Application interface determination method, apparatus, medium, and device

Abstract

The present disclosure relates to an application interface determination method, device, medium and equipment, the method comprising: receiving application description information of a target application to be developed; determining, according to the application description information, a matching degree of each candidate interface in a preset interface library and the target application; determining a quality parameter corresponding to each candidate interface; and determining, according to the matching degree and the quality parameter corresponding to each candidate interface, an application interface for developing the target application from the candidate interfaces. Thus, when determining the application interface for developing the target application, not only the matching accuracy between the function provided by the candidate interface and the function required by the target application is considered, but also the quality of the candidate interface itself is detected to ensure the availability and stability of the determined application interface, thereby improving the availability and running stability of the target application to some extent and improving the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of application development, and more specifically, to a method, apparatus, medium, and device for determining application interfaces. Background Technology

[0002] Web API services are a service-oriented architecture technology often used to automate interactions or link business processes between distributed and heterogeneous systems. Single-function web services struggle to meet complex and ever-changing needs. Therefore, a new enterprise application development technology called Mashup has emerged. Mashup technology can integrate single-function services. A wide variety of Web APIs are available within a Mashup service platform, allowing users to selectively call these APIs to create Mashup applications that meet their specific requirements.

[0003] However, with the increasing number of Web API services published online, and the unstructured description documents of Web APIs, related technologies typically involve constructing a call relationship matrix based on the call relationship between Mashup and Web API, and then using collaborative filtering to determine the interface APIs required by the Mashup application. Alternatively, collaborative filtering methods based on Mashup and collaborative filtering methods based on Web API can be used to determine the interface APIs required by the Mashup application.

[0004] However, when the above collaborative filtering-based method determines the interfaces required by a Mashup application, the determined application interfaces are often difficult to meet the functional requirements of the Mashup application when the call relationship matrix between the Mashup application and the Web API is sparse. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, apparatus, medium, and device for determining application interfaces, so as to improve the availability and stability of the determined application interfaces, thereby improving the availability and operational stability of the target application to a certain extent.

[0006] To achieve the above objectives, firstly, this disclosure provides an application interface determination method, the method comprising:

[0007] Receive application description information for the target application to be developed;

[0008] Based on the application description information, determine the matching degree between each candidate interface in the preset interface library and the target application;

[0009] Determine the quality parameter corresponding to each candidate interface, the quality parameter being used to represent the quality of the service provided by the candidate interface;

[0010] Based on the matching degree and quality parameters corresponding to each candidate interface, the application interface for developing the target application is determined from the candidate interfaces.

[0011] Optionally, determining the quality parameters corresponding to each candidate interface includes:

[0012] For each candidate interface, determine the target parameters corresponding to the candidate interface;

[0013] For each candidate interface, the quality parameters corresponding to the candidate interface are determined based on the target parameters corresponding to the candidate interface.

[0014] The target parameters include at least one of the following: sample coverage parameter, interface update parameter, and anomaly parameter; wherein the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interface, the interface update parameter is determined based on the number of updates corresponding to the candidate interface, and the anomaly parameter is determined based on the number of interface anomaly feedbacks corresponding to the candidate interface.

[0015] Optionally, the target parameter includes the sample coverage parameter; the method for obtaining the sample coverage parameter includes:

[0016] For each candidate interface, obtain the interface example corresponding to the candidate interface based on the identifier of the candidate interface;

[0017] For each candidate interface, the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interface and the total number of interface samples corresponding to all candidate interfaces.

[0018] Optionally, before the step of determining the sample coverage parameter based on the number of interface samples corresponding to the candidate interfaces and the total number of interface samples corresponding to all candidate interfaces, the method of obtaining the sample coverage parameter further includes:

[0019] Based on the interface examples corresponding to the candidate interfaces, determine the parameter examples in the interface examples that correspond to each interface parameter in the candidate interfaces;

[0020] The ratio of the number of interface parameters with parameter samples in the candidate interface to the total number of interface parameters in the candidate interface is determined as the first sample coverage parameter.

