Regression test case generation method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a regression test case generation method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring target test demand information; performing function classification on the target test demand information to obtain a target demand function category; screening the historical test demand information according to the target demand function category to obtain selected test demand information; generating test parameters according to the selected test demand information to obtain target test parameters; performing screening processing on preset historical regression test cases according to the selected test demand information to obtain selected regression test cases; and generating a test case based on preset case generation indication information, the target test parameters and the selected regression test case to obtain a target regression test case. The method can be applied to business systems needing a large amount of data, such as financial science and technology and health medical treatment, manpower can be saved, and the use case generation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and is applied to the fields of financial technology and healthcare. In particular, it relates to a regression test case generation method and apparatus, computer equipment and storage medium. Background Technology

[0002] Regression testing is a type of software testing used to confirm that recent program or code changes have not adversely affected existing functionality. In the fintech and healthcare sectors, software undergoes regular feature updates, and regression test cases are regularly generated to perform regression testing on the updated software. Taking a fintech scenario as an example, different convenient functions need to be configured on financial software periodically. Therefore, before each deployment, relevant test cases need to be selected from a large number of historical test cases. New regression test cases are then generated based on the new requirements and relevant test cases to test the financial software and ensure the updated software can be deployed successfully. Similarly, in healthcare, feature updates on medical platforms (e.g., intelligent medical consultation, updates to medical service location points, etc.) also require finding relevant regression test cases from a large number of historical test cases and generating new regression test cases to test the medical platform, ensuring the new version of the medical platform can be deployed successfully.

[0003] In related technologies, the generation of regression test cases mainly relies on manually identifying relevant historical test cases, manually analyzing newly added functionalities and test points, and then having software developers formulate new regression test cases based on the new functionalities, test points, and historical test cases. However, manually formulating regression test cases is not only labor-intensive but also inefficient. Therefore, how to generate regression test cases efficiently and effectively while saving manpower has become an urgent technical problem to be solved. Summary of the Invention

[0004] The main objective of this application is to provide a regression test case generation method and apparatus, computer equipment and storage medium, which aims to save manpower and generate regression test cases efficiently.

[0005] To achieve the above objectives, a first aspect of this application proposes a regression test case generation method, the method comprising:

[0006] Obtain target test requirements information;

[0007] The target test requirement information is functionally categorized to obtain the target requirement function categories;

[0008] Based on the target functional category, the historical test requirement information is filtered to obtain the selected test requirement information;

[0009] According to the selected test requirement information, test parameter generation is performed to obtain target test parameters;

[0010] According to the selected test requirement information, the preset historical regression test cases are screened to obtain selected regression test cases;

[0011] Based on the preset case generation instruction information, the target test parameters and the selected regression test cases, test case generation is performed to obtain target regression test cases.

[0012] In some embodiments, the target test requirement information includes target requirement identification information, target requirement title information and target requirement detail information; the target test requirement information is functionally classified to obtain target requirement function categories, including:

[0013] The target requirement title information is feature extracted to obtain target requirement title features;

[0014] The target requirement detail information is content feature extracted to obtain target requirement content features;

[0015] According to the preset function classification list, the preset classification instruction information, the target requirement identification information, the target requirement title features and the target requirement content features, the target test requirement information is functionally classified to obtain the target requirement function categories.

[0016] In some embodiments, the historical test requirement information is screened according to the target requirement function categories to obtain selected test requirement information, including:

[0017] The function categories of the historical test requirement information are obtained to obtain historical requirement function categories;

[0018] According to the target requirement function categories and the historical requirement function categories, the historical test requirement information is screened to obtain the selected test requirement information.

[0019] In some embodiments, the target test parameters include target function point information, target function point description information and target function test point information;

[0020] According to the selected test requirement information, test parameter generation is performed to obtain target test parameters, including:

[0021] According to the preset function point induction instruction information, the selected test requirement information is function point induced to obtain target function point information;

[0022] The target function point description information of the target function point information is obtained;

[0023] According to the preset test point generation instruction information, the target function point information and the target function point description information, test point generation is performed to obtain the target function test point information.

[0024] In some embodiments, the function point induction instruction information includes induction identity information, induction background information, induction rule description information, induction limitation information and induction output format information; and the function point induction of the selected test requirement information according to the preset function point induction instruction information to obtain the target function point information includes:

[0025] According to the induction identity information, the induction background information, the induction rule description information and the induction limitation information, the selected test requirement information is induced to obtain preliminary function point information;

[0026] According to the induction output format information, the preliminary function point information is format-converted to obtain the target function point information.

[0027] In some embodiments, the use case generation instruction information includes use case generation identity information, use case generation background information, use case generation rule information, use case generation design category, use case generation limitation information and use case generation format information; and the test case generation based on the preset use case generation instruction information, the target test parameter and the selected regression test case to obtain the target regression test case includes:

[0028] According to the use case generation identity information, the use case generation background information, the use case generation rule information, the use case generation design category and the use case generation limitation information, a preset original use case generation model is trained to obtain a target use case generation model;

[0029] The test case generation is performed through the target use case generation model, the target test parameter and the selected regression test case to obtain preliminary regression test cases;

[0030] According to the use case generation format information, the preliminary regression test cases are format-converted to obtain the target regression test cases.

