1325 results about "Test script" 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.

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

Methods and systems for artificial intelligence (Al) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

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 present disclosure relates to a method for generating code for a mobile communication tester. The method comprises the steps of: receiving information on a test scenario at an inputting environment; and automatically generating a test script code for the mobile communication tester based on the received information; wherein the test script code is configured in such a way that, when it is executed by the mobile communication tester, it causes the mobile communication tester to generate a test routine according to the test scenario, wherein the test routine adheres to a mobile communication standard.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

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 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 a software testing automation platform and an intelligent optimization method thereof, and particularly relates to the technical field of software testing. The test case management module simplifies definition and automatic classification of test cases through a visual interface and a natural language processing technology; secondly, a test script generation module automatically generates and optimizes a test script by utilizing a preset template library and a self-defined script function in combination with an intelligent optimization tool; the test execution module executes test tasks in parallel in a multi-server or virtual machine environment through a task scheduling algorithm, and extracts environment load and response time characteristics in real time; then, the test result analysis module comprehensively analyzes an execution result by using the data prediction model, and identifies and marks an abnormal test case; and finally, the intelligent optimization module deletes the abnormal cases and dynamically adjusts the priorities of the normal cases to ensure that the high-priority test cases are executed preferentially, so that the automation level of test management is effectively improved, and the test execution efficiency and the result accuracy are optimized.

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.

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 an intelligent cabin software testing method and device, an intelligent cabin and a vehicle. The method comprises the following steps: acquiring a test requirement document of the intelligent cabin software; according to the test demand document, generating a decision tree and a dynamic demand graph corresponding to the test demand document; generating a function knowledge graph corresponding to the intelligent cabin software according to the decision tree and the dynamic demand graph corresponding to the test demand document; according to the function knowledge graph corresponding to the intelligent cabin software, generating a test case of the intelligent cabin software; and according to the test case of the intelligent cabin software, generating and executing a test script of the intelligent cabin software to obtain a test result of the intelligent cabin software. According to the method, the automatic process of compiling the test case and generating the test script can be realized, and the test efficiency of the intelligent cabin software is 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 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.

The invention relates to the technical field of data processing, and discloses an in-vehicle testing method and system based on a small model. The method comprises the following steps: acquiring an in-vehicle machine screen image, and extracting interface elements through a lightweight recognition model to generate a recognition result; analyzing element function association to determine operation types to form a test operation sequence; an ADB instruction and a mechanical arm action are generated according to the operation sequence to construct a test script; executing the script to drive the vehicle machine to test and collecting multi-dimensional response data; and analyzing test data, calculating a passing rate and response time, and generating a verification report. According to the method, the technical problems of low interface element identification accuracy, poor test script adaptability and insufficient multi-modal data analysis capability in the traditional vehicle-mounted terminal test are solved, and the automation degree of the vehicle-mounted terminal test and the reliability of the test result are remarkably improved.

An apparatus comprises at least one processing device configured to select a test case associated with a test script comprising functional code for testing an information technology asset and a test case description. The at least one processing device is also configured to generate, utilizing a first machine learning model, first pseudocode for the test script based at least in part on the functional code of the test script, and to generate, utilizing a second machine learning model, second pseudocode for the test script based at least in part on the test case description. The at least one processing device is further configured to determine a stability of the test script based at least in part on a similarity between the first and second pseudocode, and to automatically control one or more characteristics of the functional code of the test script based at least in part on the determined stability.

The invention relates to the technical field of software development, and discloses an automatic UI function test method and system, electronic equipment and a storage medium. The method comprises the steps of receiving a natural language test requirement; intercepting an interface image in real time and transmitting the interface image to a multi-modal analysis module through an API; interface element layout is identified based on computer vision (CV), demand semantics are analyzed in combination with natural language processing (NLP), and a dynamic operation instruction set is generated; driving the automatic tool to execute clicking, sliding and input operations through the API; dynamically adjusting a test path according to execution feedback, and covering a multi-scene case; interface element states, system logs and performance data are monitored in real time, and automatic verification is achieved through multi-dimensional data comparison. According to the method, multi-modal recognition and AI intention understanding technologies are fused, the problems that a traditional UI test script is high in maintenance cost and low in scene coverage rate are solved, non-intrusive self-adaptive full-process testing is achieved, and the testing efficiency is improved by 40% or above.

The invention provides a Web automatic testing method and system based on parameterized input, and the method comprises the steps: capturing the operation of a user in a browser, so as to generate a basic script; analyzing and reconstructing the basic script to generate a test script containing parameter variables for executing different test scenes through different parameter values; modifying the test script to obtain a modified test script; setting a plurality of test cycles, and testing the modified test script by using the test cycles to obtain a test result; and generating a test report according to the test result. According to the method, the user operation is automatically captured, the basic script is generated, and the basic script is analyzed, reconstructed and optimized, so that the workload of manually compiling the script can be reduced, the flexibility and stability of the script are enhanced, and then multi-scene verification is performed through the test cycle, so that the problem of parameter change in the test method is solved, and the test efficiency is improved. And the case writing efficiency is low.

Methods, systems, and apparatus, including medium-encoded computer program products for executing a test strategy, include: receiving a request to generate a test script for a new test strategy defined for a software solution, wherein the received request includes a specification of the new test strategy; generating the test script based on executing a large language model that receives as input the specification of the new test strategy, wherein the large language model is trained to automatically generate test scripts based on technical documentation for a testing framework defined for the software solution and integration tests generated for the software solution; and executing the test script for the new test strategy to obtain output data, the output data including performance data for the software solution.

