Interface test method and device, storage medium and program product

Abstract

The embodiment of the invention provides an interface test method and device, a storage medium and a program product, and the method comprises the steps: obtaining at least one interface operation instruction which corresponds to an interface test task and is described in a natural language, and obtaining at least one interface operation instruction according to target model scores corresponding to a plurality of pre-configured test models; and determining a first test model for executing the interface test task from the plurality of test models. And according to the at least one interface operation instruction, generating a first prompt word, and obtaining a first page screenshot corresponding to the current page of the to-be-tested application. And inputting the first cue word and the first page screenshot into a first test model to determine a first interface operation instruction to be executed currently from the at least one interface operation instruction by utilizing the first test model according to the first page screenshot, and generating a second interface operation instruction which corresponds to the first interface operation instruction and is described in a code language. And sending the second interface operation instruction to the to-be-tested application so as to automatically test the to-be-tested control.

Description

Technical Field

[0001] This application relates to the field of interface testing technologies, and in particular, to an interface testing method, device, storage medium, and program product. Background Art

[0002] With the continuous improvement of terminal capabilities, the user interaction paths provided by the user interfaces (User Interface, abbreviated as UI) of various applications (such as Web applications, mobile applications, etc.) are becoming more and more diverse. For example, users can perform interface interactions through methods such as clicking, swiping, text input, and voice input. In related technologies, the interface functions are tested manually, which takes a long time and it is difficult to cover all scenarios. Summary of the Invention

[0003] Embodiments of this application provide an interface testing method, device, storage medium, and program product to achieve efficient and accurate interface automated testing.

[0004] In a first aspect, embodiments of this application provide an interface testing method, and the method includes:

[0005] Obtain at least one interface operation instruction described in natural language corresponding to an interface testing task;

[0006] Determine a first test model for executing the interface testing task from the multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models;

[0007] Generate a first prompt word according to the at least one interface operation instruction;

[0008] Obtain a first page screenshot corresponding to the current page of the application to be tested, where the first page screenshot contains an image corresponding to a control to be detected;

[0009] Input the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine, according to the first page screenshot, a first interface operation instruction to be currently executed from the at least one interface operation instruction, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction;

[0010] Send the second interface operation instruction to the application to be tested to test the control to be detected, and the second interface operation instruction is used to determine an interface interaction operation for the control to be detected.

[0011] In a second aspect, embodiments of this application provide an interface testing device, and the device includes:

[0012] An acquisition module that acquires at least one interface operation instruction described in natural language corresponding to an interface test task; and determines a first test model for executing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models;

[0013] A processing module, configured to generate a first prompt word according to the at least one interface operation instruction; and acquire a first page screenshot corresponding to the current page of the application under test, where the first page screenshot includes an image corresponding to the control to be detected;

[0014] A test module, configured to input the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine, according to the first page screenshot, a first interface operation instruction to be currently executed from the at least one interface operation instruction, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction; and send the second interface operation instruction to the application under test to test the control to be detected, where the second interface operation instruction is used to determine an interface interaction operation for the control to be detected.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a communication interface; wherein, a computer program is stored on the memory, and when the computer program is executed by the processor, the processor can at least implement the interface test method as described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor of an electronic device, the processor can at least implement the interface test method as described in the first aspect.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, including: a computer program or instruction, and when the computer program or instruction is executed by a processor of an electronic device, the processor can at least implement the interface test method as described in the first aspect.

[0018] In the solution provided by the embodiments of the present application, for a to-be-tested application that needs to be subjected to interface testing, first, at least one interface operation instruction described in natural language corresponding to the interface testing task is obtained, and a first testing model for executing the interface testing task is determined from multiple testing models according to the target model scores respectively corresponding to the multiple pre-configured testing models. For example, a testing model with a higher target model score can be determined from the multiple testing models as the first testing model. The higher the target model score, the stronger the ability of the corresponding testing model to execute the interface testing task. For example, the testing efficiency and accuracy are higher, etc. Then, according to the at least one interface operation instruction, a first prompt word is generated, and a first page screenshot corresponding to the current page of the to-be-tested application is obtained, where the first page screenshot contains an image corresponding to the to-be-detected control. After that, the first prompt word and the first page screenshot are input into the first testing model, so as to utilize the natural language understanding ability of the first testing model to determine, according to the first page screenshot, a first interface operation instruction to be currently executed from the at least one interface operation instruction, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction. Finally, the second interface operation instruction is sent to the to-be-tested application to test the to-be-detected control, and the second interface operation instruction is used to determine the interface interaction operation for the to-be-detected control. In this solution, the first testing model understands at least one interface operation instruction corresponding to the interface testing task, and combines the first page screenshot currently corresponding to the to-be-detected model to generate a second interface operation instruction corresponding to the first interface operation instruction to be currently executed, so that the to-be-tested application can realize the testing of the to-be-detected control by executing the second interface operation instruction. In the process of performing interface testing on the to-be-tested application in the embodiments of the present application, interface testing can be completed without human participation, which can not only save the labor cost of interface testing, but also ensure the efficiency and accuracy of interface testing. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of an interface testing method provided by an embodiment of the present application;

[0021] Figure 2 It is a flowchart of another interface testing method provided by an embodiment of the present application;

[0022] Figure 3 It is a flowchart of a method for calculating the target model score provided by an embodiment of the present application;

[0023] Figure 4 The structural schematic diagram of an interface testing device provided by an embodiment of the present application;

[0024] Figure 5 For Figure 4 The structural schematic diagram of an electronic device corresponding to the interface testing device provided by the illustrated embodiment. Specific embodiments

[0025] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0026] It should be noted that in the case where the embodiments of the present application involve user information, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the user or fully authorized by all parties. Additionally, the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation entrances are provided for users to select authorization or rejection. Moreover, various models (including but not limited to language models or large models) involved in the present application comply with relevant laws and standards.

