5273 results about "Automatic testing" patented technology

The invention relates to the technical field of computers, provides an automatic test script dynamic generation method and system based on multi-modal AI recognition, and is used for remarkably improving the coverage dimension and execution robustness of test cases while reducing the manual maintenance cost. The method comprises the following steps: acquiring target test interface data, extracting dynamic interaction characteristics in the test interface data, calling a trained target detection network to carry out element positioning processing on the test interface data, generating an interface element coordinate set, and generating a target test interface based on the interface element coordinate set. Calling a character recognition network to carry out multi-modal feature analysis on the interface element distribution information, and generating a feature fusion result containing character semantic features and icon classification features; the feature fusion result is input into a script generation model for dynamic path planning processing, automatic test script elements are output, and the automatic test script elements comprise logic control instructions generated based on the element operation sequence and coordinate self-correction parameters.

An example operation may include one or more of receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device, generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts, attaching the automation script to the software test within the storage device, and in response to a request to execute the software test, executing the plurality of testing elements within the software test based on the attached automation script.

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The embodiment of the invention provides a cross-platform compatibility automatic testing method, which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, collecting chip platform information to construct a testing environment database, constructing a testing case feature model according to the instruction set features and the testing environment database, designing a test case generator based on the test case feature model, and generating a test task scheduling scheme based on the test execution framework; inputting the abnormal behavior characteristics and the performance baseline threshold value into a test result analyzer, constructing a scoring dimension containing functional integrity, a performance standard-reaching rate and a stability index, and mapping the abnormal behavior characteristics and the performance baseline threshold value to the scoring dimension to generate a test result scoring matrix, calculating a weighted average value of the test result scoring matrix and the optimized weight coefficient as a compatibility level, generating a test report based on the compatibility level, and executing regression verification; according to the invention, the comprehensiveness and accuracy of the test can be improved, and more efficient compatibility evaluation is realized.

The invention discloses a fault analysis method for vehicle-mounted automatic test equipment, and relates to the technical field of vehicle-mounted electronic equipment testing, and the method comprises the steps: collecting vehicle-mounted test data of the vehicle-mounted automatic test equipment, and storing the vehicle-mounted test data in a test database according to a time sequence; performing feature extraction on the test database through a deep learning model to generate a fault feature vector; inputting the fault feature vector into a fault diagnosis model, and generating a fault type judgment result and a fault probability value; and according to the fault type determination result and the fault probability values, screening corresponding fault recovery schemes from a preset fault recovery strategy library, and sorting the fault recovery schemes according to the fault probability values from high to low to form a fault processing report. According to the method, the fault processing report is generated based on the layered fault recovery strategy library and the priority grading mechanism, the technical problems that a traditional fault diagnosis method is insufficient in feature extraction and low in diagnosis precision are solved, and the accuracy and recovery efficiency of fault diagnosis are improved.

The invention provides a front-end automatic testing method of a large language model based on LLM, and belongs to the technical field of front-end automatic testing. The method comprises five core steps of test intention recognition and semantic analysis, page structure analysis and target element positioning, automatic generation of a test operation sequence and assertion logic, test script execution and dynamic regression verification, and test feedback analysis and continuous training. The strong semantic understanding and context reasoning capabilities of the LLM are utilized to perform semantic analysis and simulation on a front-end page structure, a user behavior process and business logic, so that automatic generation and maintenance of a test script are realized. According to the method, the test coverage rate and the generation efficiency are greatly improved, the maintenance cost is remarkably reduced, the readability and the intelligence of the test case are enhanced, and the method is suitable for the modern Web front-end application test of rapid iteration.

A method of evaluating performance of a chatbot includes identifying a test scenario including a request and an expected outcome, initiating an automated conversation between a first machine learning model and the chatbot based on the test scenario, storing a recording of the automated conversation, providing the recording of the automated conversation and the test scenario to a second machine learning model, and receiving an evaluation of the automated conversation from the second machine learning model based on the recording of the automated conversation and the expected outcome.

