Test model recommendation method and system
By maintaining the test scenarios and weight parameters, and grouping and sorting the test models according to the test scenarios selected by the user, the problem of testers relying on experience in choosing models is solved, and automated test models are realized, and the accuracy and coverage of test results are improved.
Patent Information
- Application Number
- CN202210750278.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-06-28
- Publication Date
- 2025-08-22
- Estimated Expiration
- 2042-06-28
AI Technical Summary
In the prior art, testers rely too much on experience when selecting test models, resulting in deviations in the accuracy and coverage of test results. Especially for people with insufficient testing experience, it is difficult for them to choose a suitable model.
By maintaining various test scenarios and corresponding weight parameters, obtaining weight parameters based on the test scenarios selected by the user, grouping all test models, and sorting them according to preset indicators such as market share, automatically recommending appropriate test models, supporting customized configurations and real-time updates.
It realizes the automatic recommendation of appropriate test models based on different test scenarios, reduces dependence on test experience, ensures the accuracy and coverage of test results, and supports flexible configuration and real-time data updates.
Smart Images
Figure CN115114525B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a test model recommendation method, system, electronic device, and computer-readable storage medium. Background Art
[0002] With the prevalence of electronic devices (such as mobile phones) and technological development, the variety of test scenarios for electronic devices is increasing. In current testing, testers need to test some scenarios on multiple models of devices. However, the current method mainly relies on manual selection of the required models, which relies too much on testing experience, making it difficult for some people with limited testing experience to select models. Moreover, if the model selected is inappropriate or its market share is insufficient, the coverage of the test scenario will be biased, which is likely to affect the accuracy and reference value of the test results. Summary of the Invention
[0003] The main purpose of this application is to propose a test model recommendation method, system, electronic device and computer-readable storage medium, aiming to solve the problem of how to recommend suitable models for various test scenarios.
[0004] To achieve the above objectives, an embodiment of the present application provides a test model recommendation method, the method comprising:
[0005] Maintain various test scenarios and corresponding weight parameters;
[0006] Providing test scenario options to the client and receiving the specified test scenario selected by the user;
[0007] Obtaining weight parameters corresponding to the specified test scenario;
[0008] Grouping all test models according to the weight parameters;
[0009] Sort each group of models according to preset indicators;
[0010] The sorted grouped results are returned to the client and displayed as model recommendation results.
[0011] Optionally, after sorting each group of models according to the preset index, the method further includes:
[0012] Identify the models of mainstream manufacturers that rank high under the preset indicators, mark them in the grouping results, and give them priority recommendation.
[0013] Optionally, before providing the test scenario options to the client, the method further includes:
[0014] Collect a list of models that had problems in historical tests;
[0015] After sorting each group of models according to the preset indicators, it also includes:
[0016] According to the list, the models with the most problems in the test scenario are marked in the grouping results and recommended first.
[0017] Optionally, grouping all test models according to the weight parameter includes:
[0018] Get the parameters of all test models saved in the database;
[0019] The models with the same weight parameters are divided into an equivalence class, thereby dividing all the test models into multiple model groups.
[0020] Optionally, after obtaining the weight parameter corresponding to the specified test scenario, the method further includes:
[0021] Receive a user's modification of the weight parameter.
[0022] Optionally, when the test scenario options provided do not include the test scenario required by the user, the method further includes:
[0023] Receive user-defined test scenarios and corresponding weight parameters.
[0024] Optionally, the weight parameters are device parameters of two dimensions of the electronic device corresponding to the test scenario.
[0025] Optionally, the preset indicator is market share ranking.
[0026] In addition, to achieve the above-mentioned purpose, an embodiment of the present application further provides a test model recommendation system, the system comprising:
[0027] Maintenance module, used to maintain various test scenarios and corresponding weight parameters;
[0028] The receiving module is used to provide the test scenario options to the client and receive the specified test scenario selected by the user;
[0029] An acquisition module, configured to acquire weight parameters corresponding to the specified test scenario;
[0030] A grouping module, used for grouping all test models according to the weight parameters;
[0031] Sorting module, used to sort each group of models according to preset indicators;
[0032] The return module is used to return the sorted grouping results to the client and display them as model recommendation results.
