Test resource pushing method and device, equipment, storage medium and program product

Abstract

The application relates to a test resource pushing method and device, equipment, a storage medium and a program product. The application relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining new version test content of a target service module, and extracting first feature information of the new version test content; inputting the first feature information into a version difficulty prediction model to obtain a version difficulty prediction result of the new version test content, wherein the version difficulty prediction model is a model obtained by training an initial version difficulty prediction model according to historical test asset information and difficulty labels of each service module; and according to the version difficulty prediction result, test resources of the new version test content are pushed to a terminal. By using the method, test resources related to the new version test content can be quickly matched, and the test efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, storage medium, and program product for pushing test resources. Background Technology

[0002] As the banking industry fully implements digital transformation, the demand for new banking functions is constantly increasing, and the pace of version updates for business modules is accelerating. Therefore, based on version updates of business modules, it is necessary to test the new version content of the business modules.

[0003] However, given that financial institutions typically have multiple sets of testing environment information, the testing environment information is complex and intertwined. Furthermore, in a full-process, full-link testing environment, after testers determine the new version testing content for a business version, they need to select test resources related to the new version testing content from the existing test resources.

[0004] Because testers cannot quickly find test resources related to the new version's test content, they are unable to start testing work quickly, thus reducing testing efficiency. Summary of the Invention

[0005] Therefore, it is necessary to provide a test resource delivery method, apparatus, equipment, storage medium, and program product that can improve testing efficiency in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for pushing test resources. The method includes:

[0007] Obtain the new version test content of the target business module, and extract the first feature information of the new version test content;

[0008] The first feature information is input into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0009] Based on the version difficulty prediction results, test resources for the new version test content are pushed to the terminal.

[0010] In one embodiment, the method further includes:

[0011] Based on the preset dependencies between the various business modules, the associated business modules of the target business module are determined, wherein the associated business modules are the business modules that the target business module depends on.

[0012] In one embodiment, the step of pushing the test resources of the new version test content to the terminal based on the version difficulty prediction result includes:

[0013] Based on the difficulty prediction results of the aforementioned version, the difficulty level of the test content for the new version is determined;

[0014] If the difficulty level is the first difficulty level, then the historical test asset information and historical test environment information of the target business module and the associated business module are pushed to the terminal.

[0015] The test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0016] In one embodiment, the method further includes:

[0017] If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal;

[0018] The test resources include historical asset information and historical test environment information of the target business module.

[0019] In one embodiment, the method further includes:

[0020] If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal;

[0021] The test resources include historical test environment information of the target business module.

[0022] In one embodiment, the method further includes:

[0023] Obtain historical test asset information and difficulty labels for each of the aforementioned business modules;

[0024] Based on the historical test asset information and difficulty labels of each business module, the initial version difficulty prediction model is trained to obtain the version difficulty prediction model.

[0025] In one embodiment, training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each of the business modules to obtain the version difficulty prediction model includes:

[0026] Extract the second feature information of the historical test asset information of each of the aforementioned business modules;

[0027] The second feature information is input into the initial version of the difficulty prediction model to obtain the difficulty prediction result;

[0028] Based on the difficulty prediction results and the difficulty labels, the initial version of the difficulty prediction model is trained to obtain the current version of the difficulty prediction model.

[0029] Secondly, this application also provides a test resource push device. The device includes:

[0030] The first acquisition module is used to acquire the new version test content of the target business module and extract the first feature information of the new version test content;

[0031] The prediction module is used to input the first feature information into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training an initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0032] The push module is used to push test resources of the new version test content to the terminal based on the version difficulty prediction results.

[0033] In one embodiment, the device further includes:

[0034] The determining module is used to determine the associated business modules of the target business module based on the preset dependency relationships between the various business modules, wherein the associated business modules are the business modules that the target business module depends on.

[0035] In one embodiment, the push module includes:

[0036] The determining unit is used to determine the difficulty level of the new version test content based on the version difficulty prediction result.

