287 results about "Test path" patented technology

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 a PCBA automatic testing system and method, and relates to the technical field of PCBA automatic testing, and the system comprises a multi-source data collection module which is used for collecting chip dynamic power consumption waveform, PCB interlayer temperature gradient, and multi-source data of signal loop impedance change, and eliminating the sampling delay error of a sensor through a synchronous collection time sequence alignment algorithm; transmitting the acquired multi-source data to a dynamic modeling engine; according to the invention, by dynamically fusing the multi-physical-quantity data and adaptively generating the test path, the system can detect the fault of the PCBA more comprehensively and accurately, the detection efficiency is improved, and the false detection rate is reduced; the thermal aging and faults of the PCB are predicted by using a dynamic modeling engine, so that potential failure risks can be found in advance, and a powerful guarantee is provided for quality control of the PCB; and the test path is automatically generated according to the dynamic modeling result and the data of the fault knowledge graph, so that the workload and error of manually planning the test path are reduced.

The invention provides a charging pile testing method and device, a new energy automobile and electronic equipment, and the method comprises the steps: constructing a corresponding CAPL script according to the testing demands of a charging pile; performing version identification on the CAPL script to obtain a DBC file; defining message logic of the DBC file; dynamically configuring a protocol state machine corresponding to the DBC file after the message logic is defined; and after the dynamic configuration is completed, switching the configuration according to a test requirement, and generating a test report. The method can be applied to charging protocols of multiple versions, is higher in adaptability, improves the fault detection intensity, can be compatible with various different test scenes and test requirements, can dynamically adjust the test path according to the test requirements, and is higher in flexibility and higher in practicability.

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

The invention relates to the technical field of communication testing, and provides a multi-mode communication equipment test case generation method and device, equipment and a medium, and the method comprises the steps: obtaining a radio frequency parameter, network flow data and equipment log data of communication equipment, carrying out feature extraction and vectorization processing on the radio frequency parameters, the network flow data and the equipment log data to obtain a multi-modal vector; constructing a communication equipment calling relation graph according to the calling relation and the interaction protocol; constructing a test path evolution model based on a graph neural network, and transmitting the multi-modal vector to the test path evolution model, so as to search an optimal test path in a communication equipment calling relation graph through an attention mechanism; constructing a multi-target reinforcement learning reward function and dynamically adjusting the optimal test path through the multi-target reinforcement learning reward function; and according to the optimal test path, generating a target test case, and according to a test result of the target test case, adjusting model parameters of the test path evolution model, and carrying out multi-target reinforcement learning on the reward function.

The invention discloses a PCB double-sided four-flying-probe test path optimization method, and belongs to the technical field of path optimization, and the method comprises the following steps: importing double-sided PCB data containing a test network and test points; obtaining an initial matching scheme of a network internal test point corresponding to each test network through the matching strategy, and optimizing each initial matching scheme to obtain a network internal test point path; constructing an initial test sequence between test networks through a recent insertion heuristic algorithm, and then introducing a local search algorithm into an adaptive large-scale neighborhood search algorithm to carry out iterative optimization on the initial test sequence to obtain a test sequence between the networks; and combining the test point paths in the networks and the test sequence between the networks, and outputting a double-sided four-flying-probe test path. According to the PCB double-sided four-flying-probe test path optimization method, the problem that the overall moving cost is difficult to further reduce in an existing method is solved.

The invention discloses an on-line monitoring and early warning method for a bus capacitor of an electric driving system, and belongs to the field of electric driving systems. The electric driving system sequentially comprises an isolating circuit, a full-wave bridge rectifier, a bus capacitor bank, an inverter, an actuator and a capacitance online testing device; the capacitor online testing device comprises a relay, a load resistor, a voltage acquisition module, a current acquisition module and a master control calculation unit. When the electric drive system is idle, a load resistor is connected to construct a test path, and current and voltage signals are collected; disconnecting a test path, constructing a degradation characterization quantity of multi-index information fusion through time-frequency domain feature extraction, and constructing an equal-length time interval sequence degradation curve; and the TCN-BRU-GRU combined prediction model carries out trend feature learning and training on the degradation curve. According to the method, waveform fitting and local and global data feature capturing of non-equal-interval time sampling are achieved, trend prediction of bus capacitor performance degradation is achieved, early warning is conducted before failure of the bus capacitor, and safe and reliable operation of an electric drive system is guaranteed.