[0033] To achieve the above-mentioned purpose, an embodiment of the present application also provides an electronic device, which includes: a memory, a processor, and a test model recommendation program stored on the memory and runnable on the processor. When the test model recommendation program is executed by the processor, the test model recommendation method as described above is implemented.
[0034] To achieve the above-mentioned purpose, an embodiment of the present application further provides a computer-readable storage medium, on which a test model recommendation program is stored. When the test model recommendation program is executed by a processor, the test model recommendation method as described above is implemented.
[0035] The test model recommendation method, system, electronic device and computer-readable storage medium proposed in the embodiments of the present application can group and screen test models according to different test scenarios and corresponding weight parameters, and sort them according to preset indicators (such as market share), thereby automatically recommending test models to users (tester), making it convenient for users to conduct tests without relying on the user's testing experience, and ensuring coverage of models suitable for the test scenario. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 An application environment architecture diagram for implementing various embodiments of the present application;
[0037] Figure 2 This is a flowchart of a test model recommendation method proposed in the first embodiment of the present application;
[0038] Figure 3 This is a schematic diagram of a maintenance page for a test scenario in this application;
[0039] Figure 4 This is a schematic diagram of a page for adding weight data in this application;
[0040] Figure 5 A schematic diagram of a test scenario selection and recommendation result display page in this application;
[0041] Figure 6 for Figure 2 Detailed flow chart of step S206;
[0042] Figure 7 This is a flowchart of a test model recommendation method proposed in the second embodiment of the present application;
[0043] Figure 8 This is a flowchart of a test model recommendation method proposed in the third embodiment of the present application;
[0044] Figure 9 This is a flowchart of another form of the test model recommendation method proposed in the third embodiment of the present application;
[0045] Figure 10 A schematic diagram of the hardware architecture of an electronic device proposed in the fourth embodiment of the present application;
[0046] Figure 11 This is a module diagram of a test model recommendation system proposed in the fifth embodiment of the present application. DETAILED DESCRIPTION
[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0048] It should be noted that the descriptions of "first", "second", etc. in the embodiments of the present application are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0049] See also Figure 1 , Figure 1 This is a diagram of an application environment architecture for implementing various embodiments of the present application. The present application can be applied to an application environment including, but not limited to, a client 2, a server 4, and a network 6.
[0050] The client 2 is used to present various preset test scenarios and corresponding weight parameters to the user (tester), receive user selections and other operations, and present the model recommendation results to the user. The client 2 can be a terminal device such as a PC (Personal Computer), a mobile phone, a tablet computer, or a portable computer.
[0051] The server 4 is configured to provide the client 2 with various preset test scenarios and corresponding weight parameters, group the models based on the selections made by the user of the client 2, and return model recommendation results. The server 4 may be a computing device such as a rack server, blade server, tower server, or cabinet server, and may be a standalone server or a server cluster consisting of multiple servers.
[0052] The network 6 can be a wireless or wired network such as an intranet, the Internet, the Global System of Mobile Communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth, or Wi-Fi. The server 4 and one or more clients 2 are connected to each other via the network 6 for data transmission and interaction.
[0053] Example 1
[0054] like Figure 2 The figure shows a flowchart of a test model recommendation method proposed in the first embodiment of this application. It should be understood that the flowchart in this method embodiment is not intended to limit the order in which the steps are executed. Some steps in the flowchart may be added or deleted as needed. The method will be described below using the server as the execution subject.
[0055] The method comprises the following steps:
[0056] S200, maintaining various test scenarios and corresponding weight parameters.
[0057] In this embodiment, various test scenarios required can be maintained in advance, and one or more parameters that have a greater impact on each test scenario can be selected from multiple device parameters of various models as the weight parameters corresponding to the test scenario. The mapping relationship between various test scenarios and corresponding weight parameters is saved in a database. The device is an electronic device such as a mobile phone, and the parameters include memory, processor, screen refresh rate, screen resolution, screen type, system ROM, etc.
[0058] In a preferred embodiment, it is recommended to add two-dimensional weight parameters for each test scenario. For example, for the test scenario of animation scene display, which is mainly affected by the mobile phone screen resolution and refresh rate, these two parameters are set as the corresponding weight parameters of the test scenario.
[0059] Users with administrator privileges can add, modify, and delete test scenarios and corresponding weight parameters maintained in the database.
[0060] like Figure 3 The figure shows a schematic diagram of a test scenario maintenance page. In this page, you can add weight data (test scenarios and weight parameters), or delete the set test scenarios and weight parameters.
