Scene test environment construction method and device, equipment, storage medium and product

Abstract

The application discloses a scene test environment construction method and device, equipment, storage medium and product, relates to the test technical field, and the method comprises the following steps: receiving scene construction information input by a user through a front-end interface, wherein the scene construction information comprises a scene construction requirement, environment information and a target test scene selected by the user; converting the scene construction information into executable scene construction steps through a background AI engine, and automatically executing the scene construction steps according to a preset logic to construct a scene test environment. The application not only improves the complex scene test efficiency, but also reduces the test threshold, and effectively reduces the environment conflict problem when multiple people cooperate.

Description

Technical Field

[0001] This application relates to the field of product testing technology, and in particular to a method, apparatus, equipment, storage medium and product for constructing an AI-based intelligent combined complex scenario testing environment. Background Technology

[0002] As platform products become increasingly complex, testing complex business scenarios has become a crucial aspect of ensuring product quality. Currently, complex scenario testing for platform products suffers from low efficiency, manifested in several ways: a lack of systematic scenario construction methods, high testing thresholds, time-consuming manual operations, and significant environmental conflicts during multi-person collaboration. Summary of the Invention

[0003] The main purpose of this application is to provide an AI-based intelligent combined complex scenario testing environment construction method, device, equipment, storage medium and product, which aims to improve the testing efficiency of complex scenarios, lower the testing threshold and effectively reduce environmental conflicts when multiple people are collaborating.

[0004] To achieve the above objectives, this application proposes a method for constructing a scenario testing environment, which is applied in the background and includes: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The background AI engine transforms the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

[0005] In one embodiment, the step of converting the scene construction information into executable scene construction steps through a background AI engine, and automatically executing the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene includes: The background AI engine uses natural language processing technology to intelligently analyze the scene construction information, extract the scene construction steps, and generate an executable standardized construction process. According to the preset strategy, the corresponding construction method is matched and the standardized construction process is automatically executed within the minimum impact range to build a scenario test environment corresponding to the scenario construction requirements, environmental information and standard test scenario.

[0006] In one embodiment, the step of using a background AI engine and natural language processing technology to intelligently parse the scene construction information, extract scene construction steps, and generate an executable standardized construction process includes: Through the background AI engine, natural language parsing technology is used to identify the intent of the scene construction information, extract the scene construction steps and classify the tasks. Then, an executable standardized construction process is generated through business orchestration components and AI assembly configuration.

[0007] In one embodiment, the step of automatically executing the standardized construction process within the minimum impact range by matching the corresponding construction method according to a preset strategy includes: According to the preset strategy, the corresponding constructor method is matched and called to isolate and execute the application, interface, configuration and related data.

[0008] In one embodiment, the method further includes: During the construction and execution process, API support layer capabilities are invoked to perform at least one of the following operations: start or stop the application, enable or disable the API, modify configuration, restore key-value pairs, execute scheduled jobs, and perform application isolation and data recovery.

[0009] In one embodiment, the method further includes: After the scenario testing environment is constructed, the construction results are fed back to the user through the front-end interface; and / or The front-end interface executes at least one of the following actions: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

[0010] In one embodiment, the method further includes: After the test is completed, the test environment is restored using the one-click recovery function and a preset recovery strategy.

[0011] In one embodiment, the one-click recovery function includes at least one of the following: automatically rolling back configuration parameters, resetting the database, and removing application isolation to restore the scenario test environment to its initial stable state.

[0012] Furthermore, to achieve the above objectives, this application also proposes a method for constructing a scenario testing environment, which is applied to the front end, and the method includes: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The scenario construction information is sent to the backend, where the AI ​​engine converts the scenario construction information into executable scenario construction steps and automatically executes the scenario construction steps according to preset logic to build a scenario test environment corresponding to the scenario construction requirements, environmental information, and target test scenario.

[0013] In one embodiment, the method further includes: After the scenario testing environment is constructed, the construction results are fed back to the user through the front-end interface; and / or The front-end interface executes at least one of the following actions: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

[0014] Furthermore, to achieve the above objectives, this application also proposes a scenario testing environment construction device, the device comprising: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The background AI engine transforms the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

[0015] In addition, to achieve the above objectives, this application also proposes a scene testing environment construction device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the scene testing environment construction method as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the scenario testing environment construction method described above.

[0017] In addition, to achieve the above objectives, this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the scenario testing environment construction method described above.

