Test environment configuration method and device, equipment and storage medium
By generating, verifying and optimizing test environment configuration files, the problems of low configuration efficiency and high error rate in traditional methods are solved, and efficient and accurate test environment configuration is achieved, which is suitable for complex on-board software development projects.
Patent Information
- Application Number
- CN202411750903.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-02
- Publication Date
- 2025-03-11
AI Technical Summary
The traditional simulation test environment development and construction methods are time-consuming and labor-intensive, and error-prone. In addition, automated script maintenance requires code development capabilities, poor universality and flexibility, making it difficult to improve configuration efficiency and reduce error incidence.
By generating a model based on user task requests, model input data, and target configuration files, the target configuration file is obtained; verify the verification results based on the model input data and target configuration files, and determine the test environment configuration results based on the verification results.
It achieves the improvement of the configuration efficiency of the test environment while reducing the incidence of configuration errors. It is universal and accurate. It is suitable for different versions of the communication matrix and demand scenarios. Engineers who have taken over the work can also quickly and efficiently simulate the configuration of the test environment.
Smart Images
Figure CN119597678A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of software testing technology, and in particular to a test environment configuration method, device, equipment and storage medium. Background Art
[0002] As the complexity of vehicle software development projects continues to increase, the number of controller communication and interaction signals increases, and the number of functions involved increases. How to efficiently and accurately develop and build a simulation test environment to help test engineers better complete test execution tasks, and even quickly complete automated testing, has become an important topic. Traditional simulation test environment development and construction methods usually rely on manual work, which is not only time-consuming and labor-intensive, but also prone to errors. Alternatively, automated scripts are used. This method requires certain code development capabilities, and requires continuous maintenance of automated scripts based on changes in requirements and communications, which has poor universality and flexibility. Therefore, how to reduce the incidence of environmental configuration errors while improving configuration efficiency has become an urgent problem to be solved. Summary of the invention
[0003] The main purpose of this application is to provide a test environment configuration method, device, equipment and storage medium, aiming to solve the technical problem of how to improve configuration efficiency while reducing the occurrence rate of environment configuration errors.
[0004] To achieve the above objectives, the present application proposes a test environment configuration method, the test environment configuration method comprising: Generate a model according to the user task request, model input data and target configuration file, and obtain the corresponding target configuration file; Perform verification according to the model input data and the target configuration file to obtain a target file verification result; The test environment configuration result is determined according to the target file verification result.
[0005] In one embodiment, before the step of generating a model according to the user task request, the model input data and the target configuration file to obtain the corresponding target configuration file, the step further includes: Perform data analysis based on user task requests and determine the corresponding user uploaded data; Preprocessing the user uploaded data to determine the preprocessed user uploaded data; The model input data is obtained based on the preprocessed user uploaded data.
[0006] In one embodiment, before the step of generating a model according to the user task request, the model input data and the target configuration file to obtain the corresponding target configuration file, the step further includes: Perform model evaluation on multiple configuration file generation models according to the evaluation data set to obtain the target evaluation score results; Determine a corresponding initial configuration file generation model according to the target evaluation scoring result; The initial configuration file generation model is trained according to the model input data to obtain a target configuration file generation model.
[0007] In one embodiment, the step of performing model evaluation on multiple configuration file generation models according to the evaluation data set to obtain target evaluation scoring results includes: Input the evaluation data sets into each configuration file generation model for multi-dimensional evaluation, and obtain the output configuration files of each configuration file generation model; Scoring the output configuration files of each configuration file generation model according to a preset model scoring standard, and obtaining the model scoring result corresponding to each configuration file generation model; The model scoring results corresponding to the models generated by each configuration file are summarized to obtain the target evaluation scoring results.
[0008] In one embodiment, the step of performing verification according to the model input data and the target configuration file to obtain a target file verification result includes: Extracting data from the target configuration file to obtain key data of the file; Compare the key data of the file with the model input data to obtain a target data comparison result; A target file verification result is obtained according to the target data comparison result.