[0061] like Figure 4 The figure below shows a schematic diagram of a page for adding weight data (weight parameters). On this page, the weight name refers to the test scenario name. For various device parameters, you can set the priority value to determine whether it is the weight parameter corresponding to the test scenario. Among them, a value of 1 indicates that the parameter has an impact, while a blank value indicates a small or no impact. In other words, the parameter with a priority value of 1 is the weight parameter corresponding to the test scenario.
[0062] It is understood that before recommending test models to users, it is necessary to collect the various device parameters of various models and save all the parameters of all models in a database for use in subsequent recommendations. The device parameters can be collected from various official websites, such as official mobile phone websites, mobile China, and other websites that publish authoritative data, and are updated in real time or regularly.
[0063] S202: Provide test scenario options to the client, and receive a specified test scenario selected by the user.
[0064] When a client user needs to perform a test, test scenario options can be provided to the client based on the pre-maintained test scenarios, and the user can select a specified test scenario as needed.
[0065] In an optional embodiment, the test scenario options can be presented to the user in multiple levels. For example, the user can first select the performance to be tested (such as compatibility testing), and then select a specific test scenario (such as an animation scenario). In addition, the test scenarios can also include general scenarios and business scenarios. The user can first select whether the required test scenario is a general scenario or a business scenario, and then select a specific test scenario from the corresponding options.
[0066] like Figure 5 The figure shows a schematic diagram of a test scenario selection and recommendation result display page. Users can search for the required test scenario by keyword on this page, or select the required test scenario from the drop-down box.
[0067] In an optional embodiment, if the test scenario options provided do not include the test scenario the user needs, the user can also customize the test scenario, that is, add the test scenario he wants. Of course, the corresponding weight parameters need to be customized for the newly added test scenario.
[0068] S204: Obtain weight parameters corresponding to the specified test scenario.
[0069] After the client user selects the specified test scene, the server obtains the weight parameters corresponding to the specified test scene from the database. For example, when the user selects an animation scene to display this test scene, the server obtains the corresponding weight parameters from the database as the mobile phone screen resolution and refresh rate.
[0070] In an optional embodiment, the server obtains the weight parameters and returns them to the client for display to the user. The user can also modify the weight parameters as needed, for example, changing the screen type to the screen resolution. The server receives the modified weight parameters.
[0071] S206: Group all test models according to the weight parameters.
[0072] For more details, see Figure 6 , is a detailed flowchart of step S206. It is understood that this flowchart is not intended to limit the order in which the steps are performed. As needed, some steps in the flowchart may be added or deleted. In this embodiment, step S206 specifically includes:
[0073] S2060: Obtain parameters of all test models stored in the database.
[0074] Specifically, only the weight parameters of all test models may be obtained, and other parameters that do not belong to the weight parameters may not be obtained.
[0075] S2062: Classify the models with the same weight parameters into an equivalence class, thereby dividing all the test models into multiple model groups.
[0076] In this embodiment, the server groups all models according to the weight parameters, wherein the models with the same weight parameters are divided into an equivalence class (a group). For example, if the weight data is resolution and screen type, then after grouping, each group of models has the same resolution and the same screen type. In addition, the group name can be named according to the union of two weight parameters, such as "xxx resolution xxx screen type", or "mid-range machine (divided into high, medium and low-end machines according to the release time of the mobile phone and the processor), punch-hole screen (screen type)".
[0077] S208: sort each group of models according to preset indicators.
[0078] In this embodiment, the preset indicator is market share ranking. Furthermore, the market share ranking can be specifically the market share ranking of each model in the market corresponding to the application being tested. This ranking can be pre-collected and updated in real time or periodically. After grouping, each group of models is sorted from high to low based on market share ranking. In other words, within each group of models, models with higher market share are ranked higher.
[0079] In other embodiments, the preset indicator may also be any other feasible indicator as long as it can be used to sort the grouping results, which will not be described in detail here.
[0080] S210, returning the sorted grouping results and displaying them as model recommendation results.
[0081] Finally, the server returns the model groups sorted by market share to the client, and displays them as model recommendation results for the user to view. Figure 5 As shown in , users can select different group names to view the model recommendation results under that group.