[0018] One or more technical solutions proposed in this application have at least the following technical effects: The method receives scene construction information input by the user through a front-end interface. This information includes scene construction requirements, environmental information, and the target test scene selected by the user. A back-end AI engine transforms this information into executable scene construction steps and automatically executes these steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene. This application proposes a scene construction method based on natural language input. Using intelligent parsing technology, the method processes the natural language input by the user to automatically extract and accurately classify scene construction steps. By automatically executing these steps according to preset logic, the method enables intelligent construction of the scene test environment. This approach not only improves the efficiency of testing complex scenes but also lowers the testing threshold and effectively reduces environmental conflicts during multi-user collaboration. Specifically, it can intelligently construct scene environments by combining preset strategies, while supporting minimal control over the impact range of construction steps. It supports one-click construction of complex scenes, significantly improving construction efficiency and quality while lowering the testing threshold. Minimizing the impact range of construction steps effectively reduces environmental conflicts and improves the stability and reusability of the test environment. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the system architecture involved in the embodiment of the scenario test environment construction method of this application; Figure 2 A flowchart illustrating the first embodiment of the method for constructing a test environment for the scenario in this application; Figure 3 A flowchart illustrating the fifth embodiment of the method for constructing a scenario testing environment in this application; Figure 4 This is a schematic diagram of the module structure of the scenario testing environment construction device according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the scenario testing environment construction method in this application embodiment.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is as follows: The front-end interface receives scene construction information input by the user. This scene construction information includes scene construction requirements, environmental information, and the target test scene selected by the user. A background AI engine converts the scene construction information into executable scene construction steps and automatically executes these steps according to preset logic to construct a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene. This application proposes a scene construction method based on natural language input. Based on intelligent parsing technology, it processes the natural language input by the user to automatically extract and accurately classify scene construction steps. Automatic execution of scene construction steps according to preset logic enables intelligent construction of the scene test environment. This solution not only improves the efficiency of testing complex scenes but also lowers the testing threshold and effectively reduces environmental conflicts during multi-person collaboration. Specifically, it can intelligently construct scene environments by combining preset strategies, while supporting minimal control over the impact range of construction steps; it supports one-click construction of complex scenes, significantly improving construction efficiency and quality while lowering the testing threshold; and it achieves minimal control over the impact range of construction steps, effectively reducing environmental conflicts and improving the stability and reusability of the test environment.

[0026] This application takes into account that in the existing testing work of platform products, the testing efficiency of complex scenarios is low, there is a lack of systematic scenario construction methods, the testing threshold is high, manual operation is time-consuming, and environmental conflicts are prominent when multiple people are collaborating.

[0027] Therefore, this application provides a solution that lowers the testing threshold through natural language input, eliminating the need for specialized configuration knowledge; significantly improves environment construction efficiency through AI-automated parsing and step arrangement; reduces conflicts in multi-person collaborative environments at the source through isolation with minimal impact; achieves rapid environment reuse and stable consistency through one-click construction and one-click restoration; and supports standardized management of construction methods, enabling batch import and export for easy team collaboration and reuse.

[0028] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device, scenario testing environment construction device, system, or device capable of realizing the above functions. The following description uses a scenario testing environment construction system as an example to illustrate the various embodiments.

[0029] like Figure 1 As shown, the system architecture for constructing the scenario testing environment in this embodiment includes a front-end and a back-end. The front-end is configured with a front-end interaction layer, and the back-end is configured with a back-end AI engine. The front-end interface is used to input scenario construction methods and related environmental information, user-inputted construction requirements, etc., and the back-end engine is responsible for parsing and executing the construction steps.

[0030] Specifically, the front-end interaction layer is configured with a front-end application. This layer serves as the visual interaction entry point between the user and the system, providing an interface that allows users to complete all operations related to scenario construction without directly accessing the underlying technology. For example, it provides interfaces for inputting and selecting scenario construction requirements, environment information, and target test scenarios; displays the execution progress, status, and results of scenario construction / restoration; provides entry points for managing construction methods, batch import / export, and one-click construction / restoration; and receives execution notifications and exception alarms pushed by the system.

[0031] The application layer capabilities of front-end applications include: adding, deleting, and modifying constructors; batch importing and exporting constructors; scene environment construction; and scene environment restoration. Among these: Add, delete, and modify constructor methods: This feature allows users to add, delete, modify, and maintain scenario constructor methods, enabling custom extensions and version management of constructor methods. This module can adapt to the personalized construction needs of different business scenarios and supports continuous iteration of construction capabilities.

[0032] Batch import and export of constructors: Supports batch import and export of constructors as files, enabling the reuse and migration of constructor capabilities across environments and teams. This feature can reduce team collaboration costs and improve the standardization and reusability of constructors.

[0033] Scene environment construction: Responding to the user's one-click construction command, the user's needs are transmitted to the backend, driving the subsequent AI analysis and automated execution process. This function module can serve as the core trigger entry point for scene construction, initiating the entire process of environment construction.

[0034] Scene environment restoration: In response to the user's one-click restore command, the background performs an environment rollback operation to restore the test environment to its initial state. This function module can realize the rapid reuse of the test environment and avoid test data and configuration pollution.