[0037] The first push unit is configured to push historical test asset information and historical test environment information of the target business module and the associated business module to the terminal if the difficulty level is a first difficulty level; wherein, the test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0038] In one embodiment, the push module further includes:

[0039] The second push unit is used to push historical asset information and historical test environment information of the target business module to the terminal if the difficulty level is the second difficulty level; wherein, the test resources include the historical asset information and historical test environment information of the target business module.

[0040] In one embodiment, the push module further includes:

[0041] The third push unit is used to push historical test environment information of the target service module to the terminal if the difficulty level is the third difficulty level; wherein, the test resources include the historical test environment information of the target service module.

[0042] In one embodiment, the device further includes:

[0043] The second acquisition module is used to acquire historical test asset information and difficulty labels of each of the business modules.

[0044] The training module is used to train the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module, so as to obtain the version difficulty prediction model.

[0045] In one embodiment, the training module is specifically used to extract second feature information of historical test asset information of each of the business modules; input the second feature information into the initial version difficulty prediction model to obtain a difficulty prediction result; and train the initial version difficulty prediction model according to the difficulty prediction result and the difficulty label to obtain the version difficulty prediction model.

[0046] Thirdly, this application also provides a computer device. 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 steps of any of the methods described in the first aspect.

[0047] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of any of the methods in the first aspect described above.

[0048] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of any of the methods described in the first aspect.

[0049] The aforementioned test resource push method, apparatus, device, storage medium, and program product acquire new version test content for a target business module, extract first feature information of the new version test content, input the first feature information into a version difficulty prediction model trained on an initial version difficulty prediction model based on historical test asset information and difficulty labels of each business module, obtain a version difficulty prediction result for the new version test content, and then push test resources for the new version test content to the terminal based on the version difficulty prediction result. Because this embodiment can push test resources for the new version test content to the terminal based on the version difficulty prediction result, testers can quickly match test resources related to the new version test content, thereby quickly starting testing work and improving testing efficiency. Attached Figure Description

[0050] Figure 1 An application environment diagram for a test resource push method provided in an embodiment of this application;

[0051] Figure 2 A flowchart illustrating a test resource push method provided in an embodiment of this application;

[0052] Figure 3 A flowchart illustrating another test resource push method provided in this application embodiment;

[0053] Figure 4 A flowchart illustrating a version difficulty prediction model prediction method provided in this application embodiment;

[0054] Figure 5 A flowchart illustrating another version of the difficulty prediction model prediction method provided in this application embodiment;

[0055] Figure 6 A structural block diagram of a test resource push device provided in an embodiment of this application;

[0056] Figure 7 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] In traditional techniques, after testers determine the test content for a new version of a business application, they need to select test resources related to the new version's test content from the existing test resources.

[0059] However, because testers are not familiar with the existing test resources, they may spend a lot of time searching for test resources related to the new version's test content, and may even miss test resources related to the new version's test content, thereby reducing testing efficiency and preventing the test resources from being used to their maximum extent and in a reasonable manner.

[0060] To address the aforementioned technical problems, this application provides a method for pushing test resources, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0061] In one embodiment, such as Figure 2 As shown, Figure 2 This is a flowchart illustrating a test resource push method provided in an embodiment of this application, applied to... Figure 1 Taking a computer device as an example, the method includes the following steps:

[0062] S201, Obtain the new version test content of the target business module, and extract the first feature information of the new version test content.

[0063] In this embodiment, the new version test content can be a modification of the transaction link, a modification of the data transmission link, or a modification of the table structure.

[0064] After obtaining the new version test content of the target business module, the unstructured new version test content is structured by extracting the first feature information such as the functional points, modification points, transaction chain length and the number of testers required.

[0065] It should be noted that the difficulty of the new version test content is not directly determined by the test content itself, but rather by the primary feature information contained within the test content.

[0066] S202, input the first feature information into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0067] In this embodiment, the version difficulty prediction model is a model obtained by training the initial version difficulty prediction model with historical test asset information and difficulty labels of each business module based on machine learning model algorithm.