The invention provides a test case generation method and device based on natural language processing. The method comprises the following steps: preprocessing input data and generating a structured demand table; matching the rule base to determine compliance requirements; selecting the most similar test template through a semantic similarity algorithm; dynamically filling parameters to generate a test case and supplementing complete content; and outputting the structured test case and marking a risk level. Wherein the risk level is determined by comprehensively evaluating dimensions such as semantic similarity, risk matching degree, compliance matching degree and template use frequency through a weighted fusion algorithm. For high-risk test cases, test paths are expanded by creating and associating action test nodes. The test case is automatically generated through a natural language processing technology, a natural language based on efficiency and accuracy is remarkably improved, provided and generated, the test labor cost of processing is reduced, the test coverage range and the complex scene adaptability are enhanced through a multi-dimensional risk assessment and path extension mechanism, and the test efficiency is improved. Meanwhile, the test case is ensured to meet the requirements of regulations and standards.

The invention discloses a test method for multi-parameter synchronous excitation of a water electrolysis hydrogen production system, and belongs to the technical field of water electrolysis hydrogen production, and the test method comprises the following steps: obtaining key operation parameters of the water electrolysis hydrogen production system to construct a dynamic coupling response model, and generating a multi-parameter synchronous excitation signal sequence; the multi-parameter synchronous excitation signal sequence is applied to the water electrolysis hydrogen production system, system response data are synchronously collected, and a system response data set with a time sequence mark is generated; generating an adaptive test path planning instruction based on the system response data set with the time sequence mark and the dynamic coupling response model; and updating the multi-parameter synchronous excitation signal sequence according to the self-adaptive test path planning instruction, and executing an iterative test. According to the method, the dynamic coupling response model is constructed, multi-parameter synchronous excitation is generated and applied, and a closed-loop strategy based on response data adaptive iterative testing is adopted, so that the dynamic characteristics and the performance boundary of the system can be efficiently and accurately identified, and a comprehensive global performance map is constructed.

The invention provides an electronic component test system, method and device and a medium, and the method comprises the steps: applying a pulse excitation signal to an input end of a to-be-tested electronic component, and determining an equivalent parasitic parameter set of the to-be-tested electronic component; constructing a signal path simulation network when the to-be-tested electronic component is tested based on the equivalent parasitic parameter set in combination with a test path of the to-be-tested electronic component, and performing test response analysis on the initial test signal through the signal path simulation network to obtain a signal pre-compensation coefficient of the initial test signal in a transmission process; applying a spectrum shaping test signal determined based on the signal pre-compensation coefficient to the to-be-tested electronic component, and determining the waveform fidelity of the actual response and the expected response of the spectrum shaping test signal under the to-be-tested electronic component; and whether the to-be-tested electronic component passes the test is determined based on the waveform fidelity. According to the technical scheme provided by the invention, the to-be-tested electronic component can be effectively tested when the test signal generates distortion deviation.

The invention provides a test method and device for surface mounting production, equipment and a storage medium. The method comprises the steps of collecting image information and an electric signal after surface mounting of a printed circuit board; pre-processing the image information to generate a pre-processed image; performing ellipse fitting on the preprocessed image to determine geometric features of the bonding pad; performing cross-modal feature fusion on the electrical signal feature vectors corresponding to the electrical signals and the geometric features to construct a multi-dimensional defect description matrix; inputting the multi-dimensional defect description matrix into a neural network model, and outputting a defect type classification label and a corresponding confidence score; and generating a dynamic test path planning scheme through an optimization algorithm according to the defect type classification label and the confidence score. By extracting the geometric features of the bonding pad, constructing the defect description matrix fusing the electric signals and the geometric information, and performing defect identification through the neural network, deep collaboration and information complementation between modals are realized, and the identification capability of complex defects is effectively improved.

The invention relates to the technical field of probe testing, in particular to a PCB flying probe test path optimization method based on electrical characteristics, which comprises the following steps: calculating accessibility scores of test points according to the distribution of board surface test points and elements to determine risk types of the test points; determining a risk area grade corresponding to the grid unit according to the accessibility score in combination with the potential shadow area; optimizing the test path of the probe according to the risk area grade corresponding to the grid unit, and adjusting the initial moving speed of the test path of the probe in different grid units; for the same moving time sequence of different probe groups, determining overlapped grids according to the initial probe angles and the test paths corresponding to the probes, and determining whether there is a collision risk between the probes in combination with the initial moving speeds of the probes in different grid units; and when a collision risk exists between the probes, corresponding adjustment measures are taken according to the number of the overlapped grids. According to the invention, the test path is optimized in combination with multi-dimensional design based on the electrical characteristics and testability of the board.