[0031] In some embodiments, the test case generation through the target use case generation model, the target test parameter and the selected regression test case to obtain preliminary regression test cases includes:

[0032] The target test parameter is feature-extracted to obtain test parameter features;

[0033] The selected regression test case is feature-extracted to obtain test case features;

[0034] The test case generation is performed by the target use case generation model, the test parameter feature and the test case feature, and the preliminary regression test case is obtained.

[0035] To achieve the above object, a second aspect of the embodiment of the present application provides a regression test case generation device, the device comprises:

[0036] an information acquisition module, configured to acquire target test requirement information;

[0037] a function classification module, configured to perform function classification on the target test requirement information to obtain target requirement function categories;

[0038] a requirement screening module, configured to perform screening processing on historical test requirement information according to the target requirement function categories to obtain selected test requirement information;

[0039] a parameter generation module, configured to perform test parameter generation according to the selected test requirement information to obtain target test parameters;

[0040] a case screening module, configured to perform screening processing on preset historical regression test cases according to the selected test requirement information to obtain selected regression test cases;

[0041] a case generation module, configured to perform test case generation based on preset case generation instruction information, the target test parameters and the selected regression test cases to obtain target regression test cases.

[0042] To achieve the above object, a third aspect of the embodiment of the present application provides a computer device, the computer device comprises a memory and a processor, the memory stores a computer program, and the processor realizes the method of the first aspect when executing the computer program.

[0043] To achieve the above object, a fourth aspect of the embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program realizes the method of the first aspect when executed by a processor.

[0044] The regression test case generation method, apparatus, computer equipment, and storage medium proposed in this application acquire target test requirement information, classify the target test requirement information into target requirement functional categories, and then select selected test requirement information of the same functional category from historical test requirement information according to the target requirement functional category. Finally, the historical regression test cases corresponding to the selected test requirement information are determined as selected regression test cases. When generating test cases, target test parameters are first organized according to the selected test requirements, and then test cases are generated directly according to the test case generation instructions, target test parameters, and selected regression test cases to obtain the target regression test cases. Therefore, in the test case generation process, the functional category classification, test parameter generation, historical regression test case screening, and test case generation are all fully automated, reducing the manpower required for test case generation and improving the efficiency of regression test case generation. Attached Figure Description

[0045] Figure 1 This is a flowchart of the regression test case generation method provided in the embodiments of this application;

[0046] Figure 2 yes Figure 1 The flowchart of step S102 in the document;

[0047] Figure 3 yes Figure 1 The flowchart of step S103 in the process;

[0048] Figure 4 yes Figure 1 The flowchart of step S104 in the process;

[0049] Figure 5 yes Figure 4 The flowchart of step S401 in the text;

[0050] Figure 6 yes Figure 1 The flowchart of step S106 in the process;

[0051] Figure 7 yes Figure 6 The flowchart of step S602 in the document;

[0052] Figure 8 This is an overall flowchart of the regression test case generation method provided in the embodiments of this application;

[0053] Figure 9 This is a schematic diagram of the structure of the regression test case generation device provided in the embodiments of this application;

[0054] Figure 10 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0055] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0056] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a manner different from the module division in the device or the sequence in the flowchart. The terms "first", "second", etc. in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0058] First, the meanings of several terms involved in the present application are analyzed:

[0059] Artificial intelligence (AI): is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science, and artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. The research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also the theory, method, technology and application system of using digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, to perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0060] Large language model (LLM): refers to a deep learning model trained using a large amount of text data, so that the model can generate natural language text or understand the meaning of language text. These models can provide in-depth knowledge and language production on various topics by training on a large dataset. The core idea is to learn the patterns and structures of natural language through large-scale unsupervised training, to a certain extent, to simulate the language cognition and generation process of human beings.

[0061] Regression testing: refers to the modification of the old code, retesting to confirm that the modification does not introduce new errors or cause other code to produce errors. Automatic regression testing will greatly reduce the cost of system testing, maintenance and upgrading. Regression testing as a component of software life cycle, occupies a large proportion of the workload in the whole software testing process, and each stage of software development will carry out multiple regression tests. In incremental and rapid iterative development, the continuous release of new versions makes regression testing more frequent, and in extreme programming method, it is required to carry out several times of regression testing every day.

[0062] Test Case: refers to the description of a specific software product testing task, which embodies the test plan, method, technology and strategy. Its content includes test target, test environment, input data, test steps, expected result, test script, etc., and finally forms a document. Simply speaking, test case is a set of test inputs, execution conditions and expected results for a specific target, used to verify whether it meets a specific software requirement.

[0063] Agent: refers to an agent that can perceive the environment and take action to achieve a specific goal. It can be software, hardware or a system, with autonomy, adaptability and interaction ability. Agent can perceive changes in the environment (such as through sensors or data input), make judgments and decisions according to the knowledge and algorithms learned by itself, and then execute actions to influence the environment or achieve the predetermined goal.

[0064] Function Points: is a standard measurement unit for measuring the size of software. Simply put, the size of a software can be measured by the number of function points delivered to the user, just like the size of a house is measured by the area or use area provided to the user. Function point method is from the user's perspective, by quantifying system functions to measure the size of software, this measurement is mainly based on the logical design of the system.

[0065] Test Point: usually refers to a specific check item or verification point in a test case, used to confirm whether the system meets specific requirements or functions.