[0009] In one embodiment, the step of determining the test environment configuration result according to the target file verification result includes: When the target file verification result is a verification failure result, generating corresponding model optimization suggestion information according to the target file verification result; The model optimization suggestion information is sent, and a test environment configuration result is determined to be a configuration failure result.
[0010] In one embodiment, after the step of determining the test environment configuration result according to the target file verification result, the method further includes: When the test environment configuration result is a configuration success result, a user update request is obtained; A test environment is configured according to the user update request and the target configuration file generation model.
[0011] In addition, to achieve the above purpose, the present application also proposes a test environment configuration device, the test environment configuration device comprising: A generation module is used to generate a model according to a user task request, model input data and a target configuration file to obtain a corresponding target configuration file; A verification module, used to perform verification according to the model input data and the target configuration file to obtain a target file verification result; A configuration module is used to determine the test environment configuration result according to the target file verification result.
[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a test environment configuration device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the test environment configuration method described above.
[0013] In addition, to achieve the above objectives, the present application also proposes a storage medium, which 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, the steps of the test environment configuration method described above are implemented.
[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the test environment configuration method described above are implemented.
[0015] This application generates a model according to user task requests, model input data, and a target configuration file to obtain a corresponding target configuration file; performs verification according to the model input data and the target configuration file to obtain a target file verification result; and determines the test environment configuration result according to the target file verification result. By analyzing the communication matrix and requirements, a corresponding test environment configuration file is generated. And according to the generated results, the generated file is checked. It can meet the communication matrix and requirements of different versions and the corresponding test environment configuration file generation scenarios, and has universality and accuracy. Through the large model dialogue method, a better user experience can be achieved, so that engineers who initially take over related work can also quickly and efficiently configure the simulation test environment to improve efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0018] Figure 1 A flowchart of the first embodiment of the test environment configuration method of the present application is provided; Figure 2 A flowchart of the second embodiment of the test environment configuration method of the present application is provided; Figure 3 A brief flowchart of the test environment configuration method provided in Example 1 of the present application; Figure 4 A schematic diagram of the module structure of the test environment configuration device according to an embodiment of the present application; Figure 5 A schematic diagram of the device structure of the hardware operating environment involved in the test environment configuration method in the embodiment of the present application.
[0019] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.
[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.
[0022] The main solution of the embodiment of the present application is: generate a model according to user task requests, model input data and target configuration files to obtain a corresponding target configuration file; perform verification according to the model input data and the target configuration file to obtain a target file verification result; determine a test environment configuration result according to the target file verification result.
[0023] As the complexity of vehicle software development projects continues to increase, the number of controller communication and interaction signals increases, and the number of functions involved increases. How to efficiently and accurately develop and build a simulation test environment to help test engineers better complete test execution tasks, and even quickly complete automated testing, has become an important topic. Traditional simulation test environment development and construction methods usually rely on manual work, which is not only time-consuming and labor-intensive, but also prone to errors. Alternatively, automated scripts are used. This method requires certain code development capabilities, and requires continuous maintenance of automated scripts based on changes in requirements and communications, which has poor universality and flexibility. Therefore, how to reduce the incidence of environmental configuration errors while improving configuration efficiency has become an urgent problem to be solved.
[0024] This application generates a model according to user task requests, model input data, and a target configuration file to obtain a corresponding target configuration file; performs verification according to the model input data and the target configuration file to obtain a target file verification result; and determines the test environment configuration result according to the target file verification result. By analyzing the communication matrix and requirements, a corresponding test environment configuration file is generated. And according to the generated results, the generated file is checked. It can meet the communication matrix and requirements of different versions and the corresponding test environment configuration file generation scenarios, and has universality and accuracy. Through the large model dialogue method, a better user experience can be achieved, so that engineers who initially take over related work can also quickly and efficiently configure the simulation test environment to improve efficiency.