[0021] The sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interfaces and the total number of interface samples corresponding to all candidate interfaces, including:

[0022] The ratio of the number of interface samples corresponding to the candidate interface to the total number of interface samples corresponding to all candidate interfaces is used as the second sample coverage parameter, and the sum of the first sample coverage parameter and the second sample coverage parameter is used as the sample coverage parameter.

[0023] Optionally, the target parameter includes the interface update parameter; the method for obtaining the interface update parameter includes:

[0024] For each candidate interface, determine the update information for each version corresponding to the candidate interface;

[0025] Based on the update information for each version, determine the number of updates corresponding to the candidate interface;

[0026] The result obtained by subtracting the update count ratio from 1 is determined as the interface update parameter, where the update count ratio is the ratio of the update count of the candidate interface to the total update count of all candidate interfaces.

[0027] Optionally, the target parameter includes the abnormal parameter; the method for obtaining the abnormal parameter includes:

[0028] Obtain the interface feedback information corresponding to the interface library;

[0029] Based on the feedback information from the interface, determine the abnormal information corresponding to each candidate interface;

[0030] The result obtained by subtracting the interface anomaly feedback ratio is determined as the anomaly parameter. The interface anomaly feedback ratio is the ratio of the number of anomaly information corresponding to the candidate interface to the total number of anomaly information corresponding to all candidate interfaces.

[0031] Optionally, determining the matching degree between each candidate interface in the preset interface library and the target application based on the application description information includes:

[0032] Input the application description information into the application vector model to obtain the application vector representation corresponding to the target application;

[0033] For each candidate interface, the matching degree corresponding to the candidate interface is determined based on the interface vector representation and the application vector representation.

[0034] Secondly, this disclosure provides an application interface determination device, the device comprising:

[0035] The receiving module is used to receive application description information of the target application to be developed.

[0036] The first determining module is used to determine the matching degree between each candidate interface in the preset interface library and the target application based on the application description information.

[0037] The second determining module is used to determine the quality parameter corresponding to each candidate interface, wherein the quality parameter is used to represent the quality of the service provided by the candidate interface.

[0038] The third determining module is used to determine the application interface for developing the target application from the candidate interfaces based on the matching degree corresponding to each candidate interface and the quality parameter.

[0039] Thirdly, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods described in the first aspect.

[0040] Fourthly, this disclosure provides an electronic device, comprising:

[0041] A memory on which computer programs are stored;

[0042] A processor for executing the computer program in the memory to implement the steps of any of the methods described in the first aspect.

[0043] In the above technical solution, candidate interfaces in a pre-built interface library can be analyzed for the target application to be developed. A comprehensive analysis is conducted, considering the matching degree between the candidate interfaces and the target application, as well as the quality parameters of the candidate interfaces themselves, to determine the application interfaces used for developing the target application. Therefore, this technical solution, on the one hand, considers not only the matching accuracy between the functions provided by the candidate interfaces and the functions required by the target application when determining the application interfaces for development, but also ensures the matching degree between the determined application interfaces and the target application, thereby improving the functional effectiveness of the target application developed based on the application interfaces and meeting user needs. Furthermore, this disclosed solution does not directly rely on existing application development processes for collaborative filtering development, effectively avoiding deviations in application interface determination caused by the sparsity of existing application and interface call relationship data, further improving the accuracy of the determined application interfaces. On the other hand, the quality of services provided by the candidate interfaces themselves can be tested to ensure the availability and stability of the determined application interfaces, thereby improving the availability and operational stability of the target application to a certain extent and enhancing the user experience.

[0044] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart of an application interface determination method provided according to one embodiment of the present disclosure;

[0047] Figure 2 This is a block diagram of an application interface determining device according to one embodiment of the present disclosure;

[0048] Figure 3 This is a block diagram illustrating an electronic device according to an exemplary embodiment;

[0049] Figure 4 This is a block diagram illustrating an electronic device according to another exemplary embodiment. Detailed Implementation

[0050] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0051] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0052] Figure 1 The diagram shown is a flowchart of an application interface determination method according to an embodiment of this disclosure. Figure 1 As shown, the method may include:

[0053] In step 11, the application description information of the target application to be developed is received.

