1839 results about "Automatic testing" patented technology

The invention relates to the technical field of software testing, and discloses an automatic test script generation method and system, a storage medium and electronic device.The method comprises the steps that test case information and tested environment information are received, and structured case representation is generated; enhancing the structured use case representation based on a preset knowledge base; generating a plurality of positioning candidates for the target control in the testing step, and establishing a priority relationship among the positioning candidates; generating an executable automatic test script; running the script, and when execution failure is detected, automatically adjusting a positioning strategy, a waiting strategy or a business process based on a failure type, and executing again; and feeding back the successfully executed script information to the knowledge base, and updating the content and priority of the knowledge base. Through organic combination of knowledge base enhancement, multi-candidate positioning and a self-healing mechanism, the technical problems that an existing automatic test script is high in maintenance cost, poor in stability and lack of domain knowledge are solved.

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

The invention provides a multi-step automatic testing method based on machine vision, and the method comprises the steps: capturing an original image sequence of a test scene through a camera, extracting the contour features of a test object in an image through an edge detection algorithm, and obtaining a preliminary positioning coordinate; if the dynamic position change trend exceeds a preset threshold value, adjusting an image enhancement parameter to suppress background noise, and obtaining an enhanced target image; feature matching is carried out through the enhanced target image, a robust identifier such as a texture mode is extracted, and an accurate three-dimensional position coordinate is obtained; according to the accurate three-dimensional position coordinates, calculating an execution deviation value of the current step, and judging whether the deviation value is within an allowable range or not; if the deviation value is within the allowable range, a control instruction sequence is generated and transmitted to the mechanical arm, and a step execution confirmation signal is obtained; comparing a confirmation signal with a next image sequence through the steps, updating positioning model parameters, and determining a continuous adjustment scheme of the whole test process.

The invention relates to the technical field of artificial intelligence testing, discloses a method for automatically extracting UI elements of a Web page based on an AI large model, realizes automatic extraction of positioning information of the UI elements of the Web page of an embedded product, directly provides the positioning information to a Web testing framework for use, and solves the problems of low UI element extraction efficiency and insufficient adaptability in a traditional scheme. Playwright is used for obtaining page screenshots and HTML DOM information, after intelligent cleaning, the page screenshots and the HTML DOM information are transmitted to an AI model to analyze page structures, interactive elements such as buttons and input boxes are automatically recognized, and an optimal selector is generated; furthermore, page navigation and multi-page element collection are realized by combining an AI-driven automatic tool, and finally, an operation sequence is organized through a GUI (Graphical User Interface), so that automatic testing is quickly completed. According to the method, the problems of low efficiency and poor adaptability of a traditional method are solved, the accuracy and maintenance efficiency of Web testing are remarkably improved, and the method is suitable for dynamic and complex modern Web pages.

The invention discloses an automatic UI self-healing test method and system based on multi-agent AI, and belongs to the technical field of automatic UI tests.The automatic UI self-healing test method comprises the steps that an original test script is semantized through a test reasoning interpretation class, and an expected intention is output; when the test fails, synchronously collecting multi-source data and extracting multi-modal features, constructing a fault propagation graph according to an expected intention and the multi-modal features, and performing fault propagation analysis by using a graph attention network to obtain a fault diagnosis result; according to a fault diagnosis result, a deep Q network, a greedy strategy and a Byzantine fault-tolerant algorithm are adopted to negotiate and decide a repair strategy through a distributed agent cooperation mechanism, and feedback is obtained; optimizing and dynamically adjusting the repair strategy and the test reasoning interpretation class through a meta-learning algorithm based on feedback; a test intention is deeply understood by constructing a test reasoning interpretation class, and fault diagnosis and automatic repair are cooperatively completed by adopting a distributed intelligent agent, so that the semantic understanding capability, the diagnosis accuracy and the repair success rate are improved.

The invention discloses an intelligent testing method and device for a circuit board, a program product and a medium, and relates to the technical field of circuit board automatic testing. The method comprises the following steps: acquiring response waveform data of a target circuit board; performing frequency domain decomposition processing on the response waveform data to obtain a frequency domain fingerprint spectrum; obtaining a modulation influence coefficient based on the frequency domain fingerprint spectrum; when any result exceeds a preset reasonable range, obtaining a reversible modulation result and an irreversible damage result based on the target frequency domain fingerprint spectrum corresponding to the target test item and each target modulation influence coefficient; for the reversible modulation result, obtaining a standard environment equivalent map based on the target modulation influence coefficient, and recalculating the result of the target test item based on the standard environment equivalent map; and for the irreversible damage result, extracting damage frequency domain characteristics from the target frequency domain fingerprint spectrum, and based on the damage frequency domain characteristics, obtaining a defect type of the target circuit board and corresponding defect position information. And the overall efficiency of circuit board testing is improved.