[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a test environment configuration device capable of realizing the above functions, etc. The following takes the test environment configuration device as the execution subject as an example to illustrate this embodiment and the following embodiments.
[0026] Based on this, the present application embodiment provides a test environment configuration method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the test environment configuration method of the present application.
[0027] In this embodiment, the test environment configuration method includes steps S10 to S30: Step S10, generating a model according to the user task request, model input data and target configuration file, and obtaining a corresponding target configuration file; It should be noted that this embodiment automatically analyzes the communication matrix and requirements to generate a corresponding test environment configuration file. And according to the generation result, the generated file is checked. It can meet the communication matrix and requirements of different versions and generate test environment configuration files, which is universal and accurate. Through the large model dialogue method, a better user experience can be achieved, so that engineers who initially take over related work can also quickly and efficiently configure the simulation test environment to improve efficiency.
[0028] It can be understood that a user task request refers to a task issued by a user using natural language, such as generating a CAPL (CAN Application Programming Language) simulation code and its corresponding system variable file through a CAN (Controller Area Network) communication matrix. Model input data refers to data used to generate a configuration file in the input model, such as a CAN communication matrix. A target configuration file generation model refers to a model used to generate a corresponding configuration file based on user task requirements and input data. A target configuration file refers to a generated file used to store system, application or device settings and parameters.
[0029] In a specific implementation, data for generating a configuration file in a corresponding input model is determined according to a task published by a user using natural language, and then the data for generating a configuration file in the input model is input into a configuration file generation model, and the corresponding configuration file is generated in combination with a task published by a user using natural language. For example, a user publishes a task of generating a CAPL simulation code and its corresponding system variable file through a CAN communication matrix using natural language, and the CAN communication matrix is determined as model input data through analysis of the published task, and then the CAN communication matrix is input into the configuration file generation model, and then a CAPL simulation code and its corresponding system variable file are generated through the CAN communication matrix to obtain the corresponding target configuration file.
[0030] In a feasible implementation manner, step S10 may include steps A11 to A13: Step A11, performing data analysis according to the user task request to determine the corresponding user uploaded data; It should be noted that user uploaded data refers to data that needs to be uploaded by the user.
[0031] In a specific implementation, data analysis is performed on a task posted by a user using natural language to identify the data that needs to be uploaded by the user, so as to determine the corresponding user uploaded data. For example, the task posted by the user is to generate CAPL simulation code based on the CAN communication matrix, so it can be determined that the CAN communication matrix is the data that needs to be uploaded by the user, that is, the user uploaded data.
[0032] Step A12, preprocessing the user uploaded data to determine the preprocessed user uploaded data; It is understandable that in order to ensure the accuracy of the model output results, the data that needs to be uploaded by the user is preprocessed to obtain the preprocessed user uploaded data, where the goal of the preprocessing stage is to ensure the quality of the input data to improve the efficiency and accuracy of subsequent processing. This step includes but is not limited to deleting blank lines in the document, clearing extra spaces, removing duplicate records, repairing or deleting erroneous data entries, etc. For certain types of documents (such as code documents), standardized formats or structures may also be required to better meet the input requirements of the model.
[0033] Step A13, obtaining model input data based on the preprocessed user uploaded data.
[0034] In a specific implementation, after the preprocessing of the data required to be uploaded by the user is completed, it is indicated that the preprocessed data required to be uploaded by the user can ensure the accuracy of the model output result, and then the preprocessed user uploaded data is determined as the model input data.
[0035] Step S20, performing verification according to the model input data and the target configuration file to obtain a target file verification result; It can be understood that the target file verification result refers to the result of verifying whether there is any problem with the generated configuration file.
[0036] In the specific implementation, the model input data and the generated configuration file are compared and verified to determine whether there are any problems with the generated configuration file. That is, the generated configuration file is compared with the input data provided by the user item by item to ensure that all necessary information is correctly extracted and converted.
[0037] In a feasible implementation, step S20 may include steps A21 to A23: Step A21, extracting data from the target configuration file to obtain key data of the file; It can be understood that the file key data refers to the key data in the generated configuration file.