[0068] The machine learning model algorithm can be either LightGBM, an ensemble learning framework based on gradient boosting trees, or AdaBoost, an iterative algorithm that improves the accuracy of the algorithm through multiple learning iterations.

[0069] S203, based on the version difficulty prediction results, pushes test resources for the new version test content to the terminal. Test resources include, for example, test environment information and test asset information. The test environment information includes, for example, the business template name, the person in charge of the business template, the professional line to which the business template belongs, and the server information related to the business template.

[0070] The forms of push notifications to the terminal include, but are not limited to, emails, voice signals, and pop-up reminders.

[0071] The aforementioned test resource push method, apparatus, device, storage medium, and program product acquire new version test content for a target business module, extract first feature information of the new version test content, input the first feature information into a version difficulty prediction model trained on an initial version difficulty prediction model based on historical test asset information and difficulty labels of each business module, obtain a version difficulty prediction result for the new version test content, and then push test resources for the new version test content to the terminal based on the version difficulty prediction result. Because this embodiment can push test resources for the new version test content to the terminal based on the version difficulty prediction result, testers can quickly match test resources related to the new version test content, thereby quickly starting testing work and improving testing efficiency.

[0072] Optionally, based on the above embodiments, the following implementation methods may also be included:

[0073] Based on the pre-defined dependencies between various business modules, the associated business modules of the target business module are determined, where the associated business modules are the business modules that the target business module depends on.

[0074] In this embodiment, business modules are treated as entities, and the pre-defined dependencies between business modules are used as the relationships between entities. Each business module is associated with another business module, and the results of the association of business modules are stored in a graph structure. Then, the associated business modules of the target business module are determined based on this graph structure.

[0075] The dependencies between various business modules include, but are not limited to, transaction chain dependencies, service dependencies, data dependencies, and interaction dependencies.

[0076] The method provided in this embodiment determines the associated business modules of the target business module based on the preset dependencies between various business modules, thereby enabling the rapid and accurate push of test resources to the new version test content corresponding to the target business module based on the associated business modules, simplifying test preparation and improving test efficiency.

[0077] Reference Figure 3 , Figure 3 This is a flowchart illustrating another test resource push method provided in this application embodiment. This embodiment relates to an optional implementation of how to push test resources of new version test content to the terminal based on the version difficulty prediction result. Based on the above embodiment, the above-mentioned S203 specifically includes the following steps:

[0078] S301. Based on the version difficulty prediction results, determine the difficulty level of the test content for the new version.

[0079] For example, the version difficulty prediction result can be 0, 1, or 2. A version difficulty prediction result of 0 indicates that the difficulty level of the new version test content is low, a version difficulty prediction result of 1 indicates that the difficulty level of the new version test content is medium, and a version difficulty prediction result of 2 indicates that the difficulty level of the new version test content is high.

[0080] If the version difficulty prediction result obtained in this step is 2, then the difficulty level of the new version test content can be determined as high difficulty; if the version difficulty prediction result is 1, then the difficulty level of the new version test content is medium difficulty; if the version difficulty prediction result is 0, then the difficulty level of the new version test content is low difficulty.

[0081] S302. If the difficulty level is the first difficulty level, then push the historical test asset information and historical test environment information of the target business module and related business modules to the terminal; wherein, the test resources include the historical test asset information and historical test environment information of the target business module and related business modules.

[0082] It should be noted that testers need to regularly scan and fill in gaps in the historical test environment information, and be aware of the updates to the historical test environment information of the terminal in real time to ensure full coverage of the historical test environment information of the terminal.

[0083] For example, if the first difficulty level is high difficulty, then historical test asset information and historical test environment information of the target business module and related business modules are pushed to the terminal.

[0084] The method provided in this embodiment determines the difficulty level of the test content of the new version based on the version difficulty prediction result. When the difficulty level is the first difficulty level, it actively pushes the historical test asset information and historical test environment information of the target business module and related business modules to the terminal, so that the test resources can be used rationally and maximized, and the test preparation process can be simplified and the test efficiency can be improved.