The invention discloses a POL power supply automatic test method based on modular design, and belongs to the technical field of power supply test. The method comprises the following steps: firstly, under the control of an upper computer, automatically reading POL power supply basic parameters including key characteristic parameters such as input / output voltage, rated current and frequency from a preset Excel table, and establishing a judgment reference for testing; then, instrument initialization is completed, communication connection with test equipment such as an oscilloscope, an electronic load and a direct-current power supply is established, and configuration of initialization function parameters is completed; then, executing a measurement task of each core parameter of the POL power supply to be tested, and in the test process, executing a parallel measurement management mechanism, namely configuring each instrument parameter and channel state in real time according to a specific test item, and triggering a retry or interrupt logic under the conditions of communication abnormality, data timeout and the like until the test recovers a running state; and finally, after the test is completed, performing Excel storage and interaction on the standardized test data and the waveform screenshot, and finally safely exiting. According to the POL power supply automatic test method based on the modular design, the whole test process is divided into a plurality of serial or parallel function processing units such as parameter reading, instrument initialization, test execution and test management, data storage and interaction and the like, and high efficiency and usability of a test system are achieved.

In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect / errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.

The embodiment of the invention discloses an automatic test script repairing method and related equipment, which are used for solving the problem of failure of an automatic test script locator caused by frequent change of a user interface and improving the stability and maintainability of a test script. The method comprises the steps that the execution process of a test script is monitored, and if a locator of a target element related to current target operation in the test script fails, failed page feature information of a current page is collected; generating at least one candidate locator through a pre-trained locator generation model based on the failed page feature information; and verifying the candidate positioners generated by the pre-trained positioner generation model, and taking the candidate positioners passing the verification as the repaired positioners and updating the repaired positioners to the test script.

The invention provides an AI-based automatic test case generation method and system, and relates to the technical field of software testing, and the method comprises the steps: converting an original demand document uploaded by a user into an image format document, and calling a multi-modal large model to recognize text features and non-text features in the image format document, integrating to form an analyzed demand document; dynamically cutting the analyzed demand document to obtain a plurality of logically coherent content blocks; calling a large language model to generate a corresponding test case title for each content block, and forming a test case title set; and inputting each test case title in the test case title set and the corresponding content block into the large language model to generate a complete test case corresponding to the original demand document. The method has the beneficial effects that through multi-modal content analysis and intelligent workflow arrangement, automatic analysis and understanding can be performed on image-text features of a demand document, a high-quality test case is automatically generated, and the test period is greatly shortened.

The invention discloses an automatic testing line of a positioner. The automatic testing line comprises a feeding machine, a discharging machine and a testing machine arranged between the feeding machine and the discharging machine. The feeding machine comprises a feeding bin device, a feeding conveying line arranged on one side of the feeding bin device and a feeding carrying device arranged between the feeding bin device and the feeding conveying line. The feeding carrying device comprises a middle transferring device and a feeding transferring device. The feeding and transferring device comprises a feeding mechanical arm, a first feeding base connected with the feeding mechanical arm, and a plurality of feeding and transferring modules installed on one side of the first feeding base. According to the invention, automatic transmission of the carriers carrying the positioners, automatic loading and unloading of the carriers and automatic detection of the positioners can be realized, the automation degree is high, the working efficiency is high, and the requirements of modern large-scale production are met.

The invention provides a testing method and device based on a large model, an intelligent agent, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, large models, automatic testing and the like. According to the specific implementation scheme, in response to a received description document for a to-be-tested application, a large model is called to generate a test case for testing the to-be-tested application based on the description document, and the description document indicates a to-be-tested function of the to-be-tested application; performing function test on the to-be-tested application by utilizing the test case to obtain test information; calling a large model to carry out test abnormity positioning analysis based on the description document, the test case and the test information, and determining an analysis result; under the condition that the analysis result represents that the test case is abnormal, the large model is called to repair the test case, and an updated test case is obtained.