[0035] The backend configuration includes a backend AI engine, which can specifically include an AI layer, an API support layer, a data layer, and a database, among others: The AI ​​layer comprises functional modules including AI decomposition and construction steps, business orchestration components, and AI assembly configuration. The AI ​​decomposition and construction step module receives natural language scenario requirements from the front end, uses NLP technology for intent recognition and semantic parsing, and automatically extracts and decomposes them into executable atomic construction steps. This transforms unstructured user natural language requirements into a machine-understandable structured sequence of operations. The business orchestration component performs sequential arrangement, dependency management, and parallel / conditional flow control on the decomposed atomic steps, forming a complete standardized construction process. This supports complex scenario step combinations and ensures the correctness and stability of the execution logic. The AI ​​assembly configuration module automatically matches the corresponding construction method, fills in parameters, and associates resources based on the scenario type and environmental parameters, completing the configuration and assembly of the execution process. This reduces manual configuration costs and automates the generation and adaptation of the construction process.

[0036] The API support layer includes functional modules for starting / stopping applications, enabling / disabling APIs, modifying configurations, one-click restoration of specified configuration values, executing scheduled jobs, application isolation and recovery, other constructors, and sending messages to notify processing results. Specifically, the application start / stop module provides control over the start / stop of the target service, switching the application to the required running state for testing. The API enable / disable module supports enabling / disabling specified interfaces, simulating test scenarios such as interface anomalies, rate limiting, and shutdown. The configuration modification module supports modifying configuration parameters to adapt to different scenario requirements. The one-click restoration of specified configuration values ​​module allows for quick rollback of specified configuration items to their original values, enabling rapid recovery of partial configurations. The scheduled job execution module triggers scheduled tasks, data synchronization, batch processing, and other jobs to construct time-dependent complex scenarios. The application isolation and recovery module performs minimal isolation of the target application, data, and configuration to avoid environmental conflicts caused by multi-person collaborative testing; isolation is lifted and the state is restored after testing. The other constructors module provides extensible atomic operation capabilities to support the construction of newly added scenarios. The message notification module is used to push the execution result (success / failure / exception) and key information to the user after the construction / resumption execution is completed.

[0037] Data Layer: This layer includes read and write databases, which are responsible for interacting with the underlying database. It enables read, write, and persistent operations on test data, configuration data, and environment backup data, providing data support for scenario construction and backup data sources for environment recovery, ensuring data consistency and traceability.

[0038] Database: It is the underlying data storage carrier of the system, used to store constructor templates, environment configuration backups, execution logs, test business data, etc., and provides data persistence capabilities to support data reading, writing and recovery operations throughout the entire process.

[0039] The overall collaborative logic of the scenario testing environment construction system in this embodiment is as follows: the user initiates a scenario construction request through the front-end interaction layer, which triggers the process through the application layer capabilities; the AI ​​layer parses, decomposes, and orchestrates the request to generate a standardized execution process; the API support layer calls atomic operation capabilities to execute environment construction with minimal impact; the data layer cooperates with the database to complete data reading, writing, and backup; after execution, feedback is sent to the front-end through the notification capabilities of the API support layer; after the test, the scenario environment recovery capability enables one-click rollback, achieving rapid reuse of the environment.

[0040] Based on the above-described scenario testing environment system architecture, this application provides a method for constructing a scenario testing environment, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the scenario testing environment construction method of this application.

[0041] In this embodiment, the method for constructing the scene testing environment includes steps S10 to S20. The following provides a detailed explanation of each step.

[0042] like Figure 2 As shown, the first embodiment of this application proposes a method for constructing a scenario testing environment. The method is applied to the background and includes: Step S10: Receive scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The front-end interface refers to the visual web page or client software interface that users interact with, used for human-computer interaction. The front-end interface provides users with a visual interaction entry point, lowering the barrier to entry for users and supporting visual input and selection.

[0043] The scene construction information is a set of structured or unstructured data input by the user.

[0044] As one implementation method, the scenario construction information includes scenario construction requirements, environmental information, and the target test scenario selected by the user.

[0045] The scenario construction requirement is the purpose and conditions of the test scenario described by the user in natural language. It is natural language text that reflects the user's testing intention, such as "simulating the scenario where the payment gateway delays for 5 seconds after the user places an order, and then the inventory deduction fails."

[0046] The environment information includes parameters such as the version, configuration, and data of the environment to be constructed. It can be the identifier or configuration information of the target environment, such as "Test Environment A" or "Pre-release Environment 2.0", and is used to specify the specific server cluster or system instance in which the construction behavior occurs.

[0047] The target test scenario selected by the user can be a preset scenario template or a custom scenario type, which serves to clarify the environment construction goals and constraints. In practice, the user can select a specific scenario category from the system's predefined scenario template library, such as "payment anomaly scenario" or "high concurrency pressure scenario," to provide a contextual framework for AI analysis.

[0048] Step S20: The background AI engine converts the scene construction information into executable scene construction steps, and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

[0049] Among them, the backend AI engine is deployed on the server side as the core of AI parsing and execution, which integrates intelligent algorithms such as natural language processing and process orchestration. Its role is to replace manual labor in completing the requirement parsing and step orchestration, thereby achieving automation.

[0050] In this embodiment, the executable scenario construction steps are atomic operation sequences that can be directly called by the system, and their function is to transform fuzzy requirements into machine-executable instructions.