[0066] Regression testing is a quality control process of a system, which is used to verify whether the recent changes or updates to the software have inadvertently introduced new errors or had a negative impact on previous functions (for example, a new air conditioning system is installed, and it is found that the new air conditioning system can work as expected, but the originally bright light no longer brightens). Therefore, after the software is changed or updated, regression testing is needed to find out the problems in the software in advance for repair, so that the software can run normally after the change or update.

[0067] In the field of financial technology and health care, in order to ensure that the software can run normally after system update or function change. For example, in the field of financial technology, the insurance platform needs to be optimized and the system needs to be optimized regularly. In order to ensure that the insurance platform can run normally after updating, it does not affect the normal start of the original function, it is necessary to use regression test cases to test the updated insurance platform, and to determine that the original function of the insurance platform runs normally before putting the new version of the insurance platform online. In the field of health care, the medical platform also needs to be updated and optimized regularly. After updating the medical platform each time, regression testing is needed to ensure that all functions of the medical platform are running normally before putting the updated medical platform online to ensure the normal use of the medical platform.

[0068] The software update iteration speed is very fast, new functions are emerging, and the original function is continuously optimized, so the historical demand list is also expanding like a snowball, at the same time, the test case library is also growing. Therefore, how to accurately screen out the regression test cases related to the current version from thousands of test cases is undoubtedly a difficult task. On the other hand, the popularity of agile development mode and the urgent need of the industry for efficiency improvement make the timely updating and maintenance of regression test cases a big challenge. How to ensure the effectiveness of test cases while avoiding the failure of test cases due to outdated test cases, and ensure the comprehensiveness and accuracy of regression testing, has become a problem that needs to be solved. In related technologies, the screening and maintenance of regression test cases are mainly completed by test personnel, and the test personnel redevelop regression test cases. This process not only requires test personnel to repeatedly screen related historical demand documents, identify and exclude outdated function points, determine update demand points, and ensure that the generated test cases are consistent with the latest demand, but also needs to ensure the stability and security of the generated regression test cases. Therefore, manual generation of test cases requires a lot of manpower and is inefficient.

[0069] Therefore, in the new test case generation process, the historical test demand information will be automatically screened out, and the target test parameters required for test case generation will be automatically identified, and finally the regression test cases matching the new test demand information will be automatically generated. From the demand screening, parameter identification and test case generation process are all fully automated, reducing manual participation, saving manpower and improving the efficiency of regression test case generation.

[0070] The regression test case generation method and device, computer device and storage medium provided by the embodiments of the present application are described in detail through the following embodiments. First, the regression test case generation method in the embodiments of the present application is described.

[0071] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. The artificial intelligence (AI) is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving the environment, acquiring knowledge and using the knowledge to obtain the best results.

[0072] The artificial intelligence basic technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0073] The regression test case generation method provided by the embodiments of the present application relates to the field of artificial intelligence technology. The regression test case generation method provided by the embodiments of the present application can be applied in a terminal, can also be applied in a server end, and can also be software running in a terminal or a server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and big data and artificial intelligence platform, etc.; and the software can be an application for implementing the regression test case generation method, etc., but is not limited to the above forms.

[0074] The application is operable in a variety of general purpose or special purpose computer systems environments or configurations. Examples of well-known computing systems, environments, and / or configurations that can be suitable for use with the application include personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0075] It should be noted that in each specific embodiment of the present application, when it is necessary to process relevant data related to the identity or characteristics of the user according to user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or a jump to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for enabling the embodiments of the present application to normally operate will be obtained.

[0076] Figure 1 is an optional flowchart of the regression test case generation method provided by the embodiments of the present application, Figure 1 The method in can include but is not limited to including steps S101 to S106.

[0077] Step S101, obtaining target test requirement information;

[0078] Step S102, classifying the target test requirement information by function to obtain a target requirement function category;

[0079] Step S103, performing filtering processing on historical test requirement information according to the target requirement function category to obtain selected test requirement information;

[0080] Step S104, generating test parameters according to the selected test requirement information to obtain target test parameters;

[0081] Step S105, performing filtering processing on preset historical regression test cases according to the selected test requirement information to obtain selected regression test cases;

[0082] In step S106, the target regression test case is generated based on the preset case generation indication information, the target test parameter, and the selected regression test case.

[0083] The steps S101 to S106 shown in the embodiments of the present application are used to obtain the target demand function category by classifying the target test demand information, to determine the target demand function category of the target test demand information, to select the selected test demand information from the historical test demand information according to the target demand function category, and to generate the target test parameter from the selected test demand information, thereby realizing the automatic selection of the demand information and the generation of the test parameter. When the regression test case needs to be generated, the selected regression test case is selected from the historical regression test case according to the selected test demand information, and the target regression test case is generated according to the case generation indication information, the target test parameter, and the selected regression test case. Therefore, in the test case generation process, the selection of the historical demand information, the generation of the test parameter, and the generation of the regression test case are all automatically realized, which reduces the manual participation, saves manpower, improves the efficiency of the generation of the target test case, and provides a guarantee for the normal online of the updated software.

[0084] In step S101 of some embodiments, the target test demand information is the test demand information of the current software update, and the function item of the current software regression test can be determined through the target test demand information. The target test demand information includes target demand identification information, target demand title information, and target demand detail information, and the content of the target test demand information is not limited to this. Specifically, the target demand identification information is the ID of the target demand detail, which is used to identify the target demand detail. The target demand title information is the demand title for which the target regression test case needs to be generated, and the target demand title information can determine the software name and software version and other information applied to the target regression test case. The target demand detail information records the detailed content of the function points and test points required for the software applied to the target regression test case to perform regression test.

