3884 results about "Automatic testing" patented technology

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 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.

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 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 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 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 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 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.

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 a multi-defect ultrasonic guided wave nondestructive testing, positioning and recognition method based on a mechanical arm, relates to the technical field of nondestructive testing and automatic testing, and solves the technical problems that in the prior art, time and labor are consumed in detection of large components, and positioning recognition and detection precision is difficult to guarantee in a manual detection mode. The method comprises the following steps of: post-processing signals acquired for N times, and setting a signal amplitude threshold value; performing global scanning on the tested piece; and whether the mechanical arm completes global scanning or not is judged. A mechanical arm is used for carrying an electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, and detection and positioning recognition of various different defects are achieved; using the collected data as an original data set to construct a deep learning model and an integrated CNN model, and realizing adaptive scanning detection of internal defects of a large component and a large curved surface structure; the use of a coupling agent is reduced, the detection time efficiency and the detection precision are improved, and the detection accuracy and the defect positioning accuracy are realized.

The invention discloses an AI-driven automatic testing and document generation method and system, and the method comprises the steps: obtaining document data of test software, carrying out the preprocessing and information extraction, and forming structured data; identifying key test points from the structured data by using an agent, and generating a test case; executing the test case by utilizing a preset automatic test framework to obtain a test result; retrieving data information associated with the structured data in the knowledge base by utilizing RAG to form a retrieval result set; fusing the retrieval result set with the structured data and the test result according to a preset weight strategy to construct a complete context; and taking the context as input, calling the large language model by utilizing an agent, and generating a technical document. By means of the technical scheme, accurate and real-time technical document generation is achieved, the document generation quality can be continuously improved, and the real-time updating capacity and the data driving decision support capacity of the document are improved.

The invention relates to the field of industrial visual inspection, and provides an automatic test performance benchmark construction method which comprises the following steps: dynamically generating ROI (Region of Interest) parameters; performing pixel cutting on the target area of the production line according to the dynamically generated ROI parameters to generate a high-speed image data stream, and aligning the image acquisition moment with the mechanical motion phase of the production line; selecting a matched visual detection model based on target features in the high-speed image data stream, and outputting a defect detection result by the matched visual detection model; according to defect distribution parameters of a preset test scene, the simulation defect features are embedded into the high-speed image data stream, and a mixed test data stream is generated; performing multi-device time sequence alignment on the mixed test data stream and the defect detection result, and packaging into a standardized data set containing a unified time reference; and analyzing the standardized data set, comparing the simulated defect truth value label with a defect detection result, calculating a multi-dimensional performance index of the visual inspection system, and generating a test report containing parameter optimization suggestions.

Various aspects of the disclosure relate to automated testing for application programming interfaces (APIs). A dynamic API validation computing system leverages a generative AI model to dynamically generate a multitude of data sets and rules for use during API validation activities. A federated byzantine agreement mechanism performs the validation of each API using the generated test cases and test data. A generator engine incorporates a generative AI model that may be trained on a large corpus of API metadata and / or data characteristics to predict data patterns and / or validation rules for each of the APIs under test. The generator engine may also predict a structure and format of requests based on the training model inputs. Multiple Test cases for an API created through use of the generative AI model may be distributed and executed on different testing nodes that reach a consensus about whether each API has passed or failed.

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 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 discloses a Web automatic testing method, system and device, a medium and a program product, and belongs to the technical field of artificial intelligence. The method comprises the steps that test case abstract representation information is generated according to interactive input of a user and a test case mapping template, and the test case abstract representation information comprises a target test task type indicated by the interactive input and intention natural language description information; based on the test case abstract representation information, obtaining a target screenshot of the tested Web page and attribute information of interaction elements; a multi-mode large language model is called to conduct reasoning based on the target screenshot, the attribute information of the interaction elements and the intention natural language description information, reasoning result information is output, and the reasoning result information represents operation executed on the target interaction elements located at the target position in the tested Web page; and converting the reasoning result information into a machine executable instruction, and executing the machine executable instruction. According to the embodiment of the invention, the stability of Web automatic testing can be improved.