The invention discloses an AI test case intelligent optimization verification method and system, and the method specifically comprises the steps: employing a causal discovery algorithm to analyze an implicit dependency relationship for an entity association structure in a test case knowledge graph, recognizing redundant test cases and invalid test paths, and obtaining a causal dependency network; on the basis of a causal dependence network, traversing uncovered entity relationship paths in the test case knowledge graph, and generating supplementary test case suggestions; dynamically adjusting analysis strategy parameters according to historical optimization data through a self-adaptive engine to obtain an optimization test case set; and based on the optimized test case set, generating an input parameter variation sample by using a fuzzy test engine, and expanding a test boundary. According to the method, the black box test efficiency can be remarkably improved, the test coverage rate and accuracy are improved, meanwhile, the workload of a test engineer is relieved, the user experience is improved, and an efficient and intelligent solution is provided for the field of software test.

The invention relates to the technical field of geotechnical engineering intelligence, in particular to a multi-field coupling intelligent triaxial test method and system combined with machine learning, and the method comprises the following steps: initializing a test device, and establishing a material parameter prior distribution function; defining a target test path and obtaining a target control sequence; acquiring real-time test data through a multi-source fusion sensing array, and preprocessing the real-time test data to obtain multi-dimensional feature fusion data; establishing an online parameter identification module to obtain real-time correction material parameters; establishing a heat-water-force multi-field coupling prediction model to obtain an optimal control sequence; acquiring latest test data, and performing rolling optimization on the model to establish a lightweight control model; and generating an intelligent control instruction to realize self-adaptive intelligent control of the triaxial test. According to the invention, through combination with machine learning, the spanning of the triaxial test from experience control to intelligent control is realized, and an intelligent technical means is provided for the research of the multi-field coupling characteristic of the geotechnical material.

The invention belongs to the technical field of software testing, is suitable for the medical field and the financial field, and discloses a service flow chart-based test path generation method, device and equipment and a medium, and the method comprises the steps: obtaining a service flow chart of a service scene, and extracting nodes of the service flow chart and path topology information between the nodes; on the basis of the service importance weight and the historical execution probability of each node, key test paths in the path topology information are identified, and priorities of the key test paths are sorted; constructing a path coverage rate prediction model based on the dynamically adjusted priority of the key path and the historical execution probability of the key path, and predicting the coverage rate of each key path in the business flow chart based on the constructed path coverage rate prediction model; and configuring a test case set based on the predicted coverage rate of each key path in the business flow chart, and dynamically adjusting the configured test case set based on a multi-objective optimization algorithm. According to the invention, the test efficiency of the business scene is improved.

The invention relates to the technical field of Web component testing, in particular to an automatic test case generation method, system and device for a Web component and a medium, and the method comprises the following steps: extracting a front-end frame type and code features to which the Web component belongs, constructing a visual state transition network, and constructing a unified data flow diagram; based on the code features, the visual state transition network and the unified data flow diagram, semantic reasoning is carried out by utilizing a large language model to generate a test path combination, and a minimum complete test case set is generated by applying a combination test algorithm; generating a composite assertion logic; frame type conversion is executed, and the minimum complete test case set and the composite assertion logic are converted into test codes capable of being executed by a front-end frame type; the test code is executed, and the minimum complete test case set and the composite assertion logic are updated and circulated based on Web component change or test failure in the execution process. According to the method and the device, the Web component automatic test which is high in accuracy, cross-frame adaptive and capable of realizing continuous self-healing can be realized.

The invention discloses a road test route design method and system for an intelligent connected automobile, and the method comprises the steps: determining a full path based on starting point coordinate information and terminal point coordinate information, and processing the full path in combination with user path selection preference and historical test path preference data to generate a plurality of test paths; acquiring historical operation parameters and current operation parameters of path points in the test paths, inputting the parameters into a preset safety evaluation model to output a safety evaluation result, comparing, determining credible path points, and determining candidate test paths from the plurality of test paths according to the credible path points; and selecting a final test path from the candidate test paths according to a preset selection rule. According to the method, pertinence of path design is improved by combining user path selection preference and historical test path preference data, accuracy of safety evaluation is ensured by combining historical and current operation parameters, and scientificity and optimality of final test path selection are realized through a multi-stage objective screening mechanism.