[0085] For example, if it is applied to the field of financial technology and applied to an insurance platform, the user management function module in the insurance platform is optimized by the developer to form a 3.2 version of the insurance platform. In order to generate the target regression test case for testing the 3.2 version of the insurance platform, the target test demand detail information is constructed by determining the demand for which the 3.2 version of the insurance platform needs to be functionally tested, and then the target demand identification information and the target demand title information are determined. It should be noted that the target test demand detail information specifies the software information and function information that need to be regression tested.

[0086] Please refer to Figure 2 In some embodiments, step S102 can include but is not limited to steps S201 to S203:

[0087] In step S201, feature extraction is performed on the target demand title information to obtain target demand title features.

[0088] In step S202, content feature extraction is performed on the target demand detail information to obtain target demand content features.

[0089] In step S203, the target test demand information is classified according to the preset function classification list, the preset classification indication information, the target demand identification information, the target demand title features and the target demand content features to obtain the target demand function category.

[0090] In step S201 of some embodiments, the target demand title features in the target demand title information are extracted, and the target demand title information is represented by the target demand title features. It should be noted that the target demand title features can be extracted by a feature extraction network, and the feature extraction network is any one of the following: a convolutional neural network, a Transformer network, a graph neural network and a time series feature network. This embodiment does not make specific limitations on the feature extraction network.

[0091] In step S202 of some embodiments, the target demand content features in the target demand detail information are extracted, and the function features, test features and software information that need to be regressed are determined by the target demand content features. Specifically, the keywords field in the target demand detail information is identified, and the features of the keywords field are extracted as the target demand content features. It should be noted that the keywords field includes first-level classification, second-level classification, classification version and the like, and this embodiment does not make specific limitations on the keywords field.

[0092] In step S203 of some embodiments, the function classification is performed by the preset function classification model, the function classification list, the classification indication information, the target demand identification information, the target demand title features and the target demand content features. The running control of the function classification model is completed by an intelligent agent, and the function classification model is trained by a large language model. Specifically, the function classification model is trained by the function classification list and the classification indication information to build a function classification model that can accurately and conform to the function classification of a specific field. The classification indication information includes classification identity information, classification background information, classification rule description information, classification restriction information and classification output format information, so the function classification model is trained in advance according to the classification identity information, the classification background information, the classification rule description information, the classification restriction information and the classification output format information. Therefore, the function classification model can accurately implement the function classification.

[0093] In some embodiments, the intelligent agent obtains a function classification list through the interface, classifies the functions using the function classification model, and sets classification identity information, classification background information, classification rule description information, classification restriction information, and classification output format information to prompt the function classification model to complete the function classification. For example, the classification identity information is: a software test expert, the classification background information is: as a software test expert, it is necessary to classify the target test requirement information in order to better manage the requirements, which requires detailed analysis of the target test requirement information to ensure that the given target test requirement information can be correctly classified. The classification rule description information is: you are a senior software test expert with rich experience in analyzing and classifying target test requirement information and classifying it into appropriate categories. The classification restriction information is: the scope of classification should be the given function classification list, and a target test requirement information usually belongs to only one classification. The classification output format information is: Json format output. It should be noted that the function classification list records the matching information between the candidate requirement identification information, candidate requirement title information, candidate requirement detail information, and candidate requirement function category. Therefore, by calling the function classification model according to the classification instruction information, the target test requirement information corresponding to the target requirement function category can be accurately output.

[0094] In steps S201 to S203 shown in the embodiment, the function classification model is trained in advance according to the function classification list and the classification instruction information, and then the function classification is performed according to the target requirement identification information, target requirement title features, and target requirement content features to accurately output the target requirement function category.

[0095] In some embodiments, after completing the function classification of the target test requirement information, as the software iterates, the function classification is unreasonable, and the function classification needs to be deleted or added, the intelligent agent calls the function classification model to re-classify the new candidate test requirement information to realize the update of the function classification list. It should be noted that under the premise of the intelligent agent, each candidate test requirement information classification generally only takes 2-3 seconds. If the number of candidate test requirement information is relatively large, for example, 2000, it takes about 1-2 hours to complete the update of the function classification list. However, compared with the manual classification of the test personnel, it takes less than a few days, greatly improving the efficiency of function classification and saving manpower.

[0096] Specifically, if it is judged that the target test requirement information may not exist in the function classification list, indicating that the target test requirement information corresponds to a new function category or is an iteration of a certain function category. By finding the candidate test requirement information associated with the target requirement function category before, the function classification of the target test requirement information is performed according to the candidate test requirement information.

[0097] Please refer to Figure 3In some embodiments, step S103 can include but is not limited to steps S301-S302:

[0098] In step S301, a function category of historical test requirement information is acquired, and a historical requirement function category is obtained.

[0099] In step S302, the historical test requirement information is filtered according to the target requirement function category and the historical requirement function category, and selected test requirement information is obtained.

[0100] In step S301 of some embodiments, the historical requirement function category is obtained by calling a function classification model in advance by an intelligent agent to classify the historical test requirement information, and the historical requirement function category is stored in a function classification list. When extracting the historical test requirement information related to the target test requirement information, all historical requirement function categories are called from the function classification list.