The invention discloses an agent assisted testing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring demand information of a Web system to be tested; analyzing the demand information and generating a test case set; performing hierarchical labeling on the test case set to form a hierarchical case set containing the basic operation type case, the data verification type case and the complex logic type case; distributing the basic operation type use case to an execution module of the agent system, distributing the data verification type use case to an interface automation test module, and distributing the complex logic type use case to an artificial interaction terminal; and when the execution module or the interface automatic test module monitors that the case execution fails or the state is abnormal, an exception processing strategy is matched according to an exception type and an exception source, corresponding processing operation is executed, and feedback information used for optimizing the layered case set is generated. According to the method, the test tasks can be intelligently allocated and accurately executed, and automatic adaptation and optimization can be performed when an exception occurs, so that an efficient man-machine collaborative test is formed.

The invention discloses an on-line brightness uniformity automatic test method and system, and relates to the technical field of brightness test, and the method comprises the following steps: obtaining a stroboscopic signal of an illumination light source in a test environment, and extracting the stroboscopic characteristic frequency of the illumination light source through a frequency domain analysis algorithm, establishing a phase tracking baseline between the frequency of the illumination light source and a sampling clock of the optical detection equipment, and constructing a phase difference mapping matrix based on the baseline; and based on the phase difference mapping matrix, constructing a self-correction model of the sampling clock of the optical detection equipment. Through phase tracking and self-correction control, optical sampling and illumination frequency are synchronized for a long time, brightness fluctuation caused by stroboscopic aliasing is eliminated, and measurement stability and consistency are improved; and through frequency domain residual suppression and brightness index re-calibration, closed-loop control from sampling to output is realized, so that the detection result is higher in precision, the judgment is more reliable, and accurate support is provided for brightness detection and quality tracing of a production line.

The invention provides a dual-network collaborative event sequence automatic testing method and device and a storage medium. The method comprises the following steps: generating a trigger identifier associated with a first pulse as a time reference; slave station logs are collected on the secondary backboard bus side, and communication messages are collected on the primary communication network side; correcting time offset and time drift of the primary communication network and the secondary backplane bus to generate a primary event sequence and a secondary event sequence; performing consistency comparison on the primary event sequence and the secondary event sequence under a unified time base to obtain a consistency comparison result; calculating an interval between adjacent events, comparing the interval with an expected interval to obtain a time scale precision checking result, and calculating an event resolution result; and calculating the time delay between the request and the response and counting the pairing rate to obtain a redundant pairing statistical result. According to the invention, end-to-end automatic testing, unified time base alignment, dual-network consistency verification, redundant request, response quantification and millisecond-level event resolution can be realized.

The invention relates to the technical field of software development, in particular to a code change influence recognition and automatic test execution method and system.The method comprises the steps that multiple types of nodes are extracted from code data, relation edges among the nodes are constructed according to code dependency relations, weights are set, and a semantic map is obtained; when the code is changed, analyzing by using a static analysis strategy, a dynamic analysis strategy and a semantic understanding strategy to obtain a change entry node; calculating the affected probability of each node according to each entry node, the semantic map and a preset propagation threshold; and obtaining and executing a test case related to each influenced node to obtain a test result. It can be understood that according to the technical scheme, three analysis strategies are combined, the direct influence and indirect influence of code change are comprehensively captured, then the test cases can completely cover the influence nodes, and the test efficiency is high.

The invention provides a fault diagnosis method, diagnosis device and fault diagnosis equipment, and relates to the technical field of fault diagnosis, and the fault diagnosis method comprises the steps: obtaining test firmware corresponding to the model of a to-be-measured force plate in response to the model selection of the to-be-measured force plate, and displaying a test interface corresponding to the to-be-measured force plate; and in response to a detection operation for the to-be-measured force plate, running the test firmware, and determining the working state of the to-be-measured force plate. According to the method, efficient fault diagnosis is realized through the automatic test process and the user interaction interface, the efficiency and accuracy can be improved, and manual dependence is reduced.