[0027] In addition, the step timings in the following method embodiments are only examples, rather than strict limitations.

[0028] The interface testing method provided by the embodiments of the present application can be executed by an electronic device, which can be a terminal device such as a PC or a laptop, or a server. The server can be a physical server including an independent host, or can also be a virtual server, or can also be a cloud server or a server cluster. For ease of description, in the embodiments of the present application, the electronic device used to execute the interface testing method is referred to as a testing device.

[0029] Optionally, the testing device can execute the interface testing method provided by the embodiments of the present application through an AI intelligent agent (Artificial Intelligence Agent) deployed locally; or, the testing device controls the AI intelligent agent to execute the interface testing method provided by the embodiments of the present application by calling the application programming interface (Application Programming Interface, abbreviated as API) opened to the outside world by the AI intelligent agent.

[0030] Among them, an AI intelligent agent is an abstract entity that can obtain external input information, process information, make decisions, and take actions. This abstract entity can be a software program, an algorithm module, or any other form of computer object. The AI intelligent agent can learn from data through machine learning algorithms and continuously optimize its own performance. When called, the AI intelligent agent can obtain the information input by the caller and perform operations such as data analysis, pattern recognition, and natural language processing based on the input information. When processing the input information, the AI intelligent agent autonomously uses various algorithms and models (such as machine learning models, deep learning models, reinforcement learning models, etc.), and can then independently complete tasks without manual intervention.

[0031] The following will further illustrate the interface testing method provided in the embodiments of the present application with reference to the accompanying drawings.

[0032] Figure 1 It is a flowchart of an interface testing method provided in the embodiments of the present application. As Figure 1 shown, it may include the following steps:

[0033] 101. Obtain at least one interface operation instruction described in natural language corresponding to the interface testing task.

[0034] 102. Determine a first test model for performing the interface testing task from multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models.

[0035] 103. Generate a first prompt word according to at least one interface operation instruction; obtain a first page screenshot corresponding to the current page of the application to be tested, and the first page screenshot contains an image corresponding to the control to be detected.

[0036] 104. Input the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine, according to the first page screenshot, a first interface operation instruction to be currently executed from at least one interface operation instruction, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction.

[0037] 105. Send the second interface operation instruction to the application to be tested to test the control to be detected, and the second interface operation instruction is used to determine the interface interaction operation for the control to be detected.

[0038] In the embodiments of the present application, interface testing refers to testing the controls (such as input boxes, buttons, etc.) in the user interface provided by an application program, including but not limited to: verifying whether the controls can correctly implement their designed functions, checking whether the response behaviors of the controls under corresponding interaction operations meet expectations, determining whether the controls can correctly process input information and provide appropriate feedback, etc.

[0039] In this solution, the application program that needs to be subjected to interface testing is called the application to be tested. Optionally, the application to be tested includes but is not limited to: desktop application programs, mobile application programs, Web application programs, embedded application programs, etc. The controls that need to be tested in the interface of the application to be tested are called the controls to be tested.

[0040] Among them, the application to be tested runs on terminal devices such as a PC, a laptop, a mobile phone, and a smart home appliance. Optionally, the terminal device and the above-mentioned test device can be communicatively connected by accessing a wireless network based on a communication standard, such as WiFi, 2G, 3G, 6G / LTE, 5G and other mobile communication networks, or a combination thereof, and information transmission is performed based on the communication connection.

[0041] In the interface testing method provided by the embodiments of the present application, first, in response to an interface testing task triggered for the application to be tested, at least one interface operation instruction described in natural language corresponding to the interface testing task is obtained.

[0042] In actual applications, for the same application to be tested, one or more interface testing tasks can be triggered correspondingly, and one interface testing task can correspondingly include one or more interface operation instructions described in natural language. For example, for a certain application to be tested, the corresponding interface testing task and the corresponding interface operation instructions can be:

[0043] Interface testing task 1: Click on the "Housekeeping" icon, click on the self-operated "Part-time Maid" icon, swipe up the page to find the cleaning service in the xx area, and click on the favorite icon.

[0044] Interface testing task 2: Enter the local service page, click on the "Home Repair" icon, swipe up the page to find the "New Year Enjoy Life" banner and click on the banner.

[0045] Since the interface testing process is the same when different interface testing tasks are executed by using the interface testing method provided by the embodiments of the present application, in the embodiments of the present application, a single interface testing task is taken as an example for illustration. Optionally, when there are multiple interface testing tasks, the interface testing method provided by the embodiments of the present application can be used to execute multiple interface testing tasks in parallel or serially.

[0046] Optionally, at least one interface operation instruction described in natural language corresponding to the interface test task may be manually written by a tester based on actual interface test requirements, or may also be automatically generated according to interface test requirements. The embodiments of the present application do not limit the acquisition method of at least one interface operation instruction.

[0047] In the embodiments of the present application, after obtaining at least one interface operation instruction described in natural language corresponding to the interface test task, through the natural language understanding ability of a pre-configured test model, the interface operation instruction described in natural language is converted into an interface operation instruction described in code language, so that the application under test performs corresponding interface interaction operations on the control to be detected based on the interface operation instruction described in code language, thereby completing the test of the control to be detected.

[0048] It is easy to understand that one or more interface operation instructions described in natural language corresponding to the interface test task are executed sequentially, that is, one interface operation instruction is executed at a time, and the execution of the subsequent interface operation instruction may depend on the execution result of the previous interface operation instruction. For example, in the interface test task 1 mentioned above, "Swipe up the page to find the cleaners in xx area and click the favorite icon". Based on this, when the number of at least one interface operation instruction described in natural language corresponding to the interface test task is multiple, the test model is also used to determine the currently to-be-executed interface operation instruction described in natural language from these multiple interface operation instructions.