[0085] Optionally, based on the above embodiments, step S203, which involves pushing test resources for the new version test content to the terminal according to the version difficulty prediction result, can also be implemented in the following way:

[0086] If the difficulty level is the second level, then historical asset information and historical test environment information of the target business module will be pushed to the terminal; the test resources include the historical asset information and historical test environment information of the target business module. It should be noted that the second difficulty level is lower than the first difficulty level.

[0087] For example, if the second difficulty level is medium, then the historical asset information and historical test environment information of the target business module are pushed to the terminal.

[0088] The method provided in this embodiment proactively pushes historical asset information and historical test environment information of the target business module to the terminal when the difficulty level is the second level. This enables testers to quickly and specifically match test resources related to the new version's test content based on different difficulty levels, simplifying test preparation, facilitating the rapid commencement and advancement of testing, and improving test efficiency.

[0089] Optionally, based on the above embodiments, step S203, which involves pushing test resources for the new version test content to the terminal according to the version difficulty prediction result, can also be implemented in the following way:

[0090] If the difficulty level is level 3, then the historical test environment information of the target business module is pushed to the terminal; the test resources include the historical test environment information of the target business module.

[0091] For example, if the third difficulty level is low, historical test environment information of the target business module is pushed to the terminal. The third difficulty level is lower than the second difficulty level.

[0092] The method provided in this embodiment proactively pushes historical test environment information of the target business module to the terminal when the difficulty level is the third level, which greatly simplifies the test preparation process, quickly locates the key points of the test, and avoids excessive interference.

[0093] Reference Figure 4, Figure 4 This is a flowchart illustrating a version difficulty prediction model prediction method provided in this application embodiment. This embodiment relates to an optional implementation method for training an initial version difficulty prediction model based on historical test asset information and difficulty labels of each business module. Based on the above embodiment, the method further includes the following steps:

[0094] S401. Obtain historical test asset information and difficulty labels for each business module.

[0095] In this embodiment, historical test asset information refers to the asset information recorded by testers during historical testing work, based on the test projects they imported test cases for, and the analysis and recording of risks, concerns, and coverage. This historical test asset information is stored in a database. Furthermore, historical test asset information includes, but is not limited to, project information, review materials, test cases, test guidelines, test plans, test defects, and production issues.

[0096] S402. Based on the historical test asset information and difficulty labels of each business module, train the initial version difficulty prediction model to obtain the version difficulty prediction model.

[0097] The difficulty level label can be set based on the evaluation results obtained after assessing the difficulty level of the business module version, which is based on expert experience.

[0098] The method provided in this embodiment obtains historical test asset information and difficulty tags for each business module, and trains an initial version difficulty prediction model based on this information to obtain a version difficulty prediction model. This model predicts the version difficulty, and then pushes test resources for the new version test content to the terminal based on the prediction results. This eliminates the need for testers to manually select test resources related to the new version test content from existing test resources, thus simplifying test preparation and improving test efficiency.

[0099] Reference Figure 5 , Figure 5 This is a flowchart illustrating another version of the difficulty prediction model prediction method provided in this application embodiment. This embodiment relates to an optional implementation method for training an initial version of the difficulty prediction model based on historical test asset information and difficulty labels of each business module. Based on the above embodiment, the method further includes the following steps:

[0100] S501. Extract the second feature information of historical test asset information of each business module.

[0101] In this embodiment, unstructured historical test asset information is structured by extracting second feature information such as functional points, modification points, transaction chain length and the number of testers required from the historical test asset information of each business module.

[0102] S502. Input the second feature information into the initial version of the difficulty prediction model to obtain the difficulty prediction result.

[0103] The second feature information is divided into a training set and a test set. The training set can be input into the initial version of the difficulty prediction model to obtain the difficulty prediction result.

[0104] S503. Based on the difficulty prediction results and difficulty labels, train the initial version difficulty prediction model to obtain the version difficulty prediction model.

[0105] Specifically, the initial version of the difficulty prediction model is trained using the difficulty prediction results and the corresponding difficulty labels in the training set to obtain the current version of the difficulty prediction model.