[0082] Specifically, the server can return all models included in each model group in order and display them to the user, or it can return or display only the top models in each model group (e.g., top five, top ten). In addition to returning and displaying the name of each model, it can also include all device parameters corresponding to each model.
[0083] The test model recommendation method proposed in this embodiment can group and filter test models based on different test scenarios and corresponding weight parameters, and sort them according to preset indicators (such as market share). This automatically recommends test models to users (tester), making it easier for users to conduct tests without relying on their testing experience (which relies on big data). It also ensures that models suitable for the test scenario are covered. In addition, this method supports custom configuration of test scenarios and weight parameters, and can also timely update model ranking data, allowing for flexible recommendations.
[0084] Example 2
[0085] like Figure 7 FIG. 1 is a flowchart of a test model recommendation method according to a second embodiment of the present application. In this second embodiment, the test model recommendation method, based on the first embodiment described above, further includes step S310. It should be understood that the flowchart in this method embodiment is not intended to limit the order in which the steps are executed. Steps in this flowchart may be added or deleted as needed.
[0086] The method comprises the following steps:
[0087] S300, maintain various test scenarios and corresponding weight parameters.
[0088] In this embodiment, various test scenarios required can be maintained in advance, and one or more parameters that have a greater impact on each test scenario can be selected from multiple device parameters of various models as the weight parameters corresponding to the test scenario. The mapping relationship between various test scenarios and corresponding weight parameters is saved in a database. The device is an electronic device such as a mobile phone, and the parameters include memory, processor, screen refresh rate, screen resolution, screen type, system ROM, etc.
[0089] S302: Provide test scenario options to the client, and receive a specified test scenario selected by the user.
[0090] When a client user needs to perform a test, test scenario options can be provided to the client based on the pre-maintained test scenarios, and the user can select a specified test scenario as needed.
[0091] In an optional embodiment, if the test scenario options provided do not include the test scenario the user needs, the user can also customize the test scenario, that is, add the test scenario he wants. Of course, the corresponding weight parameters need to be customized for the newly added test scenario.
[0092] S304: Obtain weight parameters corresponding to the specified test scenario.
[0093] After the client user selects the specified test scene, the server obtains the weight parameters corresponding to the specified test scene from the database. For example, when the user selects an animation scene to display this test scene, the server obtains the corresponding weight parameters from the database as the mobile phone screen resolution and refresh rate.
[0094] In an optional embodiment, the server obtains the weight parameters and returns them to the client for display to the user. The user can also modify the weight parameters as needed, for example, changing the screen type to the screen resolution. The server receives the modified weight parameters.
[0095] S306: Group all test models according to the weight parameters.
[0096] Specifically, first obtain the parameters of all test models stored in the database. You only need to obtain the weight parameters of all test models, and other parameters that do not belong to the weight parameters do not need to be obtained. Then divide the models with the same weight parameters into an equivalence class, so that all test models are divided into multiple model groups. For example, if the weight data is resolution and screen type, then after grouping, each group of models has the same resolution and the same screen type. In addition, the group name can take the detailed data corresponding to the weight parameter, such as "xxx resolution xxx screen type".
[0097] S308, sorting each group of models according to preset indicators.
[0098] In this embodiment, the preset indicator is market share ranking. Furthermore, the market share ranking can be specifically the market share ranking of each model in the market corresponding to the application being tested. This ranking can be pre-collected and updated in real time or periodically. After grouping, each group of models is sorted from high to low based on market share ranking. In other words, within each group of models, models with higher market share are ranked higher.
[0099] S310: Identify the models of mainstream manufacturers that are ranked high based on the preset indicators, mark them in the grouping results, and give them priority recommendation.
[0100] In this embodiment, in addition to sorting each group of models according to market share, models of mainstream manufacturers (such as Huawei, Xiaomi, OPPO, vivo, etc., which models belong to mainstream manufacturers can be pre-set) can also be identified, and the models with the largest market share (or the top few models) of mainstream manufacturers in each group of models are marked and recommended as a priority. The mark can be a prominent mark of the manufacturer parameter corresponding to the model, or a note that it belongs to a mainstream manufacturer. The priority recommendation means putting the model at the first place in the corresponding group (if the top N models are identified, they are placed in the top N of the corresponding group in order of market share).
[0101] S312, returning the sorted grouping results and displaying them as model recommendation results.