[0038] In a specific implementation, key data of the file is obtained by extracting key data from the generated configuration file, and then comparison and verification are performed based on the extracted key data to improve the verification efficiency of the file.
[0039] Step A22, comparing the key data of the file with the model input data to obtain a target data comparison result; It can be understood that the target data comparison result refers to the result of whether the key data of the file corresponds to the model input data.
[0040] In a specific implementation, the key data extracted from the generated configuration file is compared with the data in the input model used to generate the configuration file, thereby determining whether the key data of the file corresponds to the model input data, i.e., the target data comparison result. For example, CAPL simulation code is generated based on the CAN communication matrix, and the data of the generated configuration file is compared with the CAN communication matrix to determine whether the key information in the CAN communication matrix is converted into CAPL simulation code.
[0041] Step A23, obtaining a target file verification result according to the target data comparison result.
[0042] In the specific implementation, when the target data comparison result is a correct comparison result, it indicates that the generated configuration file is correct, and then the target file verification result is determined to be a passed verification result; when the target data comparison result is an incorrect comparison result, it indicates that there is a problem with the verification result, and then the target file verification result is determined to be a failed verification result, and optimization suggestions are generated, and feedback is given to the user through the interface for communicating with the user through the big model.
[0043] Step S30, determining the test environment configuration result according to the target file verification result.
[0044] It can be understood that the test environment configuration result refers to the result of whether the test environment configuration is completed.
[0045] In the specific implementation, when the target file verification result is a verification pass result, it indicates that the test environment configuration is complete, that is, the test environment configuration result is a configuration completion result; when the target file verification result is a verification failure result, it indicates that the generated configuration file is incorrect and needs to be optimized and regenerated, thereby determining that the current test environment configuration result is a configuration incomplete result.
[0046] In a feasible implementation, step S30 may include steps A31-A32: Step A31, when the target file verification result is a verification failure result, generating corresponding model optimization suggestion information according to the target file verification result; It can be understood that the model optimization suggestion information refers to providing specific optimization suggestions to users for problems found during the verification process.
[0047] In a specific implementation, when the verification result of the target file is a verification failure result, it indicates that there is a problem with the generated configuration file, and then specific optimization suggestions are provided to the user for the problems found during the verification process, that is, model optimization suggestion information is generated.
[0048] Step A32, sending the model optimization suggestion information, and determining that the test environment configuration result is a configuration failure result.
[0049] In a specific implementation, the generated model optimization suggestion information is fed back to the user through the interface for the big model to communicate with the user, and the test environment configuration result is determined to be a configuration failure result, wherein the interface for the big model to communicate with the user can be a local application or a web page.
[0050] In a feasible implementation manner, step S30 may include steps B31 to B32: Step B31, when the test environment configuration result is a configuration success result, obtaining a user update request; It can be understood that the user update request refers to a new task request supplemented by the user.
[0051] In the specific implementation, when the test environment configuration result is a successful configuration result, it indicates that the generated configuration file is verified correctly, but the user has new task content, and then the new task request supplemented by the user is obtained through the interface for communicating with the user through the big model, that is, the user update request.
[0052] Step B32: Generate a model to configure the test environment according to the user update request and the target configuration file.
[0053] In a specific implementation, when a user has new task content, it indicates that the evaluation task content needs to be re-understood and the configuration file generation process needs to be executed, and then the test environment is reconfigured according to the new task request supplemented by the user and the model used to generate the corresponding configuration file.
[0054] It should be noted that this embodiment uses S1. Large model interaction module: S1.1 Original large model selection: According to the main characteristics of the task, the large model needs to have strong document processing capabilities, including but not limited to Excel, PDF, Word, XML, CAPL code, C code, Python code documents. Develop an evaluation set and method to evaluate and score each dimension of the large model, and select a model with a high score. S1.2 Document data preprocessing: Clean the data to be processed and remove invalid information, such as null values, duplicates, etc. Ensure the validity and consistency of the data. S1.3 Large model training: Use the data processed by S1.2 to train the large model selected by S1.1 so that it can completely and correctly obtain valid information data and return it. S1.4 Provide an interface for a large model to communicate with the user, which can be a local application or a web page.