[0051] The process of transforming the scenario construction information into executable scenario construction steps through the background AI engine refers to parsing and breaking down the user's fuzzy, unstructured natural language requirements into a series of clear, ordered, and computer-executable atomic operation instructions, such as "call interface A, set the value of parameter B to C; modify the status of record E in database D to F".

[0052] The background AI engine automatically executes the scenario construction steps according to preset logic to build a scenario test environment step that corresponds to the scenario construction requirements, environmental information and target test scenario. The preset logic refers to the rules that the system predefines to control the execution order, dependencies and conditions of the steps, such as sequential execution, conditional branching, parallel execution, etc., which are used to ensure the stability and reliability of the execution process.

[0053] A scenario testing environment is an integrated runtime environment that meets the requirements of testing business, encompassing applications, interfaces, configurations, and data. Its purpose is to provide the basic conditions for the execution of test cases.

[0054] This embodiment, through the above-described scheme, specifically receives scene construction information input by the user through a front-end interface. This scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. A back-end AI engine transforms this scene construction information into executable scene construction steps and automatically executes these steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene. This overall achieves an intelligent automatic scene test environment construction method. Users only need to describe their requirements and select a scene using natural language, and the back-end AI engine can automatically understand and execute complex construction tasks. This transforms the traditional, tedious process of manually writing scripts and configuring systems by professional testers into an automated and intelligent process, greatly reducing the entry barrier for complex scene testing and significantly improving the efficiency and accuracy of environment construction.

[0055] Based on the first embodiment described above, a second embodiment of this application is proposed. For the same content as the first embodiment, please refer to the first embodiment described above, and it will not be repeated here. The second embodiment of this application proposes a method for constructing a scenario testing environment, which refines the above step S20.

[0056] Specifically, step S20 above, which uses a background AI engine to convert the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene, may include: steps A1-A2: Step A1: Through the background AI engine, the scene construction information is intelligently analyzed using natural language parsing technology to extract the scene construction steps and generate an executable standardized construction process. Natural Language Processing (NLP) technology refers to using pre-trained language models, named entity recognition, intent classification, and other NLP techniques to analyze user-input scenario construction requirement text, extract scenario construction steps, that is, to identify key actions, operation objects, and parameters from the text, and its function is to convert user natural language into structured instructions.

[0057] Intelligent parsing refers to the semantic segmentation and logical recognition of the request text, which is used to accurately understand the user's constructed intent.

[0058] Extracting scene construction steps refers to extracting atomic steps such as starting and stopping applications, modifying configurations, and initializing data from natural language, with the purpose of forming programmable units.

[0059] Generate an executable, standardized construction process, that is, to arrange the extracted steps into a structured task sequence (such as a BPMN flowchart or JSON configuration) according to business logic, so that it can be recognized by a standardized execution engine.

[0060] Specifically, as one implementation method, step A1 may include: using a background AI engine, employing natural language parsing technology to perform intent recognition on the scene construction information, extracting scene construction steps and classifying tasks, and generating an executable standardized construction process through business orchestration components and AI assembly configuration.

[0061] Among them, intent recognition refers to the backend AI engine first performing semantic understanding on the scenario construction requirement text to determine the user's fundamental purpose. For example, it determines that the user's goal is to construct / restore / modify the configuration, and its role is to determine the overall task direction.

[0062] Task classification refers to categorizing the extracted construction steps, such as by application type, interface type, configuration type, data type, and scheduled task type. This facilitates orchestration and isolation, as well as subsequent differentiated processing.

[0063] After the background AI engine uses natural language processing technology to identify the intent of the scene construction information, extract the scene construction steps, and classify the tasks, an executable standardized construction process is generated through a business orchestration component and AI assembly configuration. The business orchestration component is a process engine responsible for defining the dependencies and execution order between steps. AI assembly configuration refers to the automatic generation of a specific workflow configuration file based on the task classification results and preset best practice templates. The combination of these two processes assembles a categorized list of tasks into a complete, driveable, and standardized process.

[0064] Step A2: Match the corresponding construction method according to the preset strategy, and automatically execute the standardized construction process within the minimum impact range to build a scenario test environment corresponding to the scenario construction requirements, environmental information and standard test scenario.

[0065] Among them, the preset strategy is a rule base defined by the system, which is used to map the parsed abstract steps to specific, executable program methods or API calls. For example, the "modify configuration" step may correspond to the "call configuration center API" method.

[0066] In this embodiment, the corresponding construction method is matched according to a preset strategy, and the standardized construction process is automatically executed within the minimum impact range. Here, minimum impact range refers to the use of isolation techniques, such as container isolation, namespace isolation, and data snapshots, during the construction steps to ensure that the construction behavior only affects the application, interface, or data specified in the current test task, without polluting or disrupting the normal operation of other users or services in the same environment.

[0067] As one implementation method, the background engine matches the corresponding constructor method according to a preset strategy, calls the constructor method, and performs isolated execution of the application, interface, configuration and related data.