[0102] Finally, the server will sort the models by market share and group them with the models that are recommended by mainstream manufacturers and return them to the client, which will be displayed to the user as model recommendation results in the client.
[0103] It is worth noting that the server can also first return the grouping results sorted by market share to the client for display to the user. Figure 5After clicking the "View Model Recommendations of Mainstream Manufacturers" button in the navigation bar, the above step S310 is executed, and then the result of grouping the models recommended by mainstream manufacturers is returned.
[0104] The test model recommendation method proposed in this embodiment can group and screen test models according to different test scenarios and corresponding weight parameters, and sort them according to preset indicators (such as market share), so as to automatically recommend test models to users (tester). It can also identify models of mainstream manufacturers and give priority recommendations, which is convenient for users to test without relying on the user's testing experience, and can ensure the coverage of models suitable for the test scenario.
[0105] Example 3
[0106] like Figure 8 FIG. 1 is a flowchart of a test model recommendation method proposed in the third embodiment of the present application. In the third embodiment, the test model recommendation method further includes steps S402 and S412 based on the first or second embodiment described above. The following description will be given by taking the second embodiment described above with steps S402 and S412 added as an example. It will be understood that the flowchart in the embodiment of the present method is not intended to limit the order in which the steps are executed. Some steps in the flowchart may be added or deleted as needed.
[0107] The method comprises the following steps:
[0108] S400: Maintain various test scenarios and corresponding weight parameters.
[0109] In this embodiment, various test scenarios required can be maintained in advance, and one or more parameters that have a greater impact on each test scenario can be selected from multiple device parameters of various models as the weight parameters corresponding to the test scenario. The mapping relationship between various test scenarios and corresponding weight parameters is saved in a database. The device is an electronic device such as a mobile phone, and the parameters include memory, processor, screen refresh rate, screen resolution, screen type, system ROM, etc.
[0110] S402, collecting a list of models that have problems in historical tests.
[0111] In historical tests, the test results for each test scenario generally contain models with problems (bugs). In order to provide a reference for subsequent tests, the test scenario and the models with problems can be recorded after each test. Then, based on all the recorded test results, the number of times each model has problems in each test scenario can be counted. The list includes the name of each test scenario, the model with problems, and the number of times each model has problems in that test scenario. The list can be written into a database for storage and can be updated after each test or updated regularly.
[0112] S404: Provide test scenario options to the client, and receive a specified test scenario selected by the user.
[0113] When a client user needs to perform a test, test scenario options can be provided to the client based on the pre-maintained test scenarios, and the user can select a specified test scenario as needed.
[0114] In an optional embodiment, if the test scenario options provided do not include the test scenario the user needs, the user can also customize the test scenario, that is, add the test scenario he wants. Of course, the corresponding weight parameters need to be customized for the newly added test scenario.
[0115] S406: Obtain weight parameters corresponding to the specified test scenario.
[0116] After the client user selects the specified test scene, the server obtains the weight parameters corresponding to the specified test scene from the database. For example, when the user selects an animation scene to display this test scene, the server obtains the corresponding weight parameters from the database as the mobile phone screen resolution and refresh rate.
[0117] In an optional embodiment, the server obtains the weight parameters and returns them to the client for display to the user. The user can also modify the weight parameters as needed, for example, changing the screen type to the screen resolution. The server receives the modified weight parameters.
[0118] S408: Group all test models according to the weight parameters.
[0119] Specifically, first obtain the parameters of all test models stored in the database. You only need to obtain the weight parameters of all test models, and other parameters that do not belong to the weight parameters do not need to be obtained. Then divide the models with the same weight parameters into an equivalence class, so that all test models are divided into multiple model groups. For example, if the weight data is resolution and screen type, then after grouping, each group of models has the same resolution and the same screen type. In addition, the group name can take the detailed data corresponding to the weight parameter, such as "xxx resolution xxx screen type".
[0120] S410: sort each group of models according to preset indicators.
[0121] In this embodiment, the preset indicator is market share ranking. Furthermore, the market share ranking can be specifically the market share ranking of each model in the market corresponding to the application being tested. This ranking can be pre-collected and updated in real time or periodically. After grouping, each group of models is sorted from high to low based on market share ranking. In other words, within each group of models, models with higher market share are ranked higher.
[0122] S412: Mark the model with the most problems in the test scenario in the grouping results according to the list and recommend it first.