[0055] S2. Configuration file generation module: S2.1 Use the large model trained by S1.3 to issue a task to it using natural language through S1.4, such as generating a CAPL simulation code and its corresponding system variable file through the CAN communication matrix. S2.3 When the large model receives the task, it can generate the corresponding configuration file based on the input requirements and data documents, and provide feedback to the user through S1.4.
[0056] S3. Verification and suggestion module: S3.1 After executing the configuration file generation task in S2.3, it enters the verification phase. The verification module will verify the configuration file generated by S2.3 and the input data document, and feedback the verification results to the user through S1.4. S3.2 If there is a problem with the verification result of S3.1, optimization suggestions will be provided to the user through S1.4. S3.3 If the user adopts the suggestions provided by S3.2, S2 needs to re-understand the evaluation task content and execute it. S3.4 If the user adds new task content, S2 needs to re-understand the evaluation task content and execute it. S3.5 If the user adopts the task results fed back by S3.1, the relevant result files / data will be returned to the big model through S1.3 for model training to improve the capabilities and understanding accuracy of the big model.
[0057] This embodiment generates a model according to the user task request, model input data and target configuration file to obtain the corresponding target configuration file; performs verification according to the model input data and the target configuration file to obtain the target file verification result; and determines the test environment configuration result according to the target file verification result. By analyzing the communication matrix and requirements, a corresponding test environment configuration file is generated. And according to the generated result, the generated file is checked. It can meet the communication matrix and requirements of different versions and the corresponding test environment configuration file generation scenarios, and has universality and accuracy. Through the large model dialogue method, a better user experience can be achieved, so that engineers who initially take over related work can also quickly and efficiently configure the simulation test environment to improve efficiency.
[0058] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 Before step S10, the test environment configuration method further includes steps S01 to S03: Step S01, performing model evaluation on multiple configuration file generation models according to the evaluation data set to obtain target evaluation scoring results; It can be understood that the evaluation data set refers to the data set consisting of representative questions or tasks in each evaluation dimension, and the target evaluation scoring result refers to the evaluation scoring result of each model.
[0059] In the specific implementation, the data sets consisting of representative questions or tasks in each evaluation dimension are respectively input into multiple configuration file generation models, and then the output results of each configuration file generation model are obtained, and then the output results of each model are evaluated and scored to determine the target evaluation score results.
[0060] In a feasible implementation, step S01 may include steps A011 to A013: Step A011, inputting the evaluation data set into each configuration file generation model for multi-dimensional evaluation, and obtaining the output configuration file of each configuration file generation model; It can be understood that the output configuration file refers to the configuration file used for model evaluation.
[0061] In the specific implementation, the data sets consisting of representative questions or tasks of each evaluation dimension are respectively input into multiple profile generation models to generate profiles for model evaluation, namely output profiles, in which the key dimensions that need to be considered when evaluating large models are included. These dimensions should fully cover the main capabilities and requirements of large models in processing document tasks. For example, text understanding ability: evaluate the model's ability to understand natural language text, especially the ability to interpret professional terms and technical documents. Code parsing ability: test whether the model can correctly parse and understand the code logic in programming languages (such as CAPL, C, Python, etc.). Format conversion efficiency: examine the speed and accuracy of the model's conversion between different document formats. Error detection and correction ability: test whether the model can identify errors or inconsistencies in the document and make effective correction suggestions. Information extraction accuracy: evaluate the accuracy of the model in extracting key information from complex documents. Response speed: measure the speed of the model in processing tasks, especially when processing large or complex documents.