[0068] Isolating an application means starting a separate application instance for the current test scenario (such as through container technology), or performing traffic coloring and routing isolation on an existing application so that it only receives traffic for this test.

[0069] Isolating interfaces refers to mocking or stubbing a specified API interface to make it return a preset response, while other interfaces retain their original logic, thus achieving precise intervention at the interface level.

[0070] Isolating configurations means modifying only the target configuration item while keeping other configurations unchanged, creating an independent configuration namespace for the current test scenario. The modified configurations only take effect in this namespace and do not affect the global configuration.

[0071] Isolating relevant data means preparing an independent set of test data for the test scenario through methods such as database snapshots, shadow tables, and data copies, so that data modifications during the test will not affect the master data.

[0072] Isolated execution refers to execution in an independent resource space, leaving no side effects after execution, and its purpose is to support parallel operations by multiple people.

[0073] Through the above steps, the specific technical means to minimize the impact have been clarified, namely, fine-grained isolation from four core dimensions: application, interface, configuration, and data. This three-dimensional isolation strategy is the key technical foundation for ensuring a stable testing environment, avoiding conflicts, and achieving rapid recovery and efficient reuse.

[0074] This embodiment, through the above-mentioned scheme, specifically by introducing intelligent parsing and strategy matching mechanisms, realizes the automated conversion from natural language to precise operation instructions. The core technical effect is to achieve "minimized impact" in the construction execution, effectively solve the environmental conflict problem in multi-person collaborative testing, ensure the stability and reusability of the test environment, and enable multiple test tasks to be carried out safely in parallel on the same basic environment.

[0075] Based on the first or second embodiment described above, a third embodiment of this application is proposed. For content that is the same as the first or second embodiment, please refer to the first or second embodiment described above, and will not be repeated here. The third embodiment of this application proposes a method for constructing a scenario testing environment, which further refines the above step S20.

[0076] Specifically, in this embodiment, the method for constructing the scene testing environment may further include: During the construction and execution process, API support layer capabilities are invoked to perform at least one of the following operations: start or stop the application, enable or disable the API, modify configuration, restore key-value pairs, execute scheduled jobs, and perform application isolation and data recovery.

[0077] In this embodiment, various operations are implemented by calling the capabilities of the API support layer during the construction and execution process. The API support layer is an abstract service layer at the bottom of the system, which encapsulates various operation APIs for infrastructure, middleware, and application services.

[0078] In this embodiment, its specific capabilities include, but are not limited to: Start and stop applications: Start or stop specific application service instances by calling the API of the container platform (such as Kubernetes) or service management platform.

[0079] Enable / disable APIs: By calling the API gateway's management API, you can dynamically enable or disable a specific API endpoint to simulate an unavailable interface.

[0080] Modify configuration: Dynamically modify the application's runtime configuration parameters by calling the API of the configuration center (such as Apollo, Nacos).

[0081] Key-value recovery: Specifically refers to calling the API of the relevant service after testing to restore the modified data in a specific key-value store (such as Redis) to its original value. Its purpose is to quickly roll back partial configurations.

[0082] Execute scheduled jobs: Call the API of a task scheduling platform (such as XXL-JOB) to trigger or pause the execution of a scheduled task to simulate an event triggered at a specific time.

[0083] Application isolation and data recovery: Enable API calls, such as calling container platform APIs to create isolated environments, and calling database tool APIs to create data snapshots or perform data rollbacks.

[0084] Therefore, the underlying service layer provides a unified atomic operation interface through the API support layer, encapsulating the underlying capabilities and providing standard calls to the upper layers.

[0085] Through the above steps, this embodiment reveals the specific implementation method of the system's automatic execution capability. Through a unified API support layer, the system can manipulate complex underlying resources in a standardized and programmable manner, thereby transforming abstract construction steps into concrete, atomically executable operation instructions. This is the core guarantee for achieving highly automated construction and recovery.

[0086] Based on the first to third embodiments described above, a fourth embodiment of this application is proposed. For the same content as the first to third embodiments, please refer to the first to third embodiments described above, which will not be repeated here. The fourth embodiment of this application proposes a method for constructing a scene testing environment, which further refines the above-mentioned method for constructing a scene testing environment.

[0087] Specifically, in this embodiment, the method for constructing the scene testing environment may further include: After the scenario testing environment is constructed, the construction results are fed back to the user through the front-end interface.

[0088] In this embodiment, after the scenario testing environment is constructed, the construction result is fed back to the user through the front-end interface. Specifically, after the scenario testing environment is constructed, the back-end pushes the construction result (success / failure, environment access address, key change logs, etc.) to the front-end interface, and the front-end interface feeds back the construction result to the user, realizing closed-loop notification.

[0089] The front-end interface supports pop-ups, messages, and status lists for feedback, thereby improving user experience and observability.

[0090] Through the above steps, this embodiment improves the system's user interaction and scalability, and real-time feedback enhances user experience and process controllability.