[0049] Optionally, the test model involved in the embodiments of the present application may be a language model (LM) or a multimodal model (MM) based on artificial intelligence, etc. The embodiments of the present application do not limit the number of model parameters supported by the model, aiming to meet actual requirements. If the model parameters are relatively large, the scale of the model will be relatively large and the model performance will be relatively better. Of course, more time and resources will be consumed during reasoning or training; if the model parameters are relatively small, the scale of the model will be relatively small. When the performance meets the requirements, the model is more lightweight and consumes relatively less time and resources during reasoning or training. The test model may be a deep learning model used to process and generate natural language text or multimodal data, which can be implemented based on a neural network architecture and can be pre-trained on a large amount of data.

[0050] In an alternative implementation, the test model may include an Encoder, a Decoder, a Self-Attention Layer, a Feed-Forward Neural Network, etc. The encoder is mainly used to convert input data (usually in the form of a sequence) into a vector representation, and this process can capture the semantic features of the input data. The decoder is responsible for converting the intermediate representation generated by the encoder into output data (usually in the form of a sequence). The self-attention layer is a mechanism that enables the model to focus on other positions in the sequence to better encode the information at the current position. The feed-forward neural network can perform non-linear transformations on the output of the self-attention layer, etc., to enhance the model's expressive ability. All parts work together so that the model built based on them can perform well in various complex processing tasks, such as natural language processing, computer vision, speech recognition, machine translation, text summarization, and intelligent question answering, etc.

[0051] In the embodiments of the present application, to improve the efficiency and accuracy of interface testing, multiple test models are pre-configured, and different test models have different model characteristics. For example, the usage costs of the models are different, the inference accuracies, inference times of the models are different, and the memory footprints of the models are different. By deploying different test models, it is convenient to custom-select a suitable test model for performing the interface test task during interface testing.

[0052] As an alternative approach, the target model scores corresponding to the multiple pre-configured test models can be determined through custom model evaluation metrics; then, based on the target model scores, the first test model for performing the interface test task is determined from the multiple test models. For example, the test model with the highest target model score is selected as the first test model, etc. In the embodiments of the present application, the focus is first on the execution of the interface test process, and the calculation process of the target model score will be specifically described in subsequent embodiments.

[0053] After that, according to at least one interface operation instruction, a first prompt word for inputting into the first test model is generated.

[0054] It can be understood that the interface test method provided by the embodiments of the present application is used to test the to-be-tested controls on the page of the to-be-tested application. Based on this, the first test model needs to know which to-be-tested controls are included in the current page of the to-be-tested application and the positions of the to-be-tested controls in order to accurately generate interface operation instructions described in code language.

[0055] Therefore, further, it is necessary to obtain a first page screenshot corresponding to the current page of the application under test, where the first page screenshot contains an image corresponding to the control to be detected. Optionally, the test device may send a screenshot instruction to the terminal device running the application under test, so that the terminal device takes a screenshot to obtain the first page screenshot and feeds back the first page screenshot to the test device.

[0056] It should be noted that in the embodiments of the present application, the screenshots corresponding to the current page of the application under test are collectively referred to as the first page screenshots. The pages corresponding to the first page screenshots obtained at different time points may be the same or different. For example, if the current page of the application under test at time t1 is page 1, then the first page screenshot obtained at time t1 is the screenshot of page 1; if the current page of the application under test at time t2 is page 2, then the first page screenshot obtained at time t2 is the screenshot of page 2, and so on.

[0057] Among them, as an optional way to generate the first prompt word, at least one interface operation instruction can be filled into the corresponding slot in the preset first prompt word template to generate the first prompt word. Optionally, the first prompt word template may include an interface test task description, an input content slot, an output content description, etc.

[0058] For example, in the first prompt word template, the interface test task description may be: "You are an agent trained to complete interface test tasks on a terminal device. You will be given a page screenshot of the application under test in the terminal device, and the page screenshot contains an image of an interactive interface control. The task you need to complete is: select an interface operation instruction to be executed on the current page from the input content, and ensure that the selected interface operation instruction does not repeat the executed interface operation instruction"; the input content slot is used to fill in at least one interface operation instruction described in natural language; the output content description may be: "An interface operation instruction described in code language carrying control coordinate information, or output that the task has been completed or no operation is required". It should be noted that the first prompt word template here is only for illustrative purposes and is not limited thereto.

[0059] After generating the first prompt word and obtaining the first page screenshot corresponding to the current page of the application under test, the first prompt word and the first page screenshot are input into the first test model. Using the first test model, based on the control image included in the first page screenshot and the coordinate information corresponding to the control image, the control to be detected is determined; the first interface operation instruction to be currently executed is determined from at least one interface operation instruction, and a second interface operation instruction described in code language corresponding to the first interface operation instruction is generated. Among them, the second interface operation instruction contains the coordinate information of the control to be detected and can be used to determine the interface interaction operation for the control to be detected.

[0060] Finally, send the second interface operation instruction to the application under test, so that the application under test tests the control to be detected by executing the second interface operation instruction. In response to the execution of the second interface operation instruction, it is determined that the execution of the first interface operation instruction is completed.

[0061] In the above embodiments, the execution process of a certain interface operation instruction described in natural language (i.e., the first interface operation instruction) in a single interface test task is taken as an example for illustration.

[0062] It is easy to understand that after the execution of the first interface operation instruction, further, the first page screenshot corresponding to the current page of the application under test can be re-obtained, and the re-obtained first page screenshot and the first prompt word are input into the first test model to repeat the above steps 104 and 105 until at least one interface operation instruction described in natural language is executed.