[0062] Step A012, scoring the output configuration files of each configuration file generation model according to a preset model scoring standard, and obtaining a model scoring result corresponding to each configuration file generation model; It can be understood that the preset model scoring criteria refer to the pre-selected model evaluation scoring criteria, and the model scoring result refers to the evaluation scoring result of the model.
[0063] In the specific implementation, this embodiment sets specific scoring criteria for each evaluation dimension, usually using a scoring system of 1 to 10 points, with 10 points being the highest score. The scoring criteria should be as detailed as possible so that the scorer can give scores objectively and impartially. For example, when evaluating "information extraction accuracy", it can be stipulated that completely correct answers get 10 points, partially correct answers are given corresponding scores based on the proportion of correct information, and completely wrong answers get 0 points. Then, the configuration files used for model evaluation of each configuration file generation model are evaluated and scored to obtain the evaluation and scoring results.
[0064] Step A013, summarize the model scoring results corresponding to each configuration file generation model to obtain the target evaluation scoring result.
[0065] In the specific implementation, the model evaluation and scoring results of each dimension corresponding to the model generated by each configuration file are aggregated and processed, and finally the target evaluation and scoring results are obtained.
[0066] Step S02, determining a corresponding initial configuration file generation model according to the target evaluation and scoring result; In a specific implementation, the model evaluation and scoring results of each dimension corresponding to each configuration file generation model are arranged in descending order to screen out the configuration file generation model with the highest score, which is the initial configuration file generation model.
[0067] Step S03: training the initial configuration file generation model according to the model input data to obtain a target configuration file generation model.
[0068] In a specific implementation, the data used to generate the configuration file in the input model is used as the training sample of the model, and then the initial configuration file generation model is trained. When the performance of the model meets the user's needs, the target configuration file generation model is obtained.
[0069] It should be noted that in this embodiment, according to the main characteristics of the task, the large model needs to have strong document processing capabilities, including but not limited to Excel, PDF, Word, XML, CAPL code, C code, Python code documents. Develop an evaluation set and method to evaluate and score each dimension of the large model, and select a model with a high score. Then clean the data to be processed to remove invalid information, such as null values, duplicates, etc. Ensure the validity and consistency of the data. Finally, use the processed data to train the selected large model so that it can completely and correctly obtain and return valid information data.
[0070] This embodiment performs model evaluation on multiple configuration file generation models according to the evaluation data set to obtain a target evaluation score result; determines the corresponding initial configuration file generation model according to the target evaluation score result; and trains the initial configuration file generation model according to the model input data to obtain a target configuration file generation model.
[0071] For example, to help understand the implementation process of the test environment configuration method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flowchart of a test environment configuration method is provided, specifically: S1. Large model interaction module: S1.1 Selection of the original large model: According to the main characteristics of the task, the large model needs to have strong document processing capabilities, including but not limited to Excel, PDF, Word, XML, CAPL code, C code, Python code documents. Develop an evaluation set and method to evaluate and score each dimension of the large model, and select a model with a high score. S1.2 Document data preprocessing: Clean the data to be processed and remove invalid information, such as null values, duplicates, etc. Ensure the validity and consistency of the data. S1.3 Large model training: Use the data processed by S1.2 to train the large model selected by S1.1 so that it can completely and correctly obtain and return valid information data. S1.4 Provide an interface for a large model to communicate with the user, which can be a local application or a web page.
[0072] S2. Configuration file generation module: S2.1 Use the large model trained by S1.3 to issue a task to it using natural language through S1.4, such as generating a CAPL simulation code and its corresponding system variable file through the CAN communication matrix. S2.3 When the large model receives the task, it can generate the corresponding configuration file based on the input requirements and data documents, and provide feedback to the user through S1.4.