[0123] In this embodiment, in addition to sorting each group of models by market share, one or more models with the most problems in the test scenario can be identified based on the list of models with problems collected in historical tests, and marked and recommended as a priority in each group of models. The marking can be to note the model as a high-risk model. The priority recommendation means placing the model at the top of the corresponding group (if the top M models with the most problems are identified, they will be placed in the top M of the corresponding group in order of the number of problems).
[0124] S414, identifying models of mainstream manufacturers that are ranked high under the preset indicators, marking them in the grouping results and giving them priority recommendation.
[0125] In this embodiment, models of mainstream manufacturers (such as Huawei, Xiaomi, OPPO, vivo, etc., which ones belong to mainstream manufacturers can be pre-set) can also be identified from the grouping results, and the models with the largest market share (or the top few models) of the mainstream manufacturers in each group of models can be marked and recommended first. The mark can be a conspicuous mark of the manufacturer parameter corresponding to the model, or a note that it belongs to a mainstream manufacturer model. The priority recommendation means placing the model at the first place in the corresponding group (if the top N models are identified, they are placed in the top N of the corresponding group in order of market share).
[0126] S416, returning the sorted grouping results and displaying them as model recommendation results.
[0127] Finally, the server will sort the models by market share and group them with the models that are recommended by mainstream manufacturers and return them to the client, which will be displayed to the user as model recommendation results in the client.
[0128] It is worth noting that the order of step S412 and step S414 can be swapped. As for how to handle conflicts in priority recommendations in step S412 and step S414, that is, whether mainstream manufacturers' models or models with the most problems are recommended first, it can be preset. Generally, the model with the most problems will be recommended at the top, that is, it will have the highest priority.
[0129] In addition, the server can also return the grouped results sorted by market share to the client for display to the user. After receiving the client's request to identify the most problematic models or the mainstream manufacturers' models (for example, the user clicks Figure 5 After clicking the "View Recommended Models of Mainstream Manufacturers" button in the navigation bar, execute the above steps S412 and S414, and then return the corresponding results.
[0130] See Figure 9 FIG. 1 is a flow chart of another form of the test model recommendation method described in this embodiment. Figure 9 The test model recommendation method described in [1] mainly includes five parts: data collection, data maintenance, scenario maintenance, recommendation algorithm, and model display. Some specific examples are given. The specific content of each part in this figure can be found in the above description and will not be repeated here.
[0131] The test model recommendation method proposed in this embodiment can group and screen test models according to different test scenarios and corresponding weight parameters, and sort them according to preset indicators (such as market share), so as to automatically recommend test models to users (tester). It can also identify models of mainstream manufacturers and models with the most problems in historical tests, give priority recommendations, and mark high-risk models, so as to facilitate user testing without relying on the user's testing experience, and can ensure coverage of models suitable for the test scenario.
[0132] Example 4
[0133] like Figure 10 FIG. 2 is a schematic diagram of the hardware architecture of an electronic device 20 according to the fourth embodiment of the present application. In this embodiment, the electronic device 20 may include, but is not limited to, a memory 21, a processor 22, and a network interface 23 that can be interconnected via a system bus. It should be noted that Figure 10Only the electronic device 20 having components 21-23 is shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead. In this embodiment, the electronic device 20 may be the server.
[0134] The memory 21 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 21 can be an internal storage unit of the electronic device 20, such as the hard disk or memory of the electronic device 20. In other embodiments, the memory 21 can also be an external storage device of the electronic device 20, such as a plug-in hard disk equipped on the electronic device 20, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 21 can also include both the internal storage unit of the electronic device 20 and its external storage device. In this embodiment, the memory 21 is generally used to store the operating system and various application software installed on the electronic device 20, such as the program code of the test model recommendation system 60. In addition, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
[0135] In some embodiments, the processor 22 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 22 is generally used to control the overall operation of the electronic device 20. In this embodiment, the processor 22 is used to execute program code stored in the memory 21 or process data, such as executing the test model recommendation system 60.
[0136] The network interface 23 may include a wireless network interface or a wired network interface. The network interface 23 is generally used to establish a communication connection between the electronic device 20 and other electronic devices.