[0101] In step S302 of some embodiments, the historical test requirement information related to the target test requirement information is filtered, that is, the historical test requirement information with the same historical requirement function category as the target requirement function category is selected as the selected test requirement information.

[0102] In steps S301-S302 of the present embodiment, the historical test requirement information with the same historical requirement function category as the target requirement function category is selected as the selected test requirement information, so as to select the historical test requirement information related to the target test requirement information, so as to analyze the function point and the test point through the related historical test requirement information.

[0103] In some embodiments, the target test parameter includes: target function point information, target function point description information, and target function test point information.

[0104] Please refer to Figure 4 In some embodiments, step S104 can include but is not limited to steps S401-S403:

[0105] In step S401, the selected test requirement information is summarized according to a preset function point summary instruction information, and target function point information is obtained.

[0106] In step S402, target function point description information of the target function point information is acquired.

[0107] In step S403, the target function test point information is generated according to the preset test point generation instruction information, the target function point information, and the target function point description information.

[0108] In step S401 of some embodiments, the function point induction is mainly completed by using a function point induction model, and the function point induction model is trained by a large language model. It should be noted that the function point induction indication information is indication information for the function point induction model to perform function point induction, and guides the function point induction model to complete the function point induction. Therefore, the selected test requirement information is subjected to function point induction by using the function point induction indication information and the function point induction model, so as to induce the function point information in the selected test requirement information into the target function point information.

[0109] In step S402 of some embodiments, the target function point description information represents the description information of the target function point information, and details the target function point. It should be noted that when the target function point information is arranged, the function point detailed description information of the target function point information is also output, and the function point detailed description information is extracted as the target function point description information.

[0110] In step S403 of some embodiments, the test point generation is also completed by using a test point generation model, and the test point generation model is also trained by a large language model. It should be noted that the test point generation indication information is used to instruct the test point generation model to generate test points, and the test points represent specific functions, scenes or conditions that need to be verified in the regression test of the software. Therefore, the target function test point information is generated by using the test point generation model according to the test point indication information, the target function point information and the target function point description information, so as to realize the automatic generation of the target function test point information.

[0111] In steps S401 to S403 of the embodiment, the selected test requirement information is subjected to function point induction by using the function point induction model according to the function point induction indication information, so as to obtain the target function point information and the target function point description information of the target function point information. Then, the target function test point information is generated by using the test point generation model according to the test point generation information, the target function point information and the target function point description information, so as to realize the automatic induction of the function points, the automatic generation of the test points, save manpower, and improve the construction efficiency of the function points and the test points.

[0112] In some embodiments, the function point induction indication information includes induction identity information, induction background information, induction rule description information, induction limitation information, and induction output format information. The induction identity information is used to indicate the identity information of the function point induction model for the function point, also known as induction role information, and configures a function point induction role for the function point induction model. The induction background information is used to set the background information for the function point induction model to perform function point induction, and assists the function point induction model to complete the function point induction. The induction rule description information is used as a rule or general description for the function point induction model to perform function point induction, and the function point induction model will complete the function point induction according to the induction rule description information. The induction limitation information is used to limit the range of the function point induction model to perform function point induction, so that the target function point information induced is within the range corresponding to the induction limitation information. The induction output format information is used to control the format of the target function point information output by the function point induction model.

[0113] Referring to Figure 5 In some embodiments, step S401 can include but is not limited to steps S501 to S502:

[0114] Step S501, according to the induction identity information, the induction background information, the induction rule description information, and the induction limitation information, the selected test requirement information is induced to obtain the preliminary function point information;

[0115] Step S502, according to the induction output format information, the preliminary function point information is format-converted to obtain the target function point information.

[0116] In step S501 of some embodiments, the function point induction model, the induction identity information, the induction background information, the induction rule description information, and the induction limitation information are used to induce the selected test requirement information, and specifically, the function point induction model is used to identify the selected test requirement information to obtain a candidate function point, and the candidate function point is induced into preliminary function point information according to the induction identity information, the induction background information, the induction rule description information, and the induction limitation information.

[0117] For example, if regression testing is needed for the updated insurance platform, the function point induction model needs to be completed before a new regression test case is generated. Specifically, the function point induction model, induction identity information, induction background information, induction rule description information, induction restriction information, and selected test requirement information are input into the function point induction model. The induction identity information is a software testing expert, the induction background information is that as a software testing expert, the selected test requirement information needs to be sorted, a detailed analysis of the selected test requirement information is required, and there may be changes and obsolescence in the selected test requirement information. The new function point is sorted out according to the order of the selected test requirement information and the obsolete content is identified. The induction rule description information is that you are a senior software testing expert with rich experience in sorting selected test requirement information, and you can summarize the function point and function point description according to the provided selected test requirement information. The induction restriction information is that the sorting of the function point and the function point description must be based on the given selected test requirement information, and irrelevant content cannot be fabricated. The induction format information is output in JSON format. Therefore, the function point induction model is controlled to complete the function point induction according to the induction identity information, induction background information, induction rule description information, and induction restriction information, so as to realize automatic and accurate induction of the function point.

[0118] In step S502 of some embodiments, the format of the preliminary function point information does not necessarily conform to the induction output format information, so the preliminary function point information is format-converted according to the induction output format information to obtain the target function point information.

[0119] In steps S501 to S502 shown in the embodiment, the function point induction model induces the selected test requirement information according to the induction identity information, induction background information, induction rule description information, and induction restriction information to obtain the preliminary function point information, and the preliminary function point information is format-converted according to the induction output format information to obtain the target function point information, realizing fully automated function point induction, saving manpower, and improving function point induction efficiency.