[0073] S3. Verification and suggestion module: S3.1 After executing the configuration file generation task in S2.3, it enters the verification phase. The verification module will verify the configuration file generated by S2.3 and the input data document, and feedback the verification results to the user through S1.4. S3.2 If there is a problem with the verification result of S3.1, optimization suggestions will be provided to the user through S1.4. S3.3 If the user adopts the suggestions provided by S3.2, S2 needs to re-understand the evaluation task content and execute it. S3.4 If the user adds new task content, S2 needs to re-understand the evaluation task content and execute it. S3.5 If the user adopts the task results fed back by S3.1, the relevant result files / data will be returned to the big model through S1.3 for model training to improve the capabilities and understanding accuracy of the big model.
[0074] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the test environment configuration method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.
[0075] This application also provides a test environment configuration device, please refer to Figure 4 , the test environment configuration device comprises: A generation module 10 is used to generate a model according to a user task request, model input data and a target configuration file, and obtain a corresponding target configuration file; A verification module 20, used to perform verification according to the model input data and the target configuration file to obtain a target file verification result; The configuration module 30 is used to determine the test environment configuration result according to the target file verification result.
[0076] Optionally, the generating module 10 is further used for: Perform data analysis based on user task requests and determine the corresponding user uploaded data; Preprocessing the user uploaded data to determine the preprocessed user uploaded data; The model input data is obtained based on the preprocessed user uploaded data.
[0077] Optionally, the generating module 10 is further used for: Perform model evaluation on multiple configuration file generation models according to the evaluation data set to obtain the target evaluation score results; Determine a corresponding initial configuration file generation model according to the target evaluation scoring result; The initial configuration file generation model is trained according to the model input data to obtain a target configuration file generation model.
[0078] Optionally, the generating module 10 is further used for: Input the evaluation data sets into each configuration file generation model for multi-dimensional evaluation, and obtain the output configuration files of each configuration file generation model; Scoring the output configuration files of each configuration file generation model according to a preset model scoring standard, and obtaining the model scoring result corresponding to each configuration file generation model; The model scoring results corresponding to the models generated by each configuration file are summarized to obtain the target evaluation scoring results.
[0079] Optionally, the verification module 20 is further used to: Extracting data from the target configuration file to obtain key data of the file; Compare the key data of the file with the model input data to obtain a target data comparison result; A target file verification result is obtained according to the target data comparison result.
[0080] Optionally, the configuration module 30 is further used to: When the target file verification result is a verification failure result, generating corresponding model optimization suggestion information according to the target file verification result; The model optimization suggestion information is sent, and a test environment configuration result is determined to be a configuration failure result.
[0081] Optionally, the configuration module 30 is further used to: When the test environment configuration result is a configuration success result, a user update request is obtained; A test environment is configured according to the user update request and the target configuration file generation model.
[0082] The test environment configuration device provided by the present application adopts the test environment configuration method in the above embodiment, which can solve the technical problem of how to improve the configuration efficiency while reducing the occurrence rate of environment configuration errors. Compared with the prior art, the beneficial effects of the test environment configuration device provided by the present application are the same as the beneficial effects of the test environment configuration method provided by the above embodiment, and other technical features in the test environment configuration device are the same as the features disclosed in the above embodiment method, which will not be repeated here.
[0083] The present application provides a test environment configuration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the test environment configuration method in the above-mentioned embodiment one.
[0084] Reference below Figure 5 , which shows a schematic diagram of the structure of a test environment configuration device suitable for implementing the embodiment of the present application. The test environment configuration device in the embodiment of the present 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 Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5The test environment configuration device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0085] like Figure 5 As shown, the test environment configuration device may include a processing device 1001 (such as 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 (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the test environment configuration device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the test environment configuration device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a test environment configuration device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.
[0086] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.
[0087] The test environment configuration device provided by the present application adopts the test environment configuration method in the above embodiment, which can solve the technical problem of how to improve the configuration efficiency while reducing the occurrence rate of environment configuration errors. Compared with the prior art, the beneficial effects of the test environment configuration device provided by the present application are the same as the beneficial effects of the test environment configuration method provided by the above embodiment, and the other technical features in the test environment configuration device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.
[0088] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.
[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0090] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the test environment configuration method in the above-mentioned embodiment.