The invention discloses a hardware basic service component test system, which comprises a test management module, a hardware simulation module, an automatic test module, a data acquisition module, a data analysis module, a report generation module and a feedback adjustment module, the hardware simulation module is used for simulating hardware environments under different load conditions; the automatic test module is used for executing predetermined test tasks; the data acquisition module is used for monitoring hardware states and performance data in real time; the data analysis module is used for analyzing the acquired data; abnormal behaviors are identified and performance is evaluated, a report generation module generates a test report, and a feedback adjustment module dynamically adjusts test parameters and resource allocation according to an analysis result. The hardware basic service component testing efficiency, accuracy and comprehensiveness are improved, and the method can be applied to the technical field of hardware testing.

The invention relates to the technical field of communication, and discloses an optical fiber performance automatic test method, system and device based on optical power detection. The method comprises the following steps: carrying out intelligent scanning detection on an optical fiber network, generating optical fiber resource mapping data, obtaining an OTDR measurement configuration table through dynamic analysis, executing optical pulse injection and signal acquisition to obtain a time domain response characteristic curve, obtaining an optical fiber performance parameter set through wavelet denoising and peak value identification processing, and obtaining the optical fiber performance parameter set. And performing fault analysis by using deep learning and expert rules to obtain diagnosis decision data, and finally generating an optical fiber state evaluation report through digital twin modeling. According to the invention, remote automatic testing, intelligent fault diagnosis and visual monitoring management of the optical fiber network can be realized, and the operation and maintenance efficiency and reliability of the optical fiber communication network are improved.

The invention provides a system design method and system supporting automatic testing of multiple radio frequency parameters, and relates to the technical field of testing, and the method comprises the steps: receiving and analyzing a test task, generating a test configuration file according to the information of radio frequency equipment to be tested and multiple radio frequency parameter test items, and using a self-adaptive optimization algorithm to optimize the test configuration file. Dynamically adjusting the test configuration file based on historical test data to generate an optimized test configuration file; controlling a radio frequency signal generator to generate a test signal, inputting the test signal into the radio frequency equipment to be tested, starting a real-time monitoring module, performing multi-dimensional analysis on a digital signal processed by a radio frequency signal analyzer, performing anomaly detection on an analysis result by using a deep learning algorithm, identifying and marking an abnormal data point; and generating a test report according to the analysis result and the anomaly detection result.

The invention discloses a rapid calibration method and system for automatic testing equipment of a vehicle machine, and relates to the technical field of automatic calibration, and the method comprises the steps: inputting a preset standard calibration signal to the testing equipment, and collecting the response data of each testing channel of the equipment as calibration reference data; comparing the calibration reference data with a built-in standard reference value of the equipment, and calculating deviation data of each test channel; based on the deviation data, calibration parameters of each channel are calculated, and the calibration parameters comprise a gain compensation coefficient, a frequency compensation coefficient and a phase compensation coefficient; the calibration parameters are loaded to the corresponding channels of the test equipment, a preset standard calibration signal is input for verification test, and when errors of the channels are smaller than a preset residual error threshold value, calibration is completed, and the calibration parameters are stored. By establishing the gain-frequency-phase joint compensation model and combining the parallelization verification and increment optimization algorithm, the high-precision rapid calibration of the automatic test equipment of the vehicle machine is realized.

The invention discloses a test case generation method and device, equipment and a medium, and relates to the technical field of software testing, and the test case generation method comprises the following steps: storing request data subjected to structured processing into a seed case library; the request data are various types of request data in the operation process of the service system; determining a cue word according to the target information based on the large model, and generating an initial test case through the cue word and a generation rule; the target information comprises characteristics of request data in the seed case library, an interface document of the tested software and a business logic specification; executing the initial test case according to the test request of the tested software, performing statistical analysis on the code coverage rate of the tested software in the execution process, and screening the initial test case according to an analysis result to obtain a screened test case; and after the execution is finished, filtering the screened test cases according to the first execution result of each test case to obtain a target test case. Therefore, the testing efficiency and quality can be improved, and automatic testing is achieved.

The invention provides a test case generation and script optimization method and device based on machine learning, and the method comprises the steps: carrying out the training of a large model based on a Transform architecture through a public test case data set, calling the large model to generate a plurality of test cases meeting the business demands, recognizing the key operation steps in the test cases, and carrying out the script optimization of the test cases. Inserting check points at corresponding positions, converting the test script into an automatic test script described by a script language, and optimizing an execution sequence of the test script through a graph neural network; dynamically adjusting the structure of the test script through a deep reinforcement learning algorithm, generating a simulation test environment in combination with a GAN (Generative Adversarial Network), and verifying the test script. According to the method, the test case with high coverage rate and high quality can be automatically generated, the manual compiling cost is reduced, the automatic test script highly matched with the test case can be dynamically generated, and the execution efficiency is improved by optimizing the structure.

