenticity and diversity of the test seeds are enhanced. The generation of redundant test cases is greatly reduced, so that the fuzzy test process is more efficient. Meanwhile, a seed elimination technology based on an energy value is designed, it is further ensured that the tested seeds have higher quality, and the efficiency of vulnerability discovery is effectively improved. Meanwhile, according to the maximum coverage length, the test strategy is dynamically adjusted, continuous optimization of the test process is achieved, and a solid guarantee is provided for the test. According to the method, multi-level variation is carried out on the test seeds by utilizing a large language model, vulnerability information existing in the test seeds is quickly positioned, and meanwhile, multi-dimensional and deeper mutation is carried out on the test seeds, so that more existing vulnerability information is explored, namely, the multi-level mutation is carried out on the test seeds; the test seeds which successfully cause the vulnerabilities are verified and optimized by means of the response of the test target, the success and failure positions are summarized, and a more reliable guarantee is provided for subsequent variation.

The invention discloses a resource allocation method and device for a server, and relates to the technical field of cloud computing resource scheduling, and the method comprises the steps: building a cloud computing basic environment, and carrying out the enhanced deployment of the cloud computing basic environment, so as to build a cloud computing enhanced environment; and secondly, algorithm verification and full-link verification enhancement operation are performed on the cloud computing enhanced environment to dynamically deploy server resources of the cloud computing environment, so that the technical problems of low software testing efficiency, high time cost and labor cost, large error and low precision due to excessive dependence on manual intervention in related technologies are solved, and the method and the device for testing the server resources of the cloud computing environment are provided for the server resources of the cloud computing environment. The technical effects that the anomaly discovery time and the fault average repair time are greatly shortened by utilizing the AI prediction model, the limitation of a single tool in a high-concurrency complex scene is solved by adopting a labor division collaborative test strategy of step pressure and behavior simulation, and the resource utilization rate and the anomaly detection coverage rate are improved are achieved.

The invention discloses an equipment pressure testing method, equipment, a storage medium and a program product, and relates to the technical field of pressure testing, and the method comprises the steps: constructing pressure testing strategies of a first system and a second system in to-be-tested equipment, and executing corresponding pressure testing operation; collecting first test data of the first system and second test data of the second system, and extracting first feature data of the first test data and second feature data of the second test data; generating a target feature based on the first feature data and the second feature data; and generating a diagnosis result based on the target feature by using a diagnosis model constructed based on an expert system and a machine learning model, and generating a diagnosis report based on the target report template. According to the method, the pressure test data of each operating system can be collected, the features are extracted and fused, the model fusing the expert system and machine learning is used for correlation analysis and automatic diagnosis, automatic and accurate positioning of the performance bottleneck is realized, and the diagnosis comprehensiveness and accuracy are improved.

The invention relates to the technical field of computer storage, and discloses a test strategy for improving the read-write performance stability of an SSD (Solid State Disk), and the strategy comprises the following steps: obtaining the current available capacity of an SLC cache of the SSD; based on the obtained current available capacity, setting a total data volume used by a performance stability test, so that the total data volume is smaller than the current available capacity; and executing the performance stability test on the solid state disk by adopting the set total data volume so as to obtain performance data capable of reflecting the hardware stability of the solid state disk. The real-time available capacity of the SLC cache is obtained through the standard interface before the test, and the test data volume is set by taking the real-time available capacity as the rigid basis. By means of the method, precise stripping and evaluation of the performance stability of the solid state disk hardware are achieved, and the test result can truly reflect performance fluctuation caused by hardware components such as a master control or flash memory particles.

The invention discloses an intelligent commanding and testing integrated system for hydropower station debugging, and relates to the technical field of hydropower station equipment debugging and operation testing. Based on the physical connection relation and debugging parameters of the water turbine and the speed regulator, a three-dimensional function sub-region model is constructed, a multi-test-mode working condition database is established, through the effect recognition module, the system can construct and judge key indexes in each test mode, and the test efficiency is improved. Whether the problems of resonance effect, guide vane hysteresis, water attack abnormity and disturbance deviation exist or not is automatically recognized, a corresponding early warning instruction is sent out, and a closed-loop link from'phenomenon recognition 'to'reason tracing' is achieved. According to the method, whether a single test is qualified or not can be judged, the collaborative imbalance risk coefficient in the ith test mode can be further calculated, the risk level between collaborative effects is evaluated according to the collaborative imbalance risk coefficient, and intelligent adjustment of a test strategy is supported.