[0091] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.
[0092] The computer-readable storage medium may be included in the test environment configuration device; or may exist independently without being assembled into the test environment configuration device.
[0093] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the test environment configuration device, the test environment configuration device: generates a model according to the user task request, model input data and target configuration file to obtain the corresponding target configuration file; performs verification according to the model input data and the target configuration file to obtain the target file verification result; and determines the test environment configuration result according to the target file verification result.
[0094] Computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0095] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0096] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.
[0097] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned test environment configuration method, and can solve the technical problem of how to improve configuration efficiency while reducing the occurrence rate of environment configuration errors. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the test environment configuration method provided by the above-mentioned embodiment, and will not be repeated here.
[0098] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned test environment configuration method when executed by a processor.
[0099] The computer program product provided by the present application can solve the technical problem of how to improve configuration efficiency while reducing the occurrence rate of environment configuration errors. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the test environment configuration method provided by the above embodiment, which will not be repeated here.
[0100] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.
Claims
1. A test environment configuration method, characterized in that: The test environment configuration method comprises: Generate a model according to the user task request, model input data and target configuration file, and obtain the corresponding target configuration file; Perform verification according to the model input data and the target configuration file to obtain a target file verification result; The test environment configuration result is determined according to the target file verification result.
2. The method according to claim 1, characterized in that Before the step of generating a model according to the user task request, the model input data and the target configuration file to obtain the corresponding target configuration file, the step further includes: Perform data analysis based on user task requests and determine the corresponding user uploaded data; Preprocessing the user uploaded data to determine the preprocessed user uploaded data; The model input data is obtained based on the preprocessed user uploaded data.
3. The method according to claim 1, characterized in that Before the step of generating a model according to the user task request, the model input data and the target configuration file to obtain the corresponding target configuration file, the step further includes: Perform model evaluation on multiple configuration file generation models according to the evaluation data set to obtain the target evaluation score results; Determine a corresponding initial configuration file generation model according to the target evaluation scoring result; The initial configuration file generation model is trained according to the model input data to obtain a target configuration file generation model.
4. The method according to claim 3, characterized in that The step of performing model evaluation on multiple configuration file generation models according to the evaluation data set to obtain target evaluation scoring results includes: Input the evaluation data sets into each configuration file generation model for multi-dimensional evaluation, and obtain the output configuration files of each configuration file generation model; Scoring the output configuration files of each configuration file generation model according to a preset model scoring standard, and obtaining the model scoring result corresponding to each configuration file generation model; The model scoring results corresponding to the models generated by each configuration file are summarized to obtain the target evaluation scoring results.
5. The method according to claim 1, characterized in that The step of performing verification according to the model input data and the target configuration file to obtain a target file verification result comprises: Extracting data from the target configuration file to obtain key data of the file; Compare the key data of the file with the model input data to obtain a target data comparison result; A target file verification result is obtained according to the target data comparison result.
6. The method according to claim 1, characterized in that The step of determining the test environment configuration result according to the target file verification result comprises: When the target file verification result is a verification failure result, generating corresponding model optimization suggestion information according to the target file verification result; The model optimization suggestion information is sent, and a test environment configuration result is determined to be a configuration failure result.
7. The method according to any one of claims 1 to 6, characterized in that After the step of determining the test environment configuration result according to the target file verification result, the method further includes: When the test environment configuration result is a configuration success result, a user update request is obtained; A test environment is configured according to the user update request and the target configuration file generation model.
8. A test environment configuration device, characterized in that: The device comprises: A generation module is used to generate a model according to a user task request, model input data and a target configuration file to obtain a corresponding target configuration file; A verification module, used to perform verification according to the model input data and the target configuration file to obtain a target file verification result; A configuration module is used to determine the test environment configuration result according to the target file verification result.
9. A test environment configuration device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the test environment configuration method according to any one of claims 1 to 7.
10. 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, the steps of the test environment configuration method according to any one of claims 1 to 7 are implemented.