[0106] Optionally, the initial version of the difficulty prediction model can be trained using the training set to obtain an intermediate version of the difficulty prediction model. The test set can then be input into the intermediate version of the difficulty prediction model for testing to obtain the difficulty prediction results corresponding to the test set. The intermediate version of the difficulty prediction model can be optimized using the difficulty prediction results and difficulty labels corresponding to the test set. When the model accuracy cannot be further improved through iteration, the final version of the difficulty prediction model is obtained.

[0107] The method provided in this application extracts the second feature information of historical test asset information of each business module and inputs the second feature information into an initial version difficulty prediction model to obtain a difficulty prediction result. Based on the difficulty prediction result and difficulty label, the initial version difficulty prediction model is trained to obtain a version difficulty prediction model, thereby improving the prediction accuracy of the version difficulty prediction model and making the version difficulty prediction result more accurate. Based on the version difficulty prediction result, test resources for new version test content can be quickly and accurately pushed to the terminal, facilitating the rapid development and advancement of testing work, improving testing efficiency, ensuring the flawless launch of business functions, and guaranteeing the continuity of business functions in the production system.

[0108] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0109] Based on the same inventive concept, this application also provides a test resource push device for implementing the test resource push method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more test resource push device embodiments provided below can be found in the limitations of the test resource push method described above, and will not be repeated here.

[0110] Reference Figure 6 , Figure 6 This application provides a test resource push device 600, which includes: an acquisition module 601, a prediction module 602, and a push module 603, wherein:

[0111] The first acquisition module 601 is used to acquire the new version test content of the target business module and extract the first feature information of the new version test content;

[0112] The prediction module 602 is used to input the first feature information into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training an initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0113] The push module 603 is used to push test resources of the new version test content to the terminal based on the version difficulty prediction result.

[0114] The test resource push device provided in this embodiment obtains the new version test content of the target business module, extracts the first feature information of the new version test content, and inputs the first feature information into a version difficulty prediction model obtained after training an initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module. This yields a version difficulty prediction result for the new version test content, and then pushes the test resources for the new version test content to the terminal based on the version difficulty prediction result. Because this embodiment can push test resources for the new version test content to the terminal based on the version difficulty prediction result, testers can quickly match test resources related to the new version test content, thereby quickly starting testing and improving testing efficiency.

[0115] In some embodiments, the device 600 further includes:

[0116] The determining module is used to determine the associated business modules of the target business module based on the preset dependency relationships between the various business modules, wherein the associated business modules are the business modules that the target business module depends on.

[0117] In some embodiments, the push module 603 includes:

[0118] The determining unit is used to determine the difficulty level of the new version test content based on the version difficulty prediction result.

[0119] The first push unit is configured to push historical test asset information and historical test environment information of the target business module and the associated business module to the terminal if the difficulty level is a first difficulty level; wherein, the test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0120] In some embodiments, the push module 603 further includes:

[0121] The second push unit is used to push historical asset information and historical test environment information of the target business module to the terminal if the difficulty level is the second difficulty level; wherein, the test resources include the historical asset information and historical test environment information of the target business module.

[0122] In some embodiments, the push module 603 further includes:

[0123] The third push unit is used to push historical test environment information of the target service module to the terminal if the difficulty level is the third difficulty level; wherein, the test resources include the historical test environment information of the target service module.

[0124] In some embodiments, the device 600 further includes:

[0125] The second acquisition module is used to acquire historical test asset information and difficulty labels of each of the business modules.

[0126] The training module is used to train the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module, so as to obtain the version difficulty prediction model.

[0127] In some embodiments, the training module is specifically used to extract second feature information of historical test asset information of each of the business modules; input the second feature information into the initial version difficulty prediction model to obtain difficulty prediction results; and train the initial version difficulty prediction model according to the difficulty prediction results and the difficulty labels to obtain the version difficulty prediction model.

[0128] Each module in the aforementioned test resource push device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0129] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores test resources for new versions of test content. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a test resource delivery method.