The invention discloses a dynamic simulation test verification method and system for a pumped storage unit monitoring system, and the method comprises the steps: building a high-dimensional system state matrix of a pumped storage unit based on the physical parameters and structural features of a hydraulic, mechanical and electrical system of the unit, carrying out the dimension reduction of the high-dimensional system state matrix, and obtaining a dynamic simulation test result of the pumped storage unit; a dynamic simulation model of unit multi-physics field coupling is obtained; constructing a multi-dimensional parameter space based on the parameter sensitivity coefficient and the working condition boundary condition of the dynamic simulation model, and calculating optimal test point distribution through a high-dimensional point flow diameter algorithm to obtain a test scene library; based on the dynamic simulation model and the test scene library, obtaining a real-time monitoring signal sequence conforming to a monitoring system communication protocol; and based on the real-time monitoring signal sequence, signal acquisition, logic judgment and alarm control functions of the monitoring system are tested through an automatic test script, and a performance evaluation result of the monitoring system is obtained. The test comprehensiveness and effectiveness of the pumped storage unit monitoring system are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The embodiment of the invention relates to the technical field of information, and discloses an interface verification and test method and device based on a large model, a medium and a product, and the method comprises the steps: obtaining a to-be-tested interface document, analyzing the interface document through a first large model, constructing a test request parameter based on a request parameter definition, acquiring a real-time response message by calling a real interface; performing semantic comparison on a response example in the document structure information and the real-time response message by using the second large model to obtain a corrected interface document; comparing the parameter constraint condition in the corrected interface document with the verification logic in the business logic code snippet by using a third model to generate a logic verification passing result; and on the basis of the corrected interface document and a logic verification passing result, a Mock rule and an automatic test case script are generated by utilizing the fourth model, so that the maintenance cost and the false alarm rate of the automatic test script are reduced, and the automatic test efficiency is improved.

The invention discloses an intelligent and automatic test case generation method based on a large language model, and relates to the technical field of large language model application, and the method comprises the following steps: collecting a software demand description file and an interface standardization file of a project, carrying out semantic analysis and mapping, generating a logic constraint set, and establishing a path mapping table; inputting the path mapping table into a large language model, generating a test scene and assertion, and executing semantic drift detection; when semantic drift is detected, a drift report is generated, a reverse correction process is executed, a patch prompt is generated, and the generation chain is executed again; and after the semantic distance of the prompt chain is detected to reach a convergence state, outputting a stable test case set. According to the method, the generation structure is controlled through the deterministic prompt sequence, so that the stability and controllability of test scene and assertion generation are realized; and through semantic drift detection and a reverse correction mechanism, real-time correction of semantic offset of the prompt chain is realized, and the accuracy and stability of the test case are improved.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

The invention provides a spacecraft thermal test thermal response automatic test method and system. The method comprises the steps that S1, preset configuration information is acquired, and a resistance interpretation test is carried out on a current power output loop; s2, initial test current is applied to the power output loop passing the test, and the temperatures of all test points are obtained regularly; and S3, judging whether the temperature change of the measuring point meets the response condition or not, if so, judging that the power output loop is matched with the measuring point meeting the response condition, and testing the next power output loop, and if not, returning to the step S2 and increasing the test current step by step until the measuring point meeting the matching condition exists. The testing process is high in safety, it is initially determined that the configuration of the power output loop is not abnormal, then the matching performance of the temperature measuring point and the power output loop is determined step by step in a stepped power-up mode, and the problems that due to the fact that the current applied to the power output loop is too large for a long time, a heating piece is burnt or the temperature of a product is abnormal are completely eradicated.

The invention discloses a method and a system for automatically testing electrical parameters of a fan, relates to a technology for testing the electrical parameters of the fan, and aims to automatically test the electrical parameters of the fan by automatically acquiring identification information, dynamically constructing a performance parameter mapping model and executing a self-adaptive test cycle. The technical problems of low efficiency, incomplete data coverage, significant personal error and poor model adaptability in the traditional test are effectively solved, the efficiency and accuracy of the automatic test of the electrical parameters of the fan can be improved, the data comprehensiveness is enhanced, and the model adaptability is improved.

The invention discloses a multi-parameter high-precision automatic test method and system for a voltage reference source, and the method comprises the following steps: selecting a test scheme according to the characteristics of a tested voltage reference source device, and carrying out the testing of starting time, initial precision, temperature coefficient, linear regulation rate and long-term stability; the automatic test system controls the adjustable DC voltage source to adjust the input voltage of the tested voltage reference source device according to the selected test scheme, and controls the high and low temperature test box to adjust the working environment temperature of the tested voltage reference source device; the automatic test system reads voltage data collected by the eight-bit and half-digit meter, and analyzes, processes, classifies and stores the voltage data according to a test scheme. The method is used for overcoming the defects of insufficient precision, poor synchronism and tedious process in the traditional test, and provides a reliable technical means for research, development, verification and mass production screening of the high-precision voltage reference source device.