[0091] Furthermore, in this embodiment, the method for constructing the scenario testing environment may further include: The front-end interface executes at least one of the following actions: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

[0092] Specifically, users can manage the library of system-callable constructors (i.e., atomic operations or step templates) through the front-end interface, including adding, deleting, modifying, batch importing, and batch exporting these methods.

[0093] Through the above steps, this embodiment improves the management function of the construction method, transforming the system from a closed automated tool into an open, continuously accumulating and optimized test asset platform, and realizing the system's user interaction and scalability.

[0094] Furthermore, in this embodiment, the method for constructing the scenario testing environment may further include: After the test is completed, the test environment is restored using the one-click recovery function and a preset recovery strategy.

[0095] Specifically, after testing is complete, users can trigger a one-click restore function. Once triggered, the system automatically executes a series of cleanup operations that are the opposite of or correspond to the construction steps, based on a preset restore strategy. For example, if the construction step is "modify configuration X to Y", then the restore step is "restore configuration X to its original value". The preset restore strategy can be generated along with the construction process or recorded during construction to guide the restore process.

[0096] Specifically, the one-click recovery function includes at least one of the following: automatically rolling back configuration parameters, resetting the database, and removing application isolation to restore the scenario test environment to its initial stable state. The functions are described below: Automatic rollback of configuration parameters: The system automatically calls the configuration center API to restore all configuration items that have been modified in this test to their values ​​before the test started.

[0097] Reset Database: The system calls the database tool to clean up the modified, deleted, or added test data. This can be done by restoring a data snapshot, executing a rollback script, or cleaning up data marked for specific tests.

[0098] Remove application isolation: The system calls the resource management API to destroy the independent container instance created for this test, or remove the traffic coloring rule, and reintegrate the isolated application instance or traffic into the regular service pool.

[0099] Restoring the scenario testing environment to its initial stable state: This involves a series of atomic restoration operations to ensure that the entire target testing environment remains consistent with its state before the test began, allowing it to be immediately used by other tasks and thus improving environment turnaround efficiency.

[0100] Through the above steps, this embodiment provides an automatic reset mechanism for the scenario testing environment. One-click recovery ensures that no matter what happens during the testing process, the environment can quickly and reliably return to its initial stable state. This is crucial for keeping the testing environment clean, preparing for the next test, and preventing online failures caused by environmental residue issues. It constitutes a complete, safe, and reliable automated testing environment lifecycle management solution, which greatly improves the stability and iteration efficiency of the testing environment.

[0101] Reference Figure 3 , Figure 3 This is a flowchart illustrating the fifth embodiment of the scenario testing environment construction method of this application.

[0102] In this embodiment, the method for constructing the scene testing environment includes steps S100 to S200. The following provides a detailed explanation of each step.

[0103] like Figure 3As shown, the fifth embodiment of this application proposes a method for constructing a scenario testing environment. The method is applied to the front end and includes: Step S100: Receive scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. Step S200: The scene construction information is sent to the backend, where the AI ​​engine converts the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

[0104] Specifically, the front-end interface can refer to the visual web page or client software interface that users interact with, used for human-computer interaction. The front-end interface provides users with a visual interaction entry point, its purpose being to lower the barrier to entry for users and support visual input and selection.

[0105] The scene construction information is a set of structured or unstructured data input by the user.

[0106] As one implementation method, the scenario construction information includes scenario construction requirements, environmental information, and the target test scenario selected by the user.

[0107] The scenario construction requirements are the test scenario objectives and conditions described by the user in natural language; they are natural language text reflecting the user's testing intent. Environment information includes parameters such as the version, configuration, and data of the environment to be constructed. This can be an identifier or configuration information of the target environment, such as "Test Environment A" or "Pre-release Environment 2.0," used to specify the specific server cluster or system instance where the construction behavior occurs. The target test scenario selected by the user is either a preset scenario template or a custom scenario type, its purpose being to clarify the environment construction objectives and constraints. In practice, the user can select a specific scenario category from the system's predefined scenario template library, such as "Payment Anomaly Scenario" or "High Concurrency Pressure Scenario," providing a contextual framework for AI analysis.

[0108] Among them, the backend AI engine is deployed on the server side as the core of AI parsing and execution, which integrates intelligent algorithms such as natural language processing and process orchestration. Its role is to replace manual labor in completing the requirement parsing and step orchestration, thereby achieving automation.

[0109] In this embodiment, the executable scenario construction steps are atomic operation sequences that can be directly called by the system, and their function is to transform fuzzy requirements into machine-executable instructions.

[0110] The process of transforming the scenario construction information into executable scenario construction steps through the background AI engine refers to parsing and breaking down the user's fuzzy, unstructured natural language requirements into a series of clear, ordered, and computer-executable atomic operation instructions, such as "call interface A, set the value of parameter B to C; modify the status of record E in database D to F".

[0111] The background AI engine automatically executes the scenario construction steps according to preset logic to build a scenario test environment step that corresponds to the scenario construction requirements, environmental information and target test scenario. The preset logic refers to the rules that the system predefines to control the execution order, dependencies and conditions of the steps, such as sequential execution, conditional branching, parallel execution, etc., which are used to ensure the stability and reliability of the execution process.