[0063] In summary, in the solution provided by the embodiments of the present application, the first test model is used to understand at least one interface operation instruction corresponding to the interface test task, and in combination with the first page screenshot currently corresponding to the model to be detected, a second interface operation instruction corresponding to the currently to-be-executed first interface operation instruction is generated, so that the application under test can realize the automated test of the control to be detected by executing the second interface operation instruction, without human participation, which can not only save the labor cost of interface testing, but also ensure the efficiency and accuracy of interface testing.

[0064] In practical applications, in order to ensure the correct execution of the interface test task, after executing an interface operation instruction, further, on the one hand, the executed interface operation instructions can be summarized, and the summary result is input to the first test model as context information to guide it to continue processing the remaining unexecuted interface operation instructions; on the other hand, the execution result of the executed interface operation instruction (such as: the first interface operation instruction) can also be verified, and the verification result is input to the first test model as context information to guide it to continue processing the remaining unexecuted interface operation instructions.

[0065] Optionally, the above summary task and verification task can be completed by the first test model, or can be completed by other test models other than the first test task. It can be understood that when a model executes multiple different tasks, the model may perform excellently in some tasks and poorly in other tasks, and this performance trade-off may have an adverse impact on the accuracy and efficiency of interface testing.

[0066] Based on this, in the embodiments of the present application, summary tasks and verification tasks are performed by test models other than the first test task. For ease of description, the test model used to process the summary task is called the natural language summary model, and the test model used to process the verification task is called the second test model.

[0067] The following expands on the interface test method combining the natural language summary model and the second test model.

[0068] Figure 2 It is a flowchart of another interface test method provided by the embodiments of the present application. As Figure 2 shown, it may include the following steps:

[0069] 201. Obtain at least one interface operation instruction described in natural language corresponding to the interface test task.

[0070] 202. Determine the first test model, natural language summary model, and / or second test model for performing the interface test task from multiple test models according to the target model scores respectively corresponding to the multiple test models.

[0071] Among them, the multiple test models include test models for performing different tasks, and each task corresponds to at least one test model.

[0072] In the specific implementation process, the target test model for performing the interface test task can be determined from the test models for performing the target task according to the target model scores respectively corresponding to the test models for performing the target task. Among them, the target task can be any one of the instruction conversion task (i.e., converting the interface operation instruction in natural language into the interface operation instruction in code language), summary task, or verification task. Correspondingly, the target test model is the first test model, natural language summary model, or second test model.

[0073] Optionally, the task types that any test model can perform can be associated and stored with the test model. Thus, it is convenient to screen out the target test model for performing the interface test task from the multiple pre-configured test models when performing the interface test task.

[0074] 203. Generate a first prompt word according to at least one interface operation instruction; obtain a first page screenshot corresponding to the current page of the application to be tested, and the first page screenshot contains the image corresponding to the control to be detected.

[0075] 204. Input the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine the first interface operation instruction to be currently executed from at least one interface operation instruction according to the first page screenshot, and generate a second interface operation instruction corresponding to the first interface operation instruction and described in code language.

[0076] 205. Send the second interface operation instruction to the application under test to test the control to be detected. The second interface operation instruction is used to determine the interface interaction operation for the control to be detected.

[0077] Among them, the specific implementation processes of steps 201 to 205 can refer to the foregoing embodiments, and will not be elaborated in this embodiment.

[0078] 206. Update the first prompt word by using the natural language summary model and / or the second test model.

[0079] 207. Input the updated first prompt word and the newly obtained first page screenshot into the first test model, so that the first test model outputs the interface operation instructions corresponding to the unexecuted interface operation instructions and described in code language; in response to the prompt information that all at least one interface operation instruction output by the first test model has been executed, determine that the interface test task is completed.

[0080] Among them, the specific implementation processes of steps 201 to 205 can refer to the foregoing embodiments, and will not be elaborated in this embodiment.

[0081] In the embodiments of the present application, the natural language summary model is used to summarize the executed interface operation instructions, and the second test model is used to verify the execution result of the first interface operation instruction. Further, by adding the summary result and / or the verification result to the first prompt word, the first prompt word is updated to provide more context information for the first test model to continue processing the remaining unexecuted interface operation instructions.

[0082] The processing process of the natural language summary model will be described below first.

[0083] In an optional embodiment, the natural language summary model can be used to generate an instruction summary corresponding to the historical interface operation instruction according to the historical interface operation instruction that has been executed among at least one interface operation instruction. Among them, the historical interface operation instruction includes the first interface operation instruction.

[0084] For ease of understanding, for example, it is assumed that at least one interface operation instruction includes instruction 1, instruction 2, instruction 3,..., instruction n (n is a positive integer), the first interface operation instruction is instruction 3, and instruction 1 and instruction 2 have been executed in sequence before executing instruction 3.

[0085] Based on this assumption, after sending the second interface operation instruction corresponding to instruction 3 to the application under test, the historical interface operation instructions are instruction 1, instruction 2, and instruction 3. Instruction 1, instruction 2, and instruction 3 can be input into the natural language summary model so that the natural language summary model generates the instruction summaries corresponding to instruction 1, instruction 2, and instruction 3; alternatively, the instruction summaries corresponding to instruction 1 and instruction 2, and instruction 3 can also be input into the natural language summary model so that the natural language summary model generates the instruction summaries corresponding to instruction 1, instruction 2, and instruction 3.

[0086] Among them, the instruction summaries corresponding to instruction 1 and instruction 2 are generated by the natural language summary model according to instruction 1 and instruction 2 after sending the interface operation instruction described in code language corresponding to instruction 2 to the application under test.

[0087] In fact, the instruction summary is continuously updated as the interface operation instructions in at least one interface operation instruction are executed.

[0088] Next, the processing process of the second test model will be described.