[0054] The target application can be a Mashup application. Mashup technology combines data and application functions from multiple sources through various channels to create entirely new service applications, i.e., Mash applications. Users can add relevant information about the target application they want to develop, such as its name and functional description, to the Mashup service platform as the application description information for that target application.

[0055] In step 12, based on the application description information, the matching degree between each candidate interface in the preset interface library and the target application is determined.

[0056] The pre-built interface library can contain multiple candidate interfaces, such as WebAPI. This library can be obtained from the WebAPI service provider's website, thus providing the interface description information for each candidate interface. This description information may include the functionality provided by the interface. Correspondingly, the application description information may include the functional information of the target application to be developed. Each candidate interface can then be compared with the target application to determine the matching degree, thereby confirming whether the functionality provided by the candidate interface matches the functionality required by the target application.

[0057] In step 13, the quality parameters corresponding to each candidate interface are determined. The quality parameters corresponding to the candidate interface are used to represent the quality of the service provided by the candidate interface, such as whether the candidate interface is easy to operate and learn, whether the candidate interface can run stably, and whether the candidate interface has any abnormalities, so as to ensure the operational stability and security of the determined target application based on the quality parameters.

[0058] In step 14, the application interface for developing the target application is determined from the candidate interfaces based on the matching degree and quality parameters corresponding to each candidate interface.

[0059] In the above technical solution, candidate interfaces in a pre-built interface library can be analyzed for the target application to be developed. A comprehensive analysis is conducted, considering the matching degree between the candidate interfaces and the target application, as well as the quality parameters of the candidate interfaces themselves, to determine the application interfaces used for developing the target application. Therefore, this technical solution, on the one hand, considers not only the matching accuracy between the functions provided by the candidate interfaces and the functions required by the target application when determining the application interfaces for development, but also ensures the matching degree between the determined application interfaces and the target application, thereby improving the functional effectiveness of the target application developed based on the application interfaces and meeting user needs. Furthermore, this disclosed solution does not directly rely on existing application development processes for collaborative filtering development, effectively avoiding deviations in application interface determination caused by the sparsity of existing application and interface call relationship data, further improving the accuracy of the determined application interfaces. On the other hand, the quality of services provided by the candidate interfaces themselves can be tested to ensure the availability and stability of the determined application interfaces, thereby improving the availability and operational stability of the target application to a certain extent and enhancing the user experience.

[0060] In one possible embodiment, an exemplary method for determining the matching degree between each candidate interface in the preset interface library and the target application based on the application description information in step 12 may include:

[0061] The matching degree between candidate interfaces and target applications is determined by matching the functional categories of the candidate interfaces with the functional categories of the target applications. For example, each candidate interface can be labeled with the functional categories of the services it provides. These functional categories can have multiple levels, allowing the matching degree to be determined based on the categories.

[0062] As another example, an embodiment of step 12 may include:

[0063] Input the application description information into the application vector model to obtain the application vector representation corresponding to the target application;

[0064] For each candidate interface, the matching degree corresponding to the candidate interface is determined based on the interface vector representation and the application vector representation.

[0065] The interface vector corresponding to the candidate interface is obtained by inputting the interface description information corresponding to the candidate interface into the interface vector model. The interface vector model and the application vector model are jointly trained based on the relationship graph between the developed application and the interface called by the developed application, using graph embedding learning.

[0066] For example, a large number of developed applications and the call relationships between the interfaces called by these applications can be obtained to obtain training samples. This can be achieved by obtaining application description information of developed applications and interface description information of the interfaces called by these applications as training samples. The developed applications are treated as application nodes, and the interfaces they call are treated as interface nodes. For each interface called by a developed application, a connection is established between the application node of the developed application and the interface node of the called interface, thereby determining the application-interface relationship graph. Further, the application vector model and the interface vector model can be jointly trained based on graph embedding learning. Specifically, the graph embedding learning method commonly used in this field can be based on a deep learning network (GNN) to obtain the application vector model and the interface vector model. The application vector model is used to convert the input application description information into its corresponding application vector representation, and the interface vector model is used to convert the input interface description information into its corresponding interface vector representation.