The invention relates to the technical field of software quality assurance, and discloses an internet-based software engineering automation test system, which comprises a test environment perception module, a test intelligent arrangement module and a test execution and optimization module, a server resource state, a network delay parameter and a code structure feature are collected in real time through an environment dynamic sensing module, a multi-dimensional environment adaptation degree evaluation model is constructed, and when it is detected that the environment adaptation degree is lower than a threshold value, container resource expansion and network strategy adjustment are automatically triggered; establishing a mapping relation between multi-level defect features and code modules through a defect root positioning system, searching an optimal diagnosis scheme in a strategy space by applying an improved particle swarm algorithm, and accurately positioning a fault point to a source code line number; finally, closed-loop control of test environment self-adaption, test path self-correction and defect positioning self-optimization is realized, and the accuracy of a test result and the fault repair efficiency are improved.

Testing a semiconductor can be time-consuming as the chip architecture becomes more complex. Testing the possible scenarios becomes increasingly difficult. Chip quality characteristics relating to the chips on a wafer can be used to estimate a probability or rating relating to bypassing system-level testing (SLT). A chip can bypass SLT if there is a high likelihood of passing SLT. Thousands of chip characteristics can be received from wafer testing, chip probe testing, environmental parameters, factory parameters, and other parameters. A chip quality model can use chip quality characteristics as input to generate chip group and SLT parameters. The chip quality model can be a machine learning model or other types of machine learning systems. The chip group parameter or the SLT parameter can be used to direct the testing path of a chip where some chips can bypass SLT thereby saving production time.

The invention belongs to the field of cross-application function automatic testing in mobile applications, and relates to a large model driven cross-application function testing method and system based on memory implantation. The method comprises the following steps: constructing a cross-application function knowledge base for storing and organizing complete information of historical cross-App tasks; searching a historical path most similar to the current test task from the cross-application function knowledge base, reconstructing the historical path into a dialogue memory format conforming to the natural interaction habit of the large language model, and implanting the large language model; and dynamically generating a test path matched with task semantics by utilizing a large language model according to an implanted dialogue memory format, and realizing test state monitoring and feedback correction in the whole process in combination with a local state sensing mechanism. According to the method, a cross-application function knowledge base is constructed, and a global + local memory implantation mechanism is designed, so that the model has a long-term memory ability and a real-time state sensing ability, and therefore, the model can stably run in a complex scene, and a test task can be efficiently completed.

The invention provides a test method and system for a storage chip, and relates to the technical field of storage chips, and the method comprises the steps: obtaining the physical parameter data of the storage chip; mapping each physical parameter data to a low-dimensional manifold space in a coupling manner by taking a storage unit as a unit; calculating the local curvature of each storage unit in the low-dimensional manifold space; the storage units are screened according to the size relation between the self-adaptive local curvature threshold value based on the average value and the standard deviation and the local curvature; in combination with a low-dimensional manifold space, a test path of a fault potential area is generated based on an optimal transmission theory; testing the memory chip once according to the test path; and verifying whether the fault potential area is completely tested or not by combining a homology group in algebraic topology, if so, outputting the tested fault storage unit, otherwise, generating a supplementary test path, and performing secondary test according to the supplementary test path. The test efficiency and the identification accuracy of the fault unit can be effectively improved.

According to the multi-step test case generation method and system based on state-behavior map modeling, the filled Prompt template is used for guiding the large language model to recognize the key state and behavior, the large language model is used for recognizing the key state and behavior planned by the to-be-tested application instead of directly generating the test case, and the test efficiency is improved. The problems of randomness and illusion existing in direct generation of the test case by using a large language model are solved; through a mode of constructing a state-behavior map and traversing the state-behavior map, various complex scenes can be systematically covered, and the test coverage breadth and depth are far better than those of an existing test method based on recording and playback; the multi-step test case can be reliably generated by matching each step in one or more groups of test path sequences generated by traversal with the corresponding implementation template to generate the test case script file.

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.

A specific logic circuitry of a semiconductor Integrated Circuit (IC) is measured by a sensor. The sensor includes: a signal splitter that splits a signal from the specific logic circuitry into two test paths; a delay element that receives and applies a delay to a first test path, the delay based on a predetermined timing margin that is selected from a group of discrete timing margin values comprising a shortest timing margin value and at least one longer timing margin value; a comparison circuit that compares the delayed first test path and a second test path and provides a measurement output according to the comparison for an instance of measuring; and a controller that sets the predetermined timing margin such that, over the instances of measuring, a frequency of selection of the shortest timing margin value is higher than a frequency of selection of each longer timing margin value.