[0089] In an optional embodiment, first, obtain the second page screenshot corresponding to the application under test after executing the second interface operation instruction; then, generate a second prompt word according to at least one interface operation instruction; finally, input the second prompt word, the first page screenshot, and the second page screenshot into the second test model, so as to use the second test model to determine the execution result and corresponding execution suggestions of the first interface operation instruction according to the first page screenshot and the second page screenshot.

[0090] Among them, the generation process of the second prompt word is similar to the generation process of the first prompt word. At least one interface operation instruction can be filled into the corresponding slot in the preset second prompt word template to generate the second prompt word. Optionally, the second prompt word template may include an interface test task description, an input content slot, an output content description, etc.

[0091] For example, in the second prompt template, the description of the interface test task can be: "You are an agent trained to complete interface test tasks on a terminal device. You will be provided with two screenshots of the pages of the application to be tested on the terminal device before and after executing a certain interface operation instruction, as well as the interface operation instruction in the input content. Your task is to analyze the differences in aspects such as the overall layout, content, element styles, background colors, etc. between the two screenshots, avoid focusing solely on changes in individual control icons, determine the execution result of a certain interface operation instruction in the input content, and give suggestions for the next step of execution"; the input content slot is used to fill in at least one interface operation instruction described in natural language; the description of the output content can be: "Describe the execution result of a certain interface operation instruction and the corresponding execution suggestions in natural language". It should be noted that the second prompt template here is only for illustrative purposes and is not limited thereto.

[0092] In the specific implementation process, optionally, the execution result of the first interface operation instruction and the corresponding execution suggestions include, but are not limited to: the execution of the first interface operation instruction is incorrect, and it is recommended to return to the page corresponding to the first screenshot and re-execute the first interface operation instruction; or, the execution of the first interface operation instruction is correct, and it is recommended to execute other interface operation instructions among at least one interface operation instruction; or, the execution of the first interface operation instruction has no result, and it is recommended to re-execute the first interface operation instruction.

[0093] After obtaining the instruction summary and / or the execution result of the first interface operation instruction and the corresponding execution suggestions, further, add the instruction summary and / or the execution result of the first interface operation instruction and the corresponding execution suggestions to the first prompt to update the first prompt.

[0094] In response to the update of the first prompt, re-obtain the first screenshot corresponding to the current page of the application to be tested, and input the updated first prompt and the re-obtained first screenshot into the first test model, so that the first test model, based on the re-obtained first screenshot, determines the currently pending interface operation instruction from the unexecuted interface operation instructions included in at least one interface test instruction, and generates the corresponding interface operation instruction described in code language, that is, repeat the above steps 204 and 205.

[0095] In response to the first test model outputting a prompt message indicating that all at least one interface operation instruction has been executed, determine that the interface test task is completed.

[0096] As introduced above for the interface testing method provided in the embodiments of the present application, the first test model is used to process information of the data type of "natural language + single picture", the second test model is used to process information of the data type of "natural language + multiple pictures", and the natural language summary model is used to process information of the data type of "natural language".

[0097] Based on this, in an optional embodiment, the method of determining a target test model for performing an interface testing task from multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models includes:

[0098] Filter at least one test model that matches the target data type from the multiple test models according to the target data type to be processed for the interface testing task; then, determine the target test model for performing the interface testing task from the at least one test model according to the target model scores corresponding to the at least one test model.

[0099] Among them, the target data type can be any one of "natural language + single picture", "natural language + multiple pictures", and "natural language"; the target test model can be any one of the first test model, the natural language summary model, or the second test model.

[0100] For example, if the target data type is "natural language + single picture", at least one test model that matches the data type of "natural language + single picture" can be filtered from the multiple test models; then, according to the target model scores corresponding to the at least one test model, the first test model for performing the interface testing task is determined from the at least one test model. Among them, the target model score of the first test model is greater than the target model scores of other test models in the at least one test model.

[0101] If the target data type is "natural language + multiple pictures", at least one test model that matches the data type of "natural language + multiple pictures" can be filtered from the multiple test models; then, according to the target model scores corresponding to the at least one test model, the second test model for performing the interface testing task is determined from the at least one test model. Among them, the target model score of the second test model is greater than the target model scores of other test models in the at least one test model.

[0102] If the target data type is "natural language", at least one test model that matches the data type of "natural language" can be selected from multiple test models; then, according to the target model scores corresponding to the at least one test model, a natural language summary model for performing the interface test task is determined from the at least one test model. Among them, the target model score of the natural language summary model is greater than the target model scores of other test models in the at least one test model.

[0103] Optionally, the data types that any test model can handle can be associated and stored with the test model. Thus, it is convenient to screen out the target test model for performing the interface test task from multiple pre-configured test models when performing the interface test task.

[0104] In this solution, the executed interface operation instructions are summarized by the natural language summary model, and the execution result of the first interface operation instruction is verified by the second test model. Further, by adding the summary result and / or the verification result to the first prompt word, the first prompt word is updated to provide more context information for the first test model to continue processing the remaining unexecuted interface operation instructions, thereby effectively improving the efficiency and accuracy of the interface test.

[0105] The execution process of the interface test method provided in the embodiments of the present application is described above. Next, the calculation process of the target model score corresponding to the test model is described.

[0106] Figure 3 For a method flowchart for calculating the target model score provided in the embodiments of the present application, as Figure 3 shown, the following steps may be included:

[0107] 301. Determine the initial model scores corresponding to multiple test models respectively according to the static model evaluation indicators and the first weights corresponding to the multiple pre-configured test models.

[0108] 302. Determine the target model scores corresponding to multiple test models respectively according to the initial model scores, dynamic model evaluation indicators, and the second weights corresponding to the multiple test models; wherein, the dynamic model evaluation indicators change dynamically within a preset index value range according to the initial index values and the execution results of at least one interface operation instruction.