[0067] Taking interface description information as an example, the interface description information of candidate interfaces can be obtained through a pre-built interface library. This interface description information may include information such as interface name, function description, and call relationship. The corresponding text can then be concatenated to obtain the description text. Furthermore, keywords corresponding to the function can be extracted from this description text, using commonly used keyword extraction methods in this field, which will not be elaborated upon here. Finally, based on the keywords extracted from the description text and the interface vector model, corresponding word vector embedding vector representations can be generated.

[0068] In related technologies, a call relationship graph is typically determined based on the identifiers of the application and the interfaces called by that application. Then, based on the constructed call relationship graph, graph embedding vector generation techniques are used to generate embedding vector representations corresponding to the applications and interfaces in the graph. However, this process only relies on the identifier ID embedding vectors generated from the call relationships to generate the embedding vectors corresponding to the applications and interfaces, making it difficult to accurately represent the applications and interfaces.

[0069] In the above embodiments, the matching degree is calculated by combining the application description information and the interface description information. This ensures that the textual description information of the application and interface are fully considered during the calculation process, thereby improving the accuracy of the determined application vector representation and interface vector representation, and providing reliable data support for calculating the matching degree. Furthermore, based on the existing association between developed applications and the interfaces called by those applications, application vector models and interface vector models can be jointly trained. This facilitates the vector transformation representation of the target application and various candidate interfaces, and also ensures the consistency between the transformed application vector representation and interface vector representation, further guaranteeing the accuracy of subsequent calculations of the matching degree between the application and the interface, and providing reliable data support for the subsequent determination of the application interface.

[0070] In one possible embodiment, an exemplary implementation of determining the quality parameter corresponding to each candidate interface in step 13 may include:

[0071] For each candidate interface, a target parameter corresponding to the candidate interface is determined, wherein the target parameter includes at least one of the following: sample coverage parameter, interface update parameter, and exception parameter.

[0072] In the first case, the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interface.

[0073] The interface examples serve as demonstrations of how to use candidate interfaces. A pre-built interface library can contain examples for one or more interfaces, allowing users to understand the usage and implementation logic of the candidate interface by reading these examples. In practical applications, the interface examples corresponding to a candidate interface are associated with that candidate interface. The more interface examples a candidate interface has, the greater its example coverage parameter, meaning users can learn more about the candidate interface based on more examples. This indicates higher usability, ease of use, and learnability of the candidate interface.

[0074] The second method involves determining the interface update parameters based on the number of updates corresponding to the candidate interface.

[0075] Candidate interfaces can be updated by releasing multiple versions. The fewer times a candidate interface is updated, the higher its stability. In other words, the operational stability of a candidate interface can be represented by its update parameters, and the quality of the interface can be evaluated based on these update parameters.

[0076] The third method involves determining the exception parameter based on the number of interface exception feedbacks corresponding to the candidate interface.

[0077] During the use of the interface, when an interface malfunctions, users can discuss and answer questions about the interface malfunction through the exception feedback service. The exception feedback service can be a service interface or a service exception Q&A website. By obtaining the feedback information of the interface malfunction, the degree of malfunction of the candidate interface can be determined, i.e., the exception parameter. The operation reliability of the candidate interface can be represented by the exception parameter, and the quality of the interface can be evaluated based on the exception parameter.

[0078] For each candidate interface, the quality parameters corresponding to the candidate interface are determined based on the target parameters corresponding to the candidate interface.

[0079] As an example, if there is only one target parameter, it can be directly used as the quality parameter. If there are more than one target parameter, the weighted sum of the target parameters can be used to determine the quality parameter corresponding to the candidate interface, so as to characterize the quality of the candidate interface from the perspectives of multiple target parameters. The weights of each target parameter can be set based on the actual application scenario, and the sum of the weights of each target parameter is 1. If the target parameters include the above three parameters, the weights of the sample coverage parameter, interface update parameter, and anomaly parameter can all be 1 / 3, or the weight of the sample coverage parameter can be 0.5, the weight of the interface update parameter can be 0.25, and the weight of the anomaly parameter can be 0.25. This disclosure does not limit this.

[0080] Therefore, by using the above technical solution