The invention discloses a semantic perception kernel fuzzy test method and device based on a large language model, and the method achieves the structural and context-related semantic analysis of a system call sequence in a variation stage through the introduction of the large language model with the language understanding and code modeling capabilities. And a variation strategy with logic consistency and attack potential is dynamically generated, so that the effectiveness and diversity of fuzzy test input are greatly improved. Specifically, semantic modeling and structured understanding are performed on a system call sequence by using a large language model, a variation strategy instruction with context relevance and execution reasonability is generated, and fuzzy test path exploration is continuously optimized through a dynamic feedback mechanism. The defects of a traditional fuzzy test method in variation efficiency, coverage depth and complex state adaptation are effectively overcome, and a more intelligent, self-adaptive and efficient vulnerability detection and security guarantee solution is provided for an operating system kernel.

The invention relates to the technical field of software testing, in particular to an AI-based low-code intelligent test generation system which comprises a demand analysis module, a test path generation module, a test script conversion module, an execution environment management module and a result analysis module. A test path of the test path generation module is supplied to the test script conversion module and the result analysis module, a test script generated by the test script conversion module is sent to the execution environment management module, and an execution result of the execution environment management module is fed back to the result analysis module; and an optimization suggestion generated by the result analysis module is sent to the test path generation module and the test script conversion module. Through cooperation of multiple modules, full-process automation of low-code testing is achieved, testing paths and scripts are dynamically optimized, testing efficiency and coverage pertinence are improved, manual intervention and the error rate are reduced, and dynamic adaptation and execution stability of a testing scheme are ensured.

The invention provides a domain controller test case intelligent generation method and system based on a knowledge graph, belongs to the technical field of automobile electronic testing, and is used for solving the problems of insufficient coverage rate, low efficiency and difficulty in knowledge inheritance of domain controller testing in related technologies. According to the method, a test knowledge graph fusing a functional architecture, historical data and expert experience is constructed, vector representation of nodes and relationships is learned by using a graph neural network, an optimized test path is generated through intelligent reasoning, then an extended test case is generated through a multi-dimensional generalization technology, and finally continuous optimization is performed through closed-loop quality evaluation. The test coverage rate and the design efficiency can be remarkably improved, and systematic precipitation and reuse of test knowledge are achieved.

The invention provides a path-based test case priority sequence recommendation method and system, and relates to the technical field of software testing, and the method comprises the following steps: obtaining test execution information of a target test case based on new and old version programs; assigning each node position of a test coverage path in the target test case to generate a test path feature vector; based on an XGBoost algorithm, respectively taking the test path feature vector and the program execution result as input and output, and training a fault prediction model; based on the fault prediction model, determining a prediction fault probability of a basic coverage path in the new version program, and screening to obtain a fault coverage path; according to the method, the target association scores of the test coverage paths and the fault coverage paths in the target test cases are calculated based on the preset recommendation rule, the target test cases are arranged according to the target association scores from large to small, and the priority recommendation sequence is generated, so that the fault paths can be effectively covered, and the accuracy of priority ranking of the test cases can be improved.

The invention provides a wafer-level inertial device test path planning method and system, and relates to the technical field of inertial device detection.The method comprises the steps that whether a to-be-tested point position capable of being tested exists or not is judged by determining a test point position coordinate of an index path and the type of a multi-view probe; according to the type of the multi-view probe, judging whether defective pixels exist in a plurality of point position coordinates of a neighborhood of the point position to be detected or not; moving the to-be-detected point position, judging whether a plurality of point position coordinates in the neighborhood of the moved to-be-detected point position have dead pixels or not, skipping the moved to-be-detected point position if the dead pixels exist, and re-determining the to-be-detected point position of the index path; and if the dead pixel does not exist, sending a moving instruction to the probe station, connecting the multi-view probe with the point location to be tested and a plurality of point locations in the neighborhood, and performing driving end testing and detection end testing. According to the method, in the batch test process of the wafer-level inertial device, path planning can be carried out, dead pixels are avoided in real time, the test efficiency is improved, and the test cost is reduced.

The invention provides a test script generation method and device, electronic equipment, a medium and a program product, relates to application of a large model in a financial science and technology scene, and can be applied to the technical field of artificial intelligence, the technical field of big data and the field of financial science and technology. The method comprises the following steps: acquiring a pre-constructed multi-agent cluster; obtaining a target test demand by using a user interaction agent; performing semantic understanding on the target test requirement by using a semantic analysis agent to obtain semantic representation information; generating candidate test paths based on the semantic representation information and a preset interface structure knowledge graph by using an operation reasoning agent, and outputting a test path sequence based on the candidate test paths; and receiving the test path sequence by using a script generation agent, and constructing a test script based on the test path sequence and a script template to generate a target test script.