The invention provides an automatic network protocol testing method and system based on a large language model. According to the method, through three core technologies of structured protocol specification extraction, hybrid test case generation and retrieval feedback enhanced code generation, full-process automation from a protocol specification document to an executable test code is realized. The method comprises the following steps: firstly, carrying out preprocessing and structured analysis on an RFC document, and extracting protocol field information and state machine description; a complete test case covering normal, abnormal and boundary conditions is automatically generated based on protocol elements; and finally, an initial test code is generated in combination with a test equipment API document retrieval result, and multi-round iterative optimization is performed through running log analysis and an experience knowledge base. Compared with the prior art, according to the scheme, an end-to-end automatic test work chain is achieved, new protocol testing can be adapted without manual protocol modeling, meanwhile, a large language model and expert experience are fused through a feedback mechanism, and the test efficiency and the code quality are remarkably improved.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention provides an on-line automatic test method for the compressive capacity of a corrugated carton, and belongs to the technical field of corrugated carton production. A micro-deformation loading system is constructed to apply a test load which does not exceed 10% of the expected maximum bearing capacity, and a laser displacement sensor array is adopted to measure micro-deformation displacement data of six surfaces of the carton; and synchronously acquiring acoustic emission signals and extracting spectrum characteristic parameters. Micro-deformation data is analyzed based on a carton fiber structure physical mechanism model, stress field distribution is calculated, analysis is carried out in combination with a hierarchical attention carton strength evaluation model, a rigidity coefficient is calculated and compared with a reference threshold value, a structural weak area is identified through a strain energy distribution diagram, and a damage risk index is quantified. And comprehensively evaluating the compressive strength prediction value and the confidence interval of the corrugated carton, and outputting a prediction result, thereby realizing accurate evaluation of the compressive strength of the corrugated carton under a non-destructive condition.

The invention relates to a full-process order life cycle management method, system and device and a medium, and the method comprises the following steps: obtaining user information and user demands, and building a work order; according to a preset rule, work order distribution is carried out, related information is supplemented, and a pre-sales scheme is generated; technical feasibility, resource requirements and risks of the pre-sales scheme are automatically assessed, and an assessment report is generated; project research and development are carried out based on the pre-sales scheme and the evaluation report, the research and development progress is updated in real time in the research and development process, and research and development results are uploaded after research and development are completed; the research and development results are tested based on the automatic test script, and a test report is automatically generated; generating a delivery list based on research and development results and test reports, and automatically sending the delivery list to the user; and obtaining questions fed back by the user, performing automatic classification according to the questions, and performing adjustment according to a classification result until the order is completed. Compared with the prior art, the device has the advantages of high automation degree and the like.

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-rule static detection and large-model dynamic repair method for code defects. The method comprises the steps that code defect types are summarized and abstracted into a unified defect mode rule set; constructing an abstract syntax tree, a control flow diagram and a data flow diagram of the source code; performing static analysis based on a multi-rule engine to identify potential defects; inputting the defect context into a large language model to generate a repair suggestion; performing semantic consistency verification and integration on the repair suggestions; and executing automatic testing and secondary static analysis to verify a repair result. According to the method, the traditional static analysis technology and the modern large language model capability are combined, accurate recognition and intelligent repair of code defects are achieved, a closed-loop defect processing flow is formed, the defect detection accuracy is improved, intelligent repair is achieved, closed-loop verification is formed, the development efficiency is improved, and man-machine cooperation is supported.

The invention relates to the technical field of automatic testing, and discloses an automatic testing scheme generation method and device and a medium, and the method comprises the steps: matching a demand action in a test demand with a code entry name corresponding to a code segment, and determining a code risk level of a current demand code block; analyzing the code change frequency and the code dependency degree of the current demand code block according to historical test records in the test domain knowledge base, and determining the test dynamic priority of the current demand code block; constructing a control flow diagram and a data flow diagram of the current demand code block, and detecting risk nodes in the control flow diagram and the data flow diagram; generating a test scene of the current demand code block according to the risk node, and generating a boundary value test rule according to a key constraint condition in the data flow diagram; and querying an environment simulation rule of the current demand code block, and packaging the test scene, the boundary value test rule and the environment simulation rule into an automatic test scheme. According to the invention, the code coverage when the test scheme is generated can be 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.

A digital assistant can provide support for automated testing of applications. A natural language interface can be provided by which a testing user can specify a request for one or more testing actions. A natural language processing model can recognize intents in the request, and the intents can be used to execute executable code to perform the requested testing actions. Multiple actions per request can be supported. An object repository can be leveraged to determine user interface control identifiers, and a test data container can store values for use during testing. Testing functionality can thus be provided to a wider base of testing users. A real time, scriptless approach can conserve computing resources.

The invention discloses a code generation method based on dynamic hierarchical sparse attention, and belongs to the technical field of data processing, and the method comprises the following steps: S1, collecting an input data set; s2, constructing a dynamic hierarchical sparse attention model; s3, optimizing the dynamic layered sparse attention model by using the input data set, and generating a final dynamic layered sparse attention model; and S4, generating a code by using the final dynamic hierarchical sparse attention model. According to the method, the model can still keep high-quality code generation capacity at extremely low labeling cost, and a flexible and efficient solution is provided for agile development and automatic testing.