[0109] As described above, the target model score is used to select the test model for performing the interface test task. It can be understood that the more accurately the target model score can reflect the ability of the corresponding test model to process the interface test task, the more beneficial it is to the accurate and efficient execution of the interface test.

[0110] Based on this, in an optional embodiment, when deploying each test model, the basic information corresponding to each test model can be obtained synchronously, such as: model identifier, applicable task type (or data type that can be processed, etc.), computing cost, model accuracy, model performance, memory occupancy information, etc.

[0111] Among them, the model identifier is used to uniquely identify the corresponding test model. For example, it can be a model ID, etc. The applicable task type is the instruction conversion task, summarization task, verification task, etc. described above; the data type that can be processed is the "natural language + single image", "natural language + multiple images", "natural language", etc. described above. The applicable task type and the data type that can be processed are used to determine a test model that matches the task type or data type corresponding to the interface test task from multiple pre-configured test models. The computing cost includes input cost and output cost, which is an economic indicator used to measure the consumption of computing resources related to input and output during the operation of the model. It is usually expressed in terms of the cost per thousand Tokens, reflecting the value of the resources required for the model to process input data and generate output data. The model accuracy is used to describe the degree of consistency between the model prediction result and the actual result. The model performance is used to describe the efficiency and response speed during the operation of the model, involving aspects such as training time and inference time. The memory occupancy information is used to describe the size of the storage space required during the operation of the model, including the model itself and its data structure during operation.

[0112] When calculating the target model score corresponding to the test model, at least one piece of information can be custom-selected from the basic information corresponding to the test model as a model evaluation index for calculating the target model score.

[0113] In an optional embodiment, information that remains basically stable after the model is deployed can be selected from the basic information corresponding to the test model, such as: input cost, output cost, model accuracy, model performance, memory occupancy information, etc., as static model evaluation indexes for calculating the target model score. Further, the initial model score corresponding to each test model can be calculated by setting corresponding weights for different static model evaluation indexes and performing weighted summation.

[0114] Specifically, the initial model score = a * (1 / input cost) + b * (1 / output cost) + c * model accuracy + d * (1 / model performance) + e * (1 / memory occupancy information). Among them, a, b, c, d, and e are the weights corresponding to the input cost, output cost, model accuracy, model performance, and memory occupancy information respectively, and a + b + c + d + e = 1.

[0115] Optionally, the weight preference can be configured by customizing the weights. For example, setting a = b = 0.35 indicates that the calculation of the initial model score focuses on calculation costs, etc.

[0116] It should be noted that the above calculation of the initial model score takes static model evaluation metrics including input cost, output cost, model accuracy, model performance, and memory occupancy information as examples for illustrative purposes, but is not limited thereto.

[0117] Optionally, the above initial model score can be directly used as the target model score corresponding to the test model, and the first test model, the second test model, or the natural language summary model for performing the interface test task can be determined from multiple test models.

[0118] It can be understood that during the use of the test model, it may perform excellently or poorly when performing the interface test task. To improve the accuracy of test model selection, in another optional embodiment, based on the above static model evaluation metrics, dynamic model evaluation metrics can be added. Among them, the dynamic model evaluation metrics change dynamically within a preset metric value range according to the initial metric value and the execution results of at least one interface operation instruction, so as to realize the dynamic adjustment of the target model score based on the execution results of the test model.

[0119] Optionally, the dynamic model evaluation metric can be: model usage priority. Among them, the model usage priority is used to describe the usage order of the model. The higher the model usage priority, the more likely it is to be determined as the test model for processing the interface test task.

[0120] As mentioned above, the execution result corresponding to a certain interface operation instruction (such as: the first interface operation instruction) output by the second test model can be: the interface operation instruction execution error, the interface operation instruction execution is correct, the interface operation instruction execution has no result, etc.

[0121] Based on this, for the target test model determined to be used for performing the interface test task (which can be any one of the first test model, the second test model, or the natural language summary model), during the process of performing the interface test task, the model usage priority corresponding to the target test model can be dynamically updated, so that the corresponding test model can be better selected when the next interface test task is executed.

[0122] As an optional way to dynamically update the model usage priority corresponding to the target test model, specifically, when the execution result corresponding to a certain interface operation instruction in at least one interface operation instruction is that the interface operation instruction execution is incorrect, subtract a first value (such as: 0.15) from the model usage priority currently corresponding to the target test model (such as: 1.0) to update the model usage priority currently corresponding to the target test model; when the execution result corresponding to a certain interface operation instruction in at least one interface operation instruction is that the interface operation instruction execution is correct, add a second value (such as: 0.1) to the model usage priority currently corresponding to the target test model (such as: 1.0) to update the model usage priority currently corresponding to the target test model; when the execution result corresponding to a certain interface operation instruction in at least one interface operation instruction is that there is no result for the interface operation instruction execution, keep the model usage priority currently corresponding to the target test model unchanged.

[0123] Among them, the model usage priority corresponding to the target test model in the initial state is a preset initial index value, such as: 0.8; when the model usage priority currently corresponding to the target test model is dynamically updated according to the execution result corresponding to the interface operation instruction, it changes dynamically within a preset index value range, such as: the range [0.5, 1.5], indicating that the model usage priority corresponding to the target test model is at least not lower than 0.5 and at most not higher than 1.5.

[0124] From the above introduction of the dynamic model evaluation index taking the model usage priority as an example, it can be seen that the dynamic model evaluation index can effectively reflect the historical performance of the test model in executing the interface test task. Based on this, combining the initial model score and the dynamic model evaluation index can calculate the target model score used to reflect the ability of the test model to process the interface test task more accurately.