[0120] In some embodiments, after completing the function point induction, the target function test point information needs to be generated. The test point generation instruction information includes test point generation identity information, test point generation background information, test point generation rule description information, test point generation restriction information, and test point generation output format information. The test point generation model generates the test point according to the test point generation identity information, test point generation background information, test point generation rule description information, test point generation restriction information, target function point information, and target function point description information to obtain preliminary function test point information, and the preliminary function test point information is format-converted according to the test point generation information to obtain the target function test point information.

[0121] For example, if the test point generation identity information is: Software Testing Expert; the test point generation background information is: As a software testing expert, you need to organize test points based on the target function point information and target function point description information to facilitate the writing of regression test cases; the test point generation rule description information is: You are a software testing expert, skilled in test requirement analysis, and can accurately analyze test points including functional testing, performance testing, and security testing for given target function point information and target function point description information; the test point generation restriction information is: The organization of test points must be based on the target function point information and target function point description information, and you cannot fabricate content unrelated to the given function point; the test point generation output format information is: Output in JSON format. Therefore, by configuring the test point generation model with test point generation identity information, test point generation background information, test point generation rule description information, test point generation restriction information, and test point generation output format information, the test point generation model can accurately and efficiently complete the generation of target function test point information and perform format conversion according to the set test point generation output format to obtain accurate and formatted target function test point information that meets the requirements.

[0122] In some embodiments, the test case generation indication information includes: test case generation identity information, test case generation background information, test case generation rule information, test case generation design category, test case generation constraint information, and test case generation format information. As disclosed above, the test case generation identity information indicates the identity information for test case generation; the test case generation background information indicates the background information for test case generation; and the test case generation rule information indicates the rule information for test case generation. The test case generation design category represents the method category used for test case generation, also known as the test case design category. The test case generation constraint information indicates the constraint information for test case generation, thereby limiting the scope of target regression test case generation. The test case generation format information sets the format of the target regression test cases, that is, the format of the output test cases after generation.

[0123] Please see Figure 6 In some embodiments, step S106 includes, but is not limited to, steps S601 to S603:

[0124] Step S601: Train the preset original use case generation model based on use case generation identity information, use case generation background information, use case generation rule information, use case generation design category, and use case generation restriction information to obtain the target use case generation model;

[0125] Step S602: Generate preliminary regression test cases by using the target test case generation model, target test parameters, and selected regression test cases.

[0126] Step S603, format conversion of the preliminary regression test case according to the format information generated according to the use case to obtain the target regression test case.

[0127] In step S601 of some embodiments, the original use case generation model can generate use cases, and is constructed by a large language model. The original use case generation model is trained into a target use case generation model in advance according to use case generation identity information, use case generation background information, use case generation rule information, use case generation design category, and use case generation restriction information. Therefore, the target use case generation model can accurately and human-like complete test case generation. It should be noted that the use case generation design category can be any one of the following: equivalence class classification, boundary value analysis category, cause-effect diagram and decision category, orthogonal experimental method category, and scene category. The equivalence class classification is to divide the selected regression test cases into several equivalence categories, and each category of selected test cases produces the same output in testing. The boundary value analysis category is based on the equivalence class classification, and focuses on the boundary value because errors are prone to occur near the boundary. The cause-effect diagram and decision category analyze the logical relationship between the selected regression test cases and the target regression test cases, convert the cause-effect diagram into a decision diagram, and generate the target regression test cases. The orthogonal experimental category uses an orthogonal table to select a representative subset from the full combination to cover multiple factors and multiple levels with fewer test cases. The scene category is to design test cases based on user experiment scenarios to simulate end-to-end business processes.

[0128] For example, the use case generation identity information is: a software test expert; the use case generation background information is: as a software test expert, in order to ensure software quality and stability, find potential defects, and need to write test cases according to the target function description information of the software; the use case generation rule information is: you are a senior software test expert, familiar with various test case generation design categories, and can write complete and effective test cases based on target function point information and target function point description information. The use case generation restriction information is: the test case conforms to the industry standard, the language is simple and clear, and ambiguity is avoided. Therefore, by customizing the use case generation identity information, the use case generation background information, the use case generation rule information, the use case generation design category, and the use case generation restriction information, a target use case generation model that can accurately and professionally output test cases is trained.

[0129] In step S602 of some embodiments, test case generation is performed by the target use case generation model and the target function point information, the target function point description information, the target function test point information, and the selected regression test cases to achieve automatic generation of test cases, and the generated preliminary regression test cases conform to the software regression test, saving manpower and improving test case generation efficiency.

[0130] In step S603 of some embodiments, in order to facilitate the running of the updated software with the regression test cases, the preliminary regression test cases need to be converted into the target regression test cases in a format so that the software can directly use the target regression test cases to complete the regression test.

[0131] In steps S601 to S603 of the embodiment, the original test case generation model is trained into the target test case generation model in advance according to the case generation identity information, the case generation background information, the case generation rule information, the case generation design category and the case generation limitation information, so that the target test case generation model can accurately and professionally generate test cases. Then, the preliminary regression test cases are generated by the target test case generation model, the selected regression test cases, the target function point information, the target function point description information and the target function test point information, and the preliminary regression test cases are converted into the target regression test cases according to the case generation format information. Therefore, the target regression test case generation process is fully automated, the manual participation is reduced, and the generated target regression test cases meet the software regression test requirements, improve the test efficiency, and save manpower.