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.

A natural language test case for an application is automatically converted into a test script by a test script generator. During the generation of the test script, the test script generator may access a blueprint of the application. The blueprint may include a time-evolving model of the application, which may include a set of translations and a state machine model of the application. The state machine model may include a state space and a set of state transitions. During the test script generation process, the blueprint may be continually updated to include new knowledge of the application. The test script generator may communicate with an artificial intelligence (AI) engine in order to determine the semantic similarity between two phrases, convert images into textual description, and perform tasks requiring the use of a large language model (LLM).

The invention discloses a multichannel extended hard disk automatic test device and method. The multichannel extended hard disk automatic test device comprises a control server, a network switch, a test host and a test device, the control server is connected with at least one test host through a network switch, each test host is connected with a test device through a programmable power strip, and the test devices are connected with multiple paths of hard disk equipment; the control server can generate test parameters and issue the test parameters to the test host with the network wakeup function, and the test host can automatically call corresponding test scripts according to the received test parameters, execute test tasks on the hard disk, record the test process and result and upload the test process and result to the control server; the test device can realize automatic switching of a plurality of hard disks of the same test host interface according to the test parameters; and meanwhile, the power supply of the test host is controlled through the programmable power strip so as to generate abnormal power failure test excitation. According to the invention, a single test host can automatically and orderly test a plurality of hard disks in batches.

The invention discloses a terminal computing power management platform stability test method and system, and belongs to the technical field of computing power platform tests.The method comprises the steps that for a test object, connection is established, basic configuration is carried out, and association extraction is carried out; service arrangement, self-definition of published message test parameters and message template customization are carried out to quickly create a service pressure test script and accurately simulate access of users of different magnitudes to a service system; scene construction: dividing a test scene according to two basic dimensions of connection and message throughput, constructing a standard test scene, and rapidly simulating a complex service scene in combination with a set point, thinking time and a condition control instruction; result visualization and performance optimization are carried out, and the performance is optimized by combining service index and resource index data in a test report, so that the stable operation of the system is ensured. According to the invention, various scenes and configurations can be quickly constructed, the test difficulty is reduced, the test efficiency is improved, and the test reliability is ensured.

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 disclosure relates to systems and methods for automated performance testing in a containerized environment. For example, a system may invoke one or more PTSs (PTSs) that each manage a respective type of performance test, such as a load test, a stress test, and a soak test. Each PTS may instantiate one or more dynamic clusters in a container to execute performance test scripts that execute operations to conduct the performance test. The PTS may estimate a testing capacity of each dynamic cluster so that the load during testing does not exceed those limits. The dynamic clusters may perform client-side load balancing that approximates real-world load profiles to more accurately simulate real-world loads based on user behaviors that would occur throughout a test session. The system may use logging and metrics from a diverse set of services that monitor target systems during the automated performance test.

The invention relates to a UI automatic testing method and system.The method comprises the steps that interface elements are obtained according to HTML source codes of a prototype interface, element keywords of the interface elements are obtained according to the element types of the interface elements, and when the interface elements contain click events, testing logic of the click events can be automatically recorded and tracked; and associating the element keyword with the test logic to establish a mapping relationship between the test logic and the interface element, constructing a to-be-tested demand description according to the element type and the mapping relationship, and inputting the to-be-tested demand description into the AI large language model to generate a UI automatic test script. And performing UI automatic testing on the to-be-tested interface corresponding to the prototype interface according to the UI automatic testing script to obtain a testing result. Therefore, when the interface layout of the to-be-tested interface is changed, the interface elements can be positioned through the element keywords, the associated test logic is further driven, the UI automatic test script does not need to be modified and maintained, and the UI automatic test efficiency is improved.

The invention provides an intelligent test platform, which comprises a test demand analysis module used for carrying out demand analysis on input test demand information based on a demand analysis agent to obtain a plurality of test demand items; the test case generation module is used for calling a case generation agent to generate a plurality of test cases corresponding to a plurality of selected target test demand items in response to a selection operation on the plurality of test demand items; the test script generation module is used for calling a script generation agent to generate at least one test script corresponding to at least one checked target test case in response to check operation on the test cases; the test data demand analysis module is used for calling a data demand agent to perform demand analysis on the test case to obtain a plurality of test data demands; and the test data generation module is used for calling a data generation agent to generate test data corresponding to the test data demand. According to the invention, the test efficiency and the test reliability are improved.

The invention discloses an LLM-based controller CAN communication test method and system, and the method comprises the steps: converting a DBC file into a configuration file, reading a VSS and CAN signal corresponding relation file, and inputting the VSS and CAN signal corresponding relation file to an LLM; calling an LLM model, and generating a test case according to the cue word; constructing an automatic framework to implement an execution function based on a behavior-driven development BDD mode; and associating the generated test case with an execution function in the automation framework, constructing a complete test suite, and operating the test suite to execute a CAN bus communication test. VSS path and CAN Signal verification is carried out on the basis of the controller which already realizes the VSS technology, the test case conforming to the BDD natural language is generated by using the LLM, the test case is directly used as the test script, the step of manually compiling the test case is omitted, the test efficiency is improved, and the test cost is reduced.