[0112] A scenario testing environment is an integrated runtime environment that meets the requirements of testing business, encompassing applications, interfaces, configurations, and data. Its purpose is to provide the basic conditions for the execution of test cases.

[0113] This embodiment, through the above-described scheme, specifically receives scene construction information input by the user through a front-end interface. This scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The scene construction information is then sent to the backend, where an AI engine transforms it into executable scene construction steps and automatically executes these steps according to preset logic. This constructs a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene. Overall, this implements an intelligent automatic scene test environment construction method. Users only need to describe their requirements and select a scene using natural language, and the backend AI engine can automatically understand and execute complex construction tasks. This transforms the traditional, tedious process of manually writing scripts and configuring systems by professional testers into an automated and intelligent process, greatly reducing the entry barrier for complex scene testing and significantly improving the efficiency and accuracy of environment construction.

[0114] Furthermore, the method also includes: after the scenario testing environment is constructed, providing feedback on the construction results to the user through the front-end interface.

[0115] In this embodiment, after the scenario testing environment is constructed, the construction result is fed back to the user through the front-end interface. Specifically, after the scenario testing environment is constructed, the back-end pushes the construction result (success / failure, environment access address, key change logs, etc.) to the front-end interface, and the front-end interface feeds back the construction result to the user, realizing closed-loop notification.

[0116] Through the above steps, this embodiment improves the system's user interaction and scalability, and real-time feedback enhances user experience and process controllability.

[0117] Furthermore, the method also includes: performing at least one of the following operations through the front-end interface: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

[0118] Specifically, users can manage the library of system-callable constructors (i.e., atomic operations or step templates) through the front-end interface, including adding, deleting, modifying, batch importing, and batch exporting these methods.

[0119] Through the above steps, this embodiment improves the management function of the construction method, transforming the system from a closed automated tool into an open, continuously accumulating and optimized test asset platform, and realizing the system's user interaction and scalability.

[0120] The scenario testing environment construction method proposed in this embodiment uses natural language processing technology to intelligently analyze complex scenario construction requirements, automatically extract and accurately classify construction steps, and, combined with preset strategies, automatically match and call the corresponding construction method in the background to complete the intelligent construction of the scenario environment. This method significantly reduces the complexity of test scenario construction, improves testing efficiency, standardizes scenario construction methods, lowers the testing threshold, and effectively reduces environmental conflicts during multi-person collaboration. In addition, such as Figure 4 As shown, this application also proposes a scenario testing environment construction device, the device comprising: The receiving module 10 is used to receive scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information and target test scene selected by the user. The construction module 20 is used to convert the scene construction information into executable scene construction steps through the background AI engine, and automatically execute the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

[0121] The scenario testing environment construction apparatus provided in this application, employing the scenario testing environment construction method in the above embodiments, significantly reduces the complexity of test scenario construction, improves testing efficiency, and simultaneously standardizes the scenario construction method, lowering the testing threshold and effectively reducing environmental conflicts during multi-person collaboration. Compared with the prior art, the beneficial effects of the scenario testing environment construction apparatus provided in this application are the same as those of the scenario testing environment construction method provided in the above embodiments, and other technical features in the scenario testing environment construction apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0122] In addition, to achieve the above objectives, this application also proposes a scene testing environment construction device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the scene testing environment construction method described above.

[0123] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a scene testing environment construction device suitable for implementing embodiments of this application. The scene testing environment construction device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The scenario test environment construction device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0124] like Figure 5 As shown, the scene testing environment construction device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the scene testing environment construction device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the scenario testing environment construction device to communicate wirelessly or wiredly with other devices to exchange data. Although the scenario testing environment construction device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0125] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0126] The scenario testing environment construction device provided in this application, employing the scenario testing environment construction method in the above embodiments, significantly reduces the complexity of test scenario construction, improves testing efficiency, and simultaneously standardizes the scenario construction method, lowering the testing threshold and effectively reducing environmental conflicts during multi-person collaboration. Compared with the prior art, the beneficial effects of the scenario testing environment construction device provided in this application are the same as those of the scenario testing environment construction method provided in the above embodiments, and other technical features in this scenario testing environment construction device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0127] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0128] In addition, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the scenario testing environment construction method described above.

[0129] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0130] The aforementioned computer-readable storage medium may be included in the scenario testing environment construction device; or it may exist independently and not assembled into the scenario testing environment construction device.

[0131] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the scenario testing environment construction device, the scenario testing environment construction device: receives scenario construction information input by the user through a front-end interface, the scenario construction information including: scenario construction requirements, environmental information, and the target test scenario selected by the user; converts the scenario construction information into executable scenario construction steps through a background AI engine, and automatically executes the scenario construction steps according to preset logic to construct a scenario testing environment corresponding to the scenario construction requirements, environmental information, and target test scenario.