The invention provides an API (Application Program Interface) intelligent testing method based on context awareness, which relates to the technical field of software testing and comprises the following steps of: constructing a system context corpus by loading structured data such as an API document and a parameter dependency table and unstructured data such as a business rule and a user manual; according to a natural language test target input by a user, a test plan including a calling sequence, parameter dependence and state change is generated in combination with a context, an API calling sequence of state perception is further generated, parameters are subjected to intelligent fuzzification processing, and a test load is formed. Executing the load, capturing response, analyzing and identifying vulnerabilities or abnormities, dynamically adjusting a test strategy based on a result, forming closed-loop feedback, iteratively executing until a termination condition is met, and finally outputting a test report.

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 relates to the technical field of software testing, in particular to a software development operation testing system. Comprising a front-end framework and a rear-end framework, the front-end framework further comprises an environment dynamic configuration module, a user behavior simulation module and a chaotic disturbance injection module, the environment dynamic configuration module is used for dynamic parameter adjustment and topology generation, and the user behavior simulation module is used for generating a behavior mode and an injection flow flood peak. And the chaotic disturbance injection module is used for providing a fault mode library and injecting real-time disturbance. According to the invention, a test environment highly consistent with a production environment is dynamically constructed through an intelligent algorithm, and test distortion caused by configuration deviation is eliminated; modeling is carried out based on real user behaviors and traffic data, complex operation scenes and burst loads are accurately simulated, and the authenticity of performance testing is improved. An adaptive optimization mechanism is introduced, a test strategy and resource configuration are dynamically adjusted, and the test efficiency and the coverage rate are remarkably improved.

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.

Illustrative embodiments include a method, an electronic device, and a computer program product for test case generation. The method includes, in response to a target function of a system being developed, determining updated knowledge, corresponding to the target function, in a knowledge base for the system. The method further includes identifying at least one test path from the updated knowledge in the knowledge base based on a predetermined test range for the target function. The method further includes extracting a test object from each of the at least one test path to generate a test case for the target function. By this method, ontology-based automatic generation of test cases can be realized. For example, whenever a product is iteratively updated, incremental knowledge is used as a test target, which can ensure comprehensive coverage of a test strategy without missing any test points, while also improving efficiency and case quality.

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 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 application relates to the technical field of folding screen testing, and discloses a reliability test 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 a self-adaptive reinforcement module. The test partition decision module acquires folding screen physical structure parameters and historical deformation data, and divides 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 intensity levels for each test partition; the abnormal response analysis module calculates a partition test deviation degree according to the difference between the target and actual test intensity levels; and the self-adaptive reinforcement module generates a predicted test intensity level for the next test cycle based on the test deviation degree and historical test data. The system can improve test accuracy and efficiency, and adapt to the reliability test requirements of various areas of the folding screen.

The invention discloses a system, method and device for improving the test coverage degree of vehicle machine software, and relates to the field of software testing, the system comprises a data acquisition module, a data preprocessing module, a test coverage degree analysis module, a test quality evaluation module and a feedback and optimization module; the data acquisition module is used for acquiring burying point data generated during testing and running of the vehicle machine software in real time; the data preprocessing module is used for cleaning, classifying and preprocessing the collected data; the test coverage analysis module is used for analyzing the preprocessed data, including statistical analysis of the data to obtain software test coverage data; the test quality evaluation module is used for performing visualization processing according to a result of statistical analysis on the data, identifying the degree of covering a functional area in software test operation, and generating an evaluation result of the coverage degree; and the feedback and optimization module is used for generating a report for optimizing the software test strategy according to the evaluation result of the coverage degree.