[0125] Optionally, the target model score corresponding to each test model can be calculated by setting corresponding weights for the initial model score and the dynamic model evaluation index and performing weighted summation.

[0126] Specifically, the target model score = f * initial model score + g * dynamic model evaluation index. Among them, f and g are the weights corresponding to the initial model score and the dynamic model evaluation index respectively, and f + g = 1.

[0127] Optionally, the weight preference can be configured by customizing the weights. For example, setting g = 0.2 means that the dynamic model evaluation index will not overly dominate the static model evaluation index, but can effectively reflect the historical performance of the model, etc.

[0128] Based on the above introduction of the initial model score and the dynamic model evaluation index, in the specific implementation process, taking the interface operation instruction as the first interface operation instruction as an example, the dynamic model evaluation index corresponding to the target test model can be updated according to the execution result of the first interface operation instruction. In response to the update of the dynamic model evaluation index, the target model score corresponding to the target test model is updated. The target test model is any one of the first test model, the second test model, and the natural language summary model.

[0129] In summary, the embodiment of the present application can effectively avoid the situation of blindly invoking multiple pre-configured test models by calculating the target model score corresponding to the test model based on the static model evaluation index and the dynamic model evaluation index and dynamically updating the target model score. When determining the model for performing the interface test task, it can not only consider the inherent performance of the model, such as calculation cost, model accuracy, etc., but also consider the historical performance of the model. Therefore, the efficiency and accuracy of the interface test task execution can be effectively enhanced.

[0130] The interface test device of one or more embodiments of the present application will be described in detail below. Those skilled in the art can understand that these devices can be configured by using commercially available hardware components through the steps taught by this solution.

[0131] Figure 4 The structural schematic diagram of an interface test device provided by the embodiment of the present application is as Figure 4 shown. The device includes: an acquisition module 11, a processing module 12, and a test module 13.

[0132] The acquisition module 11 acquires at least one interface operation instruction described in natural language corresponding to the interface test task; and determines a first test model for performing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models.

[0133] The processing module 12 is configured to generate a first prompt word according to the at least one interface operation instruction; and acquire a first page screenshot corresponding to the current page of the application to be tested, where the first page screenshot includes an image corresponding to the control to be detected.

[0134] The test module 13 inputs the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine a first interface operation instruction to be currently executed from the at least one interface operation instruction according to the first page screenshot, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction; and send the second interface operation instruction to the application to be tested to test the control to be detected, where the second interface operation instruction is used to determine the interface interaction operation for the control to be detected.

[0135] In an optional embodiment, the obtaining module 11 is further configured to: determine, from the multiple test models, a natural language summary model and / or a second test model for performing the interface test task according to the target model scores respectively corresponding to the multiple pre-configured test models.

[0136] Correspondingly, the test module 13 is further configured to: after sending the second interface operation instruction to the application under test, update the first prompt word by using the natural language summary model and / or the second test model; input the updated first prompt word and the re-obtained first page screenshot into the first test model, so that the first test model outputs an interface operation instruction described in code language corresponding to the unexecuted interface operation instruction; and in response to the first test model outputting a prompt message indicating that all of the at least one interface operation instruction has been executed, determine that the interface test task is completed.

[0137] In an optional embodiment, when the test module 13 updates the first prompt word by using the natural language summary model and / or the second test model, the test module 13 is specifically configured to: generate, by using the natural language summary model, an instruction summary corresponding to the historical interface operation instruction according to the historical interface operation instruction that has been executed in the at least one interface operation instruction, where the historical interface operation instruction includes the first interface operation instruction; and / or obtain a second page screenshot corresponding to the application under test after the second interface operation instruction is executed; generate a second prompt word according to the at least one interface operation instruction; input the second prompt word, the first page screenshot, and the second page screenshot into the second test model, so as to use the second test model to determine, according to the first page screenshot and the second page screenshot, an execution result of the first interface operation instruction and a corresponding execution suggestion; and add the instruction summary and / or the execution result of the first interface operation instruction and the corresponding execution suggestion to the first prompt word to update the first prompt word.

[0138] In an optional embodiment, the execution result of the first interface operation instruction and the corresponding execution suggestion include: the first interface operation instruction is executed incorrectly, and it is recommended to return to the page corresponding to the first page screenshot and re-execute the first interface operation instruction; or the first interface operation instruction is executed correctly, and it is recommended to execute other interface operation instructions in the at least one interface operation instruction; or the first interface operation instruction has no execution result, and it is recommended to re-execute the first interface operation instruction.

[0139] In an alternative embodiment, the processing module 12 is further configured to: update the dynamic model evaluation metrics corresponding to the target test model according to the execution result of the first interface operation instruction, where the dynamic model evaluation metrics are used to calculate the target model score, and the target test model is any one of the first test model, the second test model, and the natural language summary model; and update the target model score corresponding to the target test model in response to the update of the dynamic model evaluation metrics.

[0140] In an alternative embodiment, the processing module 12 is further configured to determine the target model scores corresponding to the multiple test models in the following manner: determine the initial model scores corresponding to the multiple test models according to the static model evaluation metrics and the first weights respectively pre-configured for the multiple test models; and determine the target model scores corresponding to the multiple test models according to the initial model scores, the dynamic model evaluation metrics, and the second weights respectively corresponding to the multiple test models; where the dynamic model evaluation metrics vary dynamically within a preset metric value range according to the initial metric values and the execution results of the at least one interface operation instruction.

[0141] In an alternative embodiment, the multiple test models are respectively used to process different types of data. When the obtaining module 11 determines the first test model for executing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple test models pre-configured, it is specifically configured to: screen out at least one test model that matches the target data type from the multiple test models according to the target data type required for executing the interface test task; and determine the first test model for executing the interface test task from the at least one test model according to the target model score corresponding to the at least one test model, where the target model score of the first test model is greater than the target model scores of other test models in the at least one test model.