[0130] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0131] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0132] Obtain the new version test content of the target business module, and extract the first feature information of the new version test content;

[0133] The first feature information is input into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0134] Based on the version difficulty prediction results, test resources for the new version test content are pushed to the terminal.

[0135] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0136] Based on the preset dependencies between the various business modules, the associated business modules of the target business module are determined, wherein the associated business modules are the business modules that the target business module depends on.

[0137] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0138] Based on the difficulty prediction results of the aforementioned version, the difficulty level of the test content for the new version is determined;

[0139] If the difficulty level is the first difficulty level, then the historical test asset information and historical test environment information of the target business module and the associated business module are pushed to the terminal.

[0140] The test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0141] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0142] If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal;

[0143] The test resources include historical asset information and historical test environment information of the target business module.

[0144] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0145] If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal;

[0146] The test resources include historical test environment information of the target business module.

[0147] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0148] Obtain historical test asset information and difficulty labels for each of the aforementioned business modules;

[0149] Based on the historical test asset information and difficulty labels of each business module, the initial version difficulty prediction model is trained to obtain the version difficulty prediction model.

[0150] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0151] Extract the second feature information of the historical test asset information of each of the aforementioned business modules;

[0152] The second feature information is input into the initial version of the difficulty prediction model to obtain the difficulty prediction result;

[0153] Based on the difficulty prediction results and the difficulty labels, the initial version of the difficulty prediction model is trained to obtain the current version of the difficulty prediction model.

[0154] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0155] Obtain the new version test content of the target business module, and extract the first feature information of the new version test content;

[0156] The first feature information is input into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0157] Based on the version difficulty prediction results, test resources for the new version test content are pushed to the terminal.

[0158] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0159] Based on the preset dependencies between the various business modules, the associated business modules of the target business module are determined, wherein the associated business modules are the business modules that the target business module depends on.

[0160] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0161] Based on the difficulty prediction results of the aforementioned version, the difficulty level of the test content for the new version is determined;

[0162] If the difficulty level is the first difficulty level, then the historical test asset information and historical test environment information of the target business module and the associated business module are pushed to the terminal.

[0163] The test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0164] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0165] If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal;

[0166] The test resources include historical asset information and historical test environment information of the target business module.

[0167] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0168] If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal;

[0169] The test resources include historical test environment information of the target business module.

[0170] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0171] Obtain historical test asset information and difficulty labels for each of the aforementioned business modules;

[0172] Based on the historical test asset information and difficulty labels of each business module, the initial version difficulty prediction model is trained to obtain the version difficulty prediction model.

[0173] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0174] Extract the second feature information of the historical test asset information of each of the aforementioned business modules;

[0175] The second feature information is input into the initial version of the difficulty prediction model to obtain the difficulty prediction result;

[0176] Based on the difficulty prediction results and the difficulty labels, the initial version of the difficulty prediction model is trained to obtain the current version of the difficulty prediction model.

[0177] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0178] Obtain the new version test content of the target business module, and extract the first feature information of the new version test content;

[0179] The first feature information is input into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module.

[0180] Based on the version difficulty prediction results, test resources for the new version test content are pushed to the terminal.

[0181] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0182] Based on the preset dependencies between the various business modules, the associated business modules of the target business module are determined, wherein the associated business modules are the business modules that the target business module depends on.

[0183] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0184] Based on the difficulty prediction results of the aforementioned version, the difficulty level of the test content for the new version is determined;

[0185] If the difficulty level is the first difficulty level, then the historical test asset information and historical test environment information of the target business module and the associated business module are pushed to the terminal.

[0186] The test resources include historical test asset information and historical test environment information of the target business module and the associated business module.

[0187] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0188] If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal;

[0189] The test resources include historical asset information and historical test environment information of the target business module.

[0190] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0191] If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal;

[0192] The test resources include historical test environment information of the target business module.