The invention provides a verification code verification method and device, equipment, a medium and a program product, and aims to solve the problem that an automatic script in the related technology cannot realize automatic testing of verification of a verification code of a rotary sliding block. The method comprises the steps of performing separation processing on a rotating main body and a target pattern in a verification code image to obtain a main body binary mask corresponding to the rotating main body and a target binary mask corresponding to the target pattern; based on the main body binary mask and the target binary mask, a target angle difference between the rotating main body and the target pattern is calculated, and the target angle difference is used for indicating the rotating main body to rotate to a rotating angle matched with the target pattern; generating a rotation track based on the target angle difference by using a slow motion function, wherein the rotation track comprises random disturbance and a random pause point; and controlling the rotating main body to rotate based on the rotating track to obtain a verification result. According to the invention, automatic testing of verification of the rotary sliding block verification code can be realized.

Disclosed in the present invention are a feeder automation test method and apparatus, a device, and a storage medium. The method comprises: calculating a voltage effective value and a current effective value of each node in a distribution network framework under different fault handling section conditions of a feeder automation system; sending the voltage effective values and the current effective values to a corresponding tester, such that after receiving the voltage effective values and the current effective values, the tester converts the voltage effective values into an analog voltage waveform, converts the current effective values into an analog current waveform, outputs the analog voltage waveform and the analog current waveform to a corresponding field power distribution terminal, and returns switch position change information of the field power distribution terminal; and comparing the switch position change information with preset standard feeder automation action logic in a corresponding fault handling section condition and then, on the basis of a comparison result, obtaining a feeder automation test result. The present invention can improve the reliability of feeder automation tests.

The invention provides a carrier rocket ground test system and method supporting visual process configuration. The system mainly comprises: (1) a visual process configuration module, which configures a test process file through a graphical interface; (2) a process analysis and scheduling module for analyzing a process file and converting the process file into a task sequence; the test execution engine module (3) is used for driving bottom hardware and executing a test instruction sequence; (4) the test resource library module is used for storing and managing various information of test elements and various information of test equipment; the execution monitoring and data management module (5) is used for displaying a test execution state in real time and recording whole-process data; according to the method, the test logic and the execution program are separated, and a user can quickly construct, modify and reuse an automatic test process in a graphical mode, so that the test flexibility is greatly improved, and the test adaptability and efficiency are remarkably improved.

The invention discloses a self-adaptive software test case generation optimization method, which comprises the following steps of: carrying out structured analysis on service requirements of a target system, extracting multi-dimensional test requirements of performance, resources, risks and coverage rates, and carrying out tagging and normalization processing to form a quantifiable index system; based on a meta-strategy layer decision engine, fusion reinforcement learning and fuzzy reasoning are carried out to realize multi-target weight distribution and dynamic compromise; generating and optimizing a preferred case set by means of a trade-off adaptive algorithm, performing conflict marking and constraint cooperative adjustment, collecting feedback data through automatic testing, correcting weight distribution and conflict detection rules according to actual results, and continuously iterating case generation strategies; the objective achievement degree is periodically evaluated, the strategy model is reconstructed or switched to adapt to scene changes, the accuracy of test requirement expression and the self-adaptability of the use case generation strategy are improved, and efficient collaborative optimization of multi-objective testing and dynamic adaptation of service scenes are achieved.

The invention relates to the technical field of computer software testing, in particular to an AI-based webpage background automatic testing method and system.The method comprises the steps that a target webpage address, a login account and test target description input by a user are obtained, a decryption password corresponding to the login account is read from an encryption database, and meanwhile a browser driver is initialized; accessing a target webpage, adjusting a view to ensure that the webpage is completely displayed, monitoring a loading state, and retrying or reporting an error if loading is overtime; detecting a webpage login area, automatically filling an account password, and detecting a login state; matching functional navigation according to the test target description, if the functional navigation is not found, rolling a navigation view to unfold hidden items, and skipping to a target page after a target menu is positioned; according to the method, the target page data is collected, the target page data is analyzed through the multi-mode AI model in combination with the test target description, and the test report is generated. The method does not need scripts, adapts to page changes, is high in flexibility, and reduces the test cost and improves the test efficiency through AI visual judgment.