The invention discloses an automatic testing device and method for multi-directional tensile strength of textiles based on constant tension, and relates to the technical field of tensile strength detection.The automatic testing device comprises a rigid rack system, and a high-elastic fabric optical response mechanism is integrated on a double-stand-column portal frame of the rigid rack system; the fiber damage process is reflected through the light transmittance of light to the fabric and the light spot change; the high-elastic fabric optical response mechanism comprises a sealing light insulation assembly, a light source assembly and an imaging assembly. The light source assembly comprises pulse LED light source arrays arranged at equal intervals in the fabric stretching direction, a lampshade and a transverse movement driving unit. The problems that in the prior art, when the tensile strength of a fabric is detected, only the limit value when a sample is completely broken can be recorded, and microcracks generated by step-by-step failure of fibers in the stretching process cannot be captured are solved. The device has the advantages that when the tensile strength of the fabric is detected, the fiber fracture process in the fabric is directly reflected through the change of light spots formed by light penetrating through the fabric, and the like.

The invention discloses an electrostatic discharge immunity automatic test management method and system. The method comprises the steps that real-time environment state data in an experimental environment are collected in real time, then redundancy mechanism and check algorithm processing is carried out, and discharge parameters of an electrostatic generator are calculated and adjusted in real time through a multi-dimensional nonlinear self-adaptive control algorithm; performing electrostatic discharge based on the adjusted discharge parameters of the electrostatic generator, generating electrostatic interference of specified parameters, simulating an actual scene of electrostatic discharge, further transmitting a generated electrostatic discharge interference signal to test equipment, and generating a corresponding response signal; the response signals and the interference signals are preprocessed and then subjected to deep spectrum analysis, key features are extracted, and the immunity of the test equipment is intelligently evaluated through a neural network model. The control precision of the electrostatic generator is improved, the electrostatic generator can dynamically adapt to the change of the experimental environment, and it is ensured that the discharging condition is always in the optimal state.

Embodiments described herein provide an automated testing pipeline for providing a testing dataset for testing a trained neural network model trained using a first training dataset. A first testing dataset for the trained neural network including a first plurality of user queries is received. A dependency parser is used to filter the first plurality of user queries based on one or more action verbs. A pretrained language model is used to rank the remaining user queries based on respective relationships with queries in the first training dataset. Further, user queries that are classified as keyword matches with the queries in the first training dataset using a bag of words classifier are removed. A second testing dataset is generated using the ranked remaining user queries. Testing outputs are generated, by the trained neural network model, using the second testing dataset.

The invention discloses an intelligent element positioning method and system based on AI and a dynamic feature library, and the method comprises the steps: carrying out the anomaly detection and abnormal data capture, executing an element positioning operation through an automatic script, and recording a page DOM tree structured snapshot when the positioning is judged to be failed; multi-modal feature extraction: analyzing the DOM tree, and extracting an XPath / CSS path and semantic attributes of a target element; performing dynamic feature library retrieval and matching, calculating DOM structure similarity, and taking a high-similarity rule as a correction basis if the high-similarity rule exists; an AI intelligent rule is generated, and candidate positioning rules are generated through the classification model; performing script correction and real-time feedback, replacing the failure positioning statement and updating the dynamic feature library; and generating a structured report, and recording abnormal data. According to the method, the AI is combined with the dynamic feature library, so that the intelligent correction of element positioning in the automatic test process is realized, and the stability and efficiency of the automatic test are improved.