[0142] Figure 4 The described device can execute the steps introduced in the foregoing embodiments. For the detailed execution process and technical effects, refer to the descriptions in the foregoing embodiments and will not be elaborated here.

[0143] In a possible design, the above Figure 4 The structure of the described interface test device can be implemented as an electronic device, as Figure 5 shown. The electronic device may include: a memory 21, a processor 22, and a communication interface 23. Among them, a computer program is stored on the memory 21. When the computer program is executed by the processor 22, the processor 22 can at least implement the interface test method provided in the foregoing embodiments.

[0144] The above-mentioned memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0145] Correspondingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to be able to implement the steps in the above method embodiments. Among them, the computer-readable storage medium includes volatile, non-volatile, or a combination thereof, and can be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium

[0146] Correspondingly, an embodiment of the present application further provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the processor can implement the steps in the above method embodiments. It should be understood that each process or the combination of multiple processes in the above method flow can be implemented by the computer program or instructions. In addition, these computer programs or instructions can be applied to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable interface test devices, so that the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable interface test devices can be used as devices to implement the corresponding functions in the above method embodiments.

[0147] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0148] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a computer product. The present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0149] Finally, it should be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitations, the element defined by the statement "including one..." does not exclude the existence of other identical elements in the process, method, commodity or device including the element.

[0150] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An interface testing method, characterized in that, Including: Obtain at least one interface operation instruction described in natural language corresponding to the interface test task; Determine a first test model for executing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models; Generate a first prompt word according to the at least one interface operation instruction; Obtain a first page screenshot corresponding to the current page of the application under test, where the first page screenshot contains an image corresponding to the control to be detected; Input the first prompt word and the first page screenshot into the first test model, so as to use the first test model to determine a first interface operation instruction to be currently executed from the at least one interface operation instruction according to the first page screenshot, and generate a second interface operation instruction described in code language corresponding to the first interface operation instruction; Send the second interface operation instruction to the application under test to test the control to be detected, and the second interface operation instruction is used to determine the interface interaction operation for the control to be detected.

2. The method according to claim 1, characterized in that, The method further includes: Determine a natural language summary model and / or a second test model for executing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple pre-configured test models; After sending the second interface operation instruction to the application under test, update the first prompt word by using the natural language summary model and / or the second test model; Input the updated first prompt word and the re-obtained first page screenshot into the first test model, so that the first test model outputs an interface operation instruction described in code language corresponding to the unexecuted interface operation instruction; In response to the first test model outputting a prompt message that all of the at least one interface operation instruction has been executed, determine that the interface test task is completed.

3. The method according to claim 2, characterized in that The updating the first prompt word by using the natural language summary model and / or the second test model includes: Through the natural language summary model, generate an instruction summary corresponding to the historical interface operation instruction according to the historical interface operation instruction that has been executed in the at least one interface operation instruction, and the historical interface operation instruction includes the first interface operation instruction; and / or, Obtain a second page screenshot corresponding to the application under test after executing the second interface operation instruction; Generate a second prompt word according to the at least one interface operation instruction; Input the second prompt word, the first page screenshot, and the second page screenshot into the second test model, so as to use the second test model to determine the execution result and corresponding execution suggestion of the first interface operation instruction according to the first page screenshot and the second page screenshot; Add the instruction summary and / or the execution result and corresponding execution suggestion of the first interface operation instruction to the first prompt word to update the first prompt word.

4. The method according to claim 3, characterized in that, The execution results and corresponding execution suggestions of the first interface operation instruction include: the first interface operation instruction is executed incorrectly, and it is recommended to return to the page corresponding to the first page screenshot and re-execute the first interface operation instruction; or, the first interface operation instruction is executed correctly, and it is recommended to execute other interface operation instructions in the at least one interface operation instruction; or, there is no result for the execution of the first interface operation instruction, and it is recommended to re-execute the first interface operation instruction.

5. The method according to claim 3, characterized in that, The method further includes: Updating the dynamic model evaluation index corresponding to the target test model according to the execution result of the first interface operation instruction, where the dynamic model evaluation index is used to calculate the target model score, and the target test model is any one of the first test model, the second test model, and the natural language summary model; In response to the update of the dynamic model evaluation index, updating the target model score corresponding to the target test model.

6. The method according to any one of claims 1 to 5, characterized in that The target model scores corresponding to the multiple test models are determined in the following manner: Determining the initial model scores corresponding to the multiple test models according to the static model evaluation indexes and the first weights respectively configured for the multiple test models; Determining the target model scores corresponding to the multiple test models according to the initial model scores, the dynamic model evaluation indexes, and the second weights respectively corresponding to the multiple test models; wherein, the dynamic model evaluation index changes dynamically within a preset index value range according to the initial index value and the execution results of the at least one interface operation instruction.

7. The method according to any one of claims 1 to 5, characterized in that, The multiple test models are respectively used to process different types of data. Determining the first test model for executing the interface test task from the multiple test models according to the target model scores respectively corresponding to the multiple test models includes: Filtering out at least one test model that matches the target data type from the multiple test models according to the target data type to be processed for executing the interface test task; Determining the first test model for executing the interface test task from the at least one test model according to the target model score corresponding to the at least one test model, where the target model score of the first test model is greater than the target model scores of other test models in the at least one test model.

8. An electronic device, characterized in that, Including: A memory, a processor, and a communication interface; wherein, a computer program is stored on the memory, and when the computer program is executed by the processor, the processor executes the interface test method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the processor executes the interface test method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Including: A computer program or instruction, and when the computer program or instruction is executed by a processor of an electronic device, the processor executes the interface test method according to any one of claims 1 to 7.

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