[0137] Example 5
[0138] like Figure 11FIG2 is a block diagram of a test model recommendation system 60 according to a fifth embodiment of the present application. The test model recommendation system 60 can be divided into one or more program modules, one or more of which are stored in a storage medium and executed by one or more processors to implement the present embodiment. The program modules referred to in the present embodiment refer to a series of computer program instruction segments that can perform specific functions. The following description will specifically introduce the functions of each program module in this embodiment.
[0139] In this embodiment, the test model recommendation system 60 includes:
[0140] The maintenance module 600 is used to maintain various test scenarios and corresponding weight parameters.
[0141] The receiving module 602 is configured to provide a test scenario option to the client and receive a specified test scenario selected by the user.
[0142] The acquisition module 604 is used to obtain the weight parameter corresponding to the specified test scenario.
[0143] The grouping module 606 is configured to group all the test models according to the weight parameters.
[0144] The sorting module 608 is used to sort each group of models according to preset indicators.
[0145] The return module 610 is used to return the sorted grouping results and display them as model recommendation results.
[0146] The specific functions of the above modules can be found in the description of the first embodiment, which will not be repeated here.
[0147] Example 6
[0148] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores a test model recommendation program, and the test model recommendation program can be executed by at least one processor to enable the at least one processor to perform the steps of the test model recommendation method as described above.
[0149] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.
[0150] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.
[0151] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present application can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented using program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.
[0152] The above are only preferred embodiments of the embodiments of the present application, and do not limit the patent scope of the embodiments of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of the embodiments of the present application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the embodiments of the present application.
Claims
1. A test model recommendation method, characterized in that: The method comprises: Maintain various test scenarios and corresponding weight parameters; Providing test scenario options to the client and receiving the specified test scenario selected by the user; Obtaining weight parameters corresponding to the specified test scenario; Grouping all test models according to the weight parameters; Sort each group of models according to preset indicators; Return the sorted grouping results to the client and display them as model recommendation results; Wherein, the weight parameter refers to the parameter type of the test model; The grouping of all test models according to the weight parameters includes: Get the parameters of all test models saved in the database; The models with the same weight parameters are divided into an equivalence class, thereby dividing all the test models into multiple model groups.
2. The test model recommendation method according to claim 1, characterized in that: After sorting each group of models according to the preset indicators, the method further includes: Identify the models of mainstream manufacturers that rank high under the preset indicators, mark them in the grouping results, and give them priority recommendation.
3. The test model recommendation method according to claim 1 or 2, characterized in that: Before providing the test scenario options to the client, the method further includes: Collect a list of models that had problems in historical tests; After sorting each group of models according to the preset indicators, it also includes: According to the list, the models with the most problems in the test scenario are marked in the grouping results and recommended first.
4. The test model recommendation method according to claim 1, characterized in that: After obtaining the weight parameter corresponding to the specified test scenario, the method further includes: Receive a user's modification of the weight parameter.
5. The test model recommendation method according to claim 1, characterized in that: When the test scenario options provided do not include the test scenario required by the user, the method further includes: Receive user-defined test scenarios and corresponding weight parameters.
6. The test model recommendation method according to claim 1, characterized in that: The weight parameters are device parameters of two dimensions of the electronic device corresponding to the test scenario.
7. The test model recommendation method according to claim 1, characterized in that: The preset indicator is market share ranking.
8. A test model recommendation system, characterized in that: The system comprises: Maintenance module, used to maintain various test scenarios and corresponding weight parameters; The receiving module is used to provide the test scenario options to the client and receive the specified test scenario selected by the user; An acquisition module, configured to acquire weight parameters corresponding to the specified test scenario; A grouping module, used for grouping all test models according to the weight parameters; Sorting module, used to sort each group of models according to preset indicators; The return module is used to return the sorted group results to the client and display them as model recommendation results; Wherein, the weight parameter refers to the parameter type of the test model; The grouping of all test models according to the weight parameters includes: Get the parameters of all test models saved in the database; The models with the same weight parameters are divided into an equivalence class, thereby dividing all the test models into multiple model groups.
9. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a test model recommendation program stored in the memory and executable on the processor. When the test model recommendation program is executed by the processor, the test model recommendation method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a test model recommendation program, which, when executed by a processor, implements the test model recommendation method according to any one of claims 1 to 7.
11. A computer program product, characterized in that The invention comprises a test model recommendation program, which implements the test model recommendation method according to any one of claims 1 to 7 when executed by a processor.