A computer-implemented method is disclosed for transforming academic test performance into personalized feedback and learning recommendations. The method involves presenting an academic test to a user via a user interface of an online learning platform and receiving the user's submitted answers. The system accesses input parameters including historical user-performance data, correct answers, and coaching session data. The user's responses are compared with the correct answers to identify incorrect responses. A prompt generator creates a prompt to guide and constrain an AI engine in analyzing the test responses. The AI engine correlates the incorrect responses with historical performance data and coaching session information to detect learning patterns or recurring errors. Based on the identified patterns, the system generates personalized feedback and targeted learning recommendations to address specific learning gaps. The method enables adaptive, AI-assisted post-assessment guidance, improving learning outcomes through individualized support.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention discloses an automatic code testing method and device based on a low-code platform and a medium, and belongs to the technical field of automatic code testing. The method comprises the steps of obtaining metadata of a target application based on an interface of a low-code platform; performing semantic analysis on the metadata based on a natural language processing algorithm to generate an initial test case set; performing coverage optimization on the initial test case set based on a machine learning model, and screening out high-priority test cases according to conditions; analyzing the high-priority test case by simulating user operation to generate an executable test script; executing the executable test script and constructing a multi-dimensional test result data set; and performing abnormal area detection on the interface screenshot based on an image recognition algorithm, and constructing a causal relationship graph with an event sequence in the operation log to position a fault root cause. By means of the method, automatic testing of the codes of the low-code platform is achieved.

The invention discloses a law and regulation simulation test method, device and equipment for an intelligent driving algorithm and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: carrying out the high-precision three-dimensional reconstruction of a law and regulation test site according to collected data, and obtaining a three-dimensional reconstruction scene, and the collected data comprises a laser radar point cloud and a multi-view camera image; obtaining simulation working condition test data based on the three-dimensional reconstruction scene; training a network model according to the simulation working condition test data and the real vehicle working condition test data to obtain a vehicle dynamics network model; and based on an automatic test program, constructing a closed-loop simulation link according to the three-dimensional reconstruction scene and the vehicle dynamics network model, and carrying out regulation simulation test on an intelligent driving algorithm. According to the invention, the efficiency and accuracy of intelligent driving regulation testing can be improved.

A computer-implemented method, a computer system, and a computer program product for generating an automation test script. Existing testing documents of a product under test can be acquired. A testing topology describing steps, containers, elements and actions of the test can be generated by extracting keywords in the existing test documents, wherein each element defines a user interface (UI) element of the product, each action defines an action attribute for an associated UI element, each container defines an operation area containing one or more UI elements, and each step defines one or more operations for one or more actions associated with one or more UI elements. An automation test script for the product can be generated based on the testing topology.

The invention relates to the technical field of battery testing, in particular to an automatic BMS function testing system and method. The method comprises the following steps: acquiring BMS working parameters; performing short-circuit working condition configuration processing according to the BMS working parameters to generate short-circuit working condition configuration data; performing short-circuit signal simulation based on the short-circuit working condition configuration data to generate short-circuit simulation control signal time sequence data; based on the short-circuit analog control signal time sequence data, a battery pack current short-circuit driving test is carried out through a BMS monitoring system, and BMS monitoring response data is generated; and performing short-circuit current monitoring protection evaluation according to the BMS monitoring response data, and generating BMS function test level data. According to the invention, the automatic test of the BMS short-circuit protection function is realized by intelligently configuring the short-circuit working condition, and the efficiency, accuracy and repeatability of the test are remarkably improved.

The invention provides a vehicle software testing method and device based on a large model, and relates to the technical field of software testing, and the method comprises the steps: obtaining the testing demand information of to-be-tested vehicle software; generating a model prompt according to the test demand information, wherein the model prompt is used for indicating to generate resources required by the vehicle software test; inputting the model prompt into the large model, and obtaining each driving scene and a vehicle software test case output by the large model; performing physical rule verification on each driving scene; determining the driving scene passing the verification as a target driving scene, and inputting the target driving scene into a simulation platform to obtain simulation data of the target driving scene; and inputting the simulation data and the test case into an automatic test platform to enable the automatic test platform to analyze the simulation data, and executing the test case according to the analyzed simulation data to obtain a test result. By applying the method provided by the invention, the test efficiency and the coverage rate can be improved.

The invention discloses a vehicle electromagnetic compatibility test method and device based on a big data platform, and relates to the technical field of vehicle testing, and the method comprises the steps: creating a vehicle electromagnetic compatibility detection item in a cloud platform, and generating a detection task; detecting equipment and a to-be-detected piece are connected to the cloud platform through the Internet of Things technology, and standardized parameters and a hardware connection template of a detection task are configured; physical connection of the detection system is completed based on the hardware connection template; calling an automatic test module to execute a standardized test process, and collecting test data in real time; performing multi-dimensional analysis and intelligent evaluation on the test data to generate a detection report and authentication data; synchronizing the detection report and the authentication data to a service platform for secondary data application; visualization and collaborative management of the test process are carried out through the remote monitoring module.