[0132] Please refer to Figure 7 In some embodiments, step S602 can include but is not limited to steps S701 to S703:

[0133] Step S701, feature extraction is performed on the target test parameters to obtain test parameter features;

[0134] Step S702, feature extraction is performed on the selected regression test cases to obtain test case features;

[0135] Step S703, test case generation is performed by the target test case generation model, the test parameter features and the test case features to obtain the preliminary regression test cases.

[0136] In steps S701 to S702 of some embodiments, the feature extraction network is used to extract the test parameter features from the target test parameters, and the feature extraction network is used to extract the test case features from the selected regression test cases. It should be noted that the feature extraction network is the same as the above-mentioned feature extraction network, which will not be described here. The target test parameters include the target function point information, the target function point description information and the target function test point information, so the test parameter features include the target function point features, the target function point description features and the target function test point features.

[0137] In step S703 of some embodiments, the embodiment calls the target use case generation model through the intelligent agent, and inputs the target function point features, the target function point description features, the target function test point features and the test case features into the target use case generation model for use case generation, so as to realize automatic construction of the test case, without manual customization of the test case, saving manpower and improving the efficiency of preliminary regression test case generation.

[0138] In steps S701 to S703 shown in the embodiment, the test parameter features and the test case features are extracted first, and then the test case generation is performed through the target use case generation, the test parameter features and the test case features, so as to realize automatic generation of the test case, save manpower and improve the efficiency of preliminary regression test case generation.

[0139] Please refer to Figure 8The embodiment of the application is applied to a regression test case generation platform. When target test requirement information is received, and the target test requirement information includes target requirement identification information, target requirement title information and target requirement detail information. First, the requirement function category matched with the target requirement identification information is searched in the function classification list. If the requirement function category matched with the target requirement identification information is found in the function classification list, the target test requirement information is directly output. If the requirement function category matched with the target requirement identification information is not found in the function classification list, the agent calls the function classification model, and the function classification list, classification indication information, target requirement identification information, target requirement title features and target requirement content features are input into the function classification model for function classification to obtain the target requirement function category. The target requirement function category can be one of a first module, a second module and a classification module. Meanwhile, the function classification list is updated according to the target requirement function category to obtain an updated function classification list. In order to generate the regression test case matched with the target test requirement information, the historical test requirement information of the historical regression test case is extracted from the historical regression test case in advance, and the selected test requirement information is extracted from the historical test requirement information according to the target requirement function category. Then, the historical regression test case corresponding to the selected test requirement information is used as the selected regression test case. The agent calls the function point induction model, and the function point induction indication information and the selected test requirement information are input into the function point induction model for function point induction to output the target function point information and the target function point description information. Then, the target function point information, the target function point description information and the test point generation indication information are input into the test point generation model for test point generation to obtain the target function test point information. Finally, the agent calls the target case generation model, and the case generation indication information, the target function point information, the target function point description information, the target function test point information and the selected regression test case are input into the target case generation model for case generation to output the target regression test case. Therefore, when the software is updated and needs to be tested, the target test requirement information is only needed to be input into the regression test case generation platform, and the test case generation operation is automatically completed by the regression test case generation platform. The design requirement function classification, function point arrangement, test point generation and case generation are all automatically implemented, the manual participation is reduced, the manpower is saved, and the regression test case generation efficiency is improved.

[0140] Please refer to Figure 9 The embodiment of the application also provides a regression test case generation device, which can implement the regression test case generation method. The device comprises:

[0141] The information acquisition module 901 is configured to acquire target test requirement information.

[0142] The function classification module 902 is configured to perform function classification on the target test requirement information, to obtain a target requirement function category.

[0143] The requirement screening module 903 is configured to perform screening processing on the historical test requirement information according to the target requirement function category, to obtain selected test requirement information.

[0144] The parameter generation module 904 is configured to perform test parameter generation according to the selected test requirement information, to obtain target test parameters.

[0145] The use case screening module 905 is configured to perform screening processing on the preset historical regression test use cases according to the selected test requirement information, to obtain selected regression test use cases.

[0146] The use case generation module 906 is configured to perform test use case generation based on preset use case generation instruction information, the target test parameters, and the selected regression test use cases, to obtain target regression test use cases.

[0147] The specific implementation of the regression test use case generation apparatus is basically the same as that of the above-described regression test use case generation method, and thus will not be described herein again.

[0148] Embodiments of the present application further provide a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above-described regression test use case generation method when executing the computer program. The computer device can be any intelligent terminal, such as a tablet computer or an in-vehicle computer.

[0149] Please refer to Figure 10 , Figure 10 The hardware structure of the computer device of another embodiment is illustrated, which includes:

[0150] The processor 1001 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0151] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 1002 and are called and executed by the processor 1001 to implement the regression test case generation method of the embodiments of the present application.

[0152] The input / output interface 1003 is configured to realize information input and output.

[0153] The communication interface 1004 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0154] The bus 1005 is configured to transmit information between various components (for example, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004) of the device.

[0155] The processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are connected to each other through the bus 1005 to realize the communication connection between them in the device.

[0156] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above-mentioned regression test case generation method.