[0132] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0134] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0135] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described scenario testing environment construction method. This significantly reduces the complexity of test scenario construction, improves testing efficiency, and standardizes the scenario construction method, lowers the testing threshold, and effectively reduces environmental conflicts during multi-person collaboration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the scenario testing environment construction method provided in the above embodiments, and will not be repeated here.

[0136] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the scenario testing environment construction method described above.

[0137] The computer program product provided in this application significantly reduces the complexity of test scenario construction, improves testing efficiency, and standardizes the scenario construction method, lowering the testing threshold and effectively reducing environmental conflicts during multi-person collaboration. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the scenario test environment construction method provided in the above embodiments, and will not be repeated here.

[0138] One or more technical solutions proposed in this application have at least the following technical effects: The method receives scene construction information input by the user through a front-end interface. This information includes scene construction requirements, environmental information, and the target test scene selected by the user. A back-end AI engine transforms this information into executable scene construction steps and automatically executes these steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene. This application proposes a scene construction method based on natural language input. Using intelligent parsing technology, the method processes the natural language input by the user to automatically extract and accurately classify scene construction steps. By automatically executing these steps according to preset logic, the method enables intelligent construction of the scene test environment. This approach not only improves the efficiency of testing complex scenes but also lowers the testing threshold and effectively reduces environmental conflicts during multi-user collaboration. Specifically, it can intelligently construct scene environments by combining preset strategies, while supporting minimal control over the impact range of construction steps. It supports one-click construction of complex scenes, significantly improving construction efficiency and quality while lowering the testing threshold. Minimizing the impact range of construction steps effectively reduces environmental conflicts and improves the stability and reusability of the test environment.

[0139] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for constructing a scenario testing environment, characterized in that, The method is applied in the background, and the method includes: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The background AI engine transforms the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

2. The method as described in claim 1, characterized in that, The steps of converting the scene construction information into executable scene construction steps through a background AI engine, and automatically executing the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information, and target test scene include: The background AI engine uses natural language processing technology to intelligently analyze the scene construction information, extract the scene construction steps, and generate an executable standardized construction process. According to the preset strategy, the corresponding construction method is matched, and the standardized construction process is automatically executed within the minimum impact range to build a scenario test environment corresponding to the scenario construction requirements, environmental information and standard test scenario.

3. The method as described in claim 2, characterized in that, The steps of using a background AI engine and natural language processing technology to intelligently analyze the scene construction information, extract scene construction steps, and generate an executable standardized construction process include: Through the background AI engine, natural language parsing technology is used to identify the intent of the scene construction information, extract the scene construction steps and classify the tasks. Then, an executable standardized construction process is generated through business orchestration components and AI assembly configuration.

4. The method as described in claim 2, characterized in that, The step of automatically executing the standardized construction process within the minimum impact range by matching the corresponding construction method according to the preset strategy includes: According to the preset strategy, the corresponding constructor method is matched and called to isolate and execute the application, interface, configuration and related data.

5. The method as described in claim 4, characterized in that, The method further includes: During the construction and execution process, API support layer capabilities are invoked to perform at least one of the following operations: start or stop the application, enable or disable the API, modify configuration, restore key-value pairs, execute scheduled jobs, and perform application isolation and data recovery.

6. The method as described in claim 1, characterized in that, The method further includes: After the scenario testing environment is constructed, the construction results are fed back to the user through the front-end interface; and / or The front-end interface executes at least one of the following actions: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

7. The method as described in claim 1, characterized in that, The method further includes: After the test is completed, the test environment is restored using the one-click recovery function and a preset recovery strategy.

8. The method as described in claim 7, characterized in that, The one-click recovery function includes at least one of the following: automatically rolling back configuration parameters, resetting the database, and removing application isolation to restore the scenario test environment to its initial stable state.

9. A method for constructing a scenario testing environment, characterized in that, The method is applied to the front end, and the method includes: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The scenario construction information is sent to the backend, where the AI ​​engine converts the scenario construction information into executable scenario construction steps and automatically executes the scenario construction steps according to preset logic to build a scenario test environment corresponding to the scenario construction requirements, environmental information, and target test scenario.

10. The method as described in claim 9, characterized in that, The method further includes: After the scenario testing environment is constructed, the construction results are fed back to the user through the front-end interface; and / or The front-end interface executes at least one of the following actions: adding, deleting, modifying, batch importing, and batch exporting the constructor method.

11. A device for constructing a scenario testing environment, characterized in that, The device includes: The system receives scene construction information input by the user through the front-end interface. The scene construction information includes: scene construction requirements, environmental information, and the target test scene selected by the user. The background AI engine transforms the scene construction information into executable scene construction steps and automatically executes the scene construction steps according to preset logic to build a scene test environment corresponding to the scene construction requirements, environmental information and target test scene.

12. A scenario testing environment construction device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the scenario testing environment construction method as described in any one of claims 1 to 10.

13. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the scenario testing environment construction method as described in any one of claims 1 to 10.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the scenario testing environment construction method as described in any one of claims 1 to 10.

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