The invention discloses an APP automatic testing method based on multi-modal perception and an Agent system, and relates to the field of image recognition or understanding, and the method comprises the following steps: a real-time perception layer carries out the real-time screenshot of a screen interface of an APP; when the interface changes, processing the screenshot in three paths: detecting a control bounding box; performing visual semantic coding on the interface elements; synchronously extracting the text content, and generating a function label. And fusing the three paths of output results to construct a UI tree. When a task instruction is input, the dynamic reasoning layer generates an action sequence according to the task instruction and the UI tree through a nerve-symbol collaboration mechanism, updates a dynamic document and stores the dynamic document in a real-time knowledge base. And the execution optimization layer converts the action of the action sequence into an operation code exclusive to the platform, executes the operation code, automatically marks an abnormal path at the same time, and updates an avoidance strategy to a real-time knowledge base. According to the method, real-time sensing, dynamic reasoning and closed loop execution are realized through a three-level architecture, and the generalization ability, the exception coverage and the execution efficiency are remarkably improved.

Provided in the embodiments of the present application are an automated software testing method, system and apparatus, and a storage medium. The method comprises: in response to acquiring a function requirement of software or a target function requirement selected by a user, processing the function requirement or the target function requirement on the basis of a test case generation model, so as to generate at least one test case expressed by a natural language (S310); on the basis of a preset condition, obtaining test information of the test case from the test case (S320); on the basis of the test information, constructing a test script corresponding to the test case, and executing the test script to obtain an execution result (S330); and processing the execution result on the basis of an execution result determination model, and determining an execution state of the test case corresponding to the execution result (S340).

The invention discloses an intelligent agent network access evaluation method and system based on automatic label generation and multi-dimensional evaluation, and the method comprises the steps: automatically generating an intelligent agent multi-dimensional label through a large-scale language model, constructing an intelligent agent portrait, and combining automatic test case generation and multi-dimensional evaluation index collection. The problems that in the prior art, agent evaluation standardization is lacked, evaluation efficiency is low, and'black box 'diagnosis is difficult are solved, standardized and automatic quality evaluation is carried out on various heterogeneous agents connected to a platform, and an agent portrait capable of being used for accurate retrieval and scheduling is generated. The method and the system can be widely applied to scenes such as a multi-agent collaboration platform, an agent store and an enterprise-level agent management system, and have the remarkable advantages of remarkably improving the ecological quality of the agent, greatly reducing the manual evaluation cost, accelerating the introduction and commercialization of a new agent and the like.

The invention relates to an RPA system based on natural language interaction and an implementation method thereof. The system comprises a user input module, an intention recognition module, a process planning module, an execution module, an inspection module and an output module. A user inputs a natural language instruction through a textbox or a microphone, the intention recognition module recognizes the intention of the user through an artificial intelligence large language model, and the process planning module plans an operation target and operation steps according to the intention of the user and a screen interface. The execution module controls the mouse and the keyboard to execute planned operation on the operation target through the technology related to automatic testing, the checking module checks whether the execution result meets the intention of the user or not after the operation is completed, and finally the output module feeds back the execution result to the user. According to the method, a common user can easily create a complex automatic process through a natural language, and the use threshold of an RPA system is greatly reduced.

The invention provides an automatic test system and method capable of being arranged and executed in batches, and the system comprises a test case arrangement plan table which is used for customizing the starting time, the precondition and the dependency relationship of each test item, whether execution is terminated or not when the test item fails, grouping execution, result archiving, product dependency and user-defined labels; the default configuration management module is used for parameterizing execution and supporting simple configuration to realize visual operation of the test script; the software framework supports an object-oriented language and provides multi-process processing to improve execution efficiency; and the distributed structure is used for realizing concurrent parallel execution across a plurality of processors or machines and supporting synchronous import and concurrent asynchronous execution of a plurality of test items. Through the support of multi-process processing and a distributed structure, the advantages of a multi-core processor and computing resources of cross-network nodes can be fully utilized, multiple test items are executed in parallel, the execution efficiency of automatic testing is greatly improved, and the overall testing period is shortened.