[0157] The memory is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0158] The regression test case generation method and device, computer device and storage medium provided by the embodiments of the present application obtain target test requirement information, perform functional classification on the target test requirement information to obtain a target requirement functional category, filter selected test requirement information of the same functional category from historical test requirement information according to the target requirement functional category, and determine the historical regression test case corresponding to the selected test requirement information as a selected regression test case. When generating a test case, the target test parameter is first arranged according to the selected test requirement, and then the test case is generated according to the case generation instruction information, the target test parameter and the selected regression test case to obtain a target regression test case. Therefore, in the test case generation process, the requirement functional category division, the test parameter generation, the historical regression test case filtering and the test case generation are all automatically implemented, manpower is reduced, and the generation efficiency of the regression test case is improved.

[0159] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0160] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps or different steps.

[0161] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0162] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0163] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a given step or its integral presence in the process, method, system, article, or apparatus having been made with a wider scope. The use of notation such as "first", "second", "third", etc. does not generally limit the areas, but is used to connect like elements or to distinguish one claim from another. These terms can be used interchangeably when appropriate. Terms concerning the relative position of elements can be interpreted such that their use adheres to their normal meaning, but they can also be interpreted to mean the opposite according to specific claims.

[0164] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0165] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the above-described device embodiments are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0166] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the application.

[0167] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0168] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store programs.

[0169] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not intended to limit the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for generating regression test cases, characterized in that, The method includes: Obtain target testing requirements information; The target test requirement information is functionally categorized to obtain the target requirement function categories; Based on the target requirement function category, the historical test requirement information is filtered and processed to obtain the selected test requirement information; Based on the selected test requirement information, test parameters are generated to obtain the target test parameters; Based on the selected test requirement information, the preset historical regression test cases are filtered to obtain the selected regression test cases; Test cases are generated based on preset test case generation instructions, the target test parameters, and the selected regression test cases to obtain target regression test cases.

2. The method according to claim 1, characterized in that, The target test requirement information includes: target requirement identification information, target requirement title information, and target requirement detail information; the functional classification of the target test requirement information to obtain target requirement functional categories includes: Feature extraction is performed on the target requirement title information to obtain the target requirement title features; Content features are extracted from the target requirement details to obtain the target requirement content features; The target test requirement information is categorized into functional categories based on a preset functional category list, preset category indication information, target requirement identification information, target requirement title features, and target requirement content features, thereby obtaining the target requirement functional category.

3. The method according to claim 1, characterized in that, The step of filtering historical test requirement information according to the target requirement function category to obtain selected test requirement information includes: The function categories for obtaining the historical test requirement information are thus derived, resulting in the historical requirement function categories. The historical test requirement information is filtered based on the target requirement function category and the historical requirement function category to obtain the selected test requirement information.

4. The method according to any one of claims 1 to 3, characterized in that, The target test parameters include: target function point information, target function point description information, and target function test point information; The step of generating test parameters based on the selected test requirement information to obtain target test parameters includes: Based on the preset function point summarization instruction information, the selected test requirement information is summarized into function points to obtain the target function point information; The target function point description information is obtained by acquiring the target function point information; Test points are generated based on preset test point generation instruction information, target function point information, and target function point description information to obtain the target function test point information.

5. The method according to claim 4, characterized in that, The function point summarization instruction information includes: summarization identity information, summarization background information, summarization rule description information, summarization restriction information, and summarization output format information; the step of summarizing the selected test requirement information into target function point information according to the preset function point summarization instruction information includes: Based on the summarized identity information, the summarized background information, the summarized rule description information, and the summarized restriction information, the selected test requirement information is summarized into functional points to obtain preliminary functional point information; The preliminary function point information is converted according to the summarized output format information to obtain the target function point information.

6. The method according to any one of claims 1 to 3, characterized in that, The test case generation instruction information includes: test case generation identity information, test case generation background information, test case generation rule information, test case generation design category, test case generation restriction information, and test case generation format information; the test case generation based on the preset test case generation instruction information, the target test parameters, and the selected regression test cases to obtain the target regression test cases includes: The target use case generation model is trained by using the use case generation identity information, use case generation background information, use case generation rule information, use case generation design category, and use case generation restriction information to generate the original use case generation model. Test cases are generated using the target test case generation model, the target test parameters, and the selected regression test cases to obtain preliminary regression test cases. The initial regression test cases are converted into the target regression test cases based on the format information generated by the test cases.

7. The method according to claim 6, characterized in that, The process of generating preliminary regression test cases using the target test case generation model, the target test parameters, and the selected regression test cases includes: Feature extraction is performed on the target test parameters to obtain test parameter features; Feature extraction is performed on the selected regression test cases to obtain test case features; Test cases are generated using the target test case generation model, the test parameter features, and the test case features to obtain the preliminary regression test cases.

8. A regression test case generation device, characterized in that, The device includes: The information acquisition module is used to acquire target test requirement information; The function classification module is used to classify the target test requirement information by function to obtain the target requirement function category; The requirement filtering module is used to filter historical test requirement information according to the target requirement function category to obtain selected test requirement information; The parameter generation module is used to generate test parameters based on the selected test requirement information to obtain the target test parameters. The test case filtering module is used to filter preset historical regression test cases based on the selected test requirement information to obtain selected regression test cases. The test case generation module is used to generate test cases based on preset test case generation instructions, the target test parameters, and the selected regression test cases, to obtain target regression test cases.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the regression test case generation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the regression test case generation method according to any one of claims 1 to 7.

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