[0193] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0194] Obtain historical test asset information and difficulty labels for each of the aforementioned business modules;

[0195] Based on the historical test asset information and difficulty labels of each business module, the initial version difficulty prediction model is trained to obtain the version difficulty prediction model.

[0196] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0197] Extract the second feature information of the historical test asset information of each of the aforementioned business modules;

[0198] The second feature information is input into the initial version of the difficulty prediction model to obtain the difficulty prediction result;

[0199] Based on the difficulty prediction results and the difficulty labels, the initial version of the difficulty prediction model is trained to obtain the current version of the difficulty prediction model.

[0200] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0201] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0202] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0203] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for pushing test resources, characterized in that, The method includes: Obtain the new version test content of the target business module, and extract the first feature information of the new version test content, wherein the first feature information includes functional points, modification points, transaction chain length and the number of testers required; The first feature information is input into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training an initial version difficulty prediction model based on the historical test asset information and difficulty labels of each business module. The historical test asset information includes project information, review materials, test cases, test guidelines, test plans, test defects and production issues. Based on the difficulty prediction results of the aforementioned version, the difficulty level of the test content for the new version is determined; If the difficulty level is the first difficulty level, then push the historical test asset information and historical test environment information of the target business module and related business modules to the terminal; The test resources include historical test asset information and historical test environment information of the target business module and the associated business module; If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal; The test resources include historical asset information and historical test environment information of the target business module; If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal; The test resources include historical test environment information of the target business module; Based on the preset dependencies between the various business modules, the associated business modules of the target business module are determined, wherein the associated business modules are the business modules that the target business module depends on; the business modules are treated as entities, and the preset dependencies between the various business modules are used as the relationships between entities to associate the various business modules, and the results of associating the various business modules are stored in a graph structure, and the associated business modules of the target business module are determined based on the graph structure.

2. The method according to claim 1, characterized in that, The method further includes: Obtain historical test asset information and difficulty labels for each of the aforementioned business modules; Based on the historical test asset information and difficulty labels of each business module, the initial version difficulty prediction model is trained to obtain the version difficulty prediction model.

3. The method according to claim 2, characterized in that, The step of training the initial version difficulty prediction model based on the historical test asset information and difficulty labels of each of the aforementioned business modules to obtain the version difficulty prediction model includes: Extract the second feature information of the historical test asset information of each of the aforementioned business modules; The second feature information is input into the initial version of the difficulty prediction model to obtain the difficulty prediction result; Based on the difficulty prediction results and the difficulty labels, the initial version of the difficulty prediction model is trained to obtain the current version of the difficulty prediction model.

4. A test resource push device, characterized in that, The device includes: The first acquisition module is used to acquire the new version test content of the target business module and extract the first feature information of the new version test content, wherein the first feature information includes functional points, modification points, transaction link length and the number of testers required. The prediction module is used to input the first feature information into the version difficulty prediction model to obtain the version difficulty prediction result of the new version test content. The version difficulty prediction model is a model obtained by training an initial version difficulty prediction model based on the historical test asset information and difficulty tags of each business module. The historical test asset information includes project information, review materials, test cases, test guidelines, test plans, test defects and production issues. The push module is used to determine the difficulty level of the new version test content based on the version difficulty prediction results. If the difficulty level is the first difficulty level, then push the historical test asset information and historical test environment information of the target business module and related business modules to the terminal; The test resources include historical test asset information and historical test environment information of the target business module and the associated business module; If the difficulty level is the second difficulty level, then the historical asset information and historical test environment information of the target business module are pushed to the terminal; The test resources include historical asset information and historical test environment information of the target business module; If the difficulty level is the third level, then the historical test environment information of the target service module is pushed to the terminal; The test resources include historical test environment information of the target business module; The determination module is used to determine the associated business modules of the target business module based on the preset dependencies between the business modules, wherein the associated business modules are the business modules that the target business module depends on; the business modules are treated as entities, and the preset dependencies between the business modules are used as the relationships between entities to associate the business modules, and the results of associating the business modules are stored in a graph structure, and the associated business modules of the target business module are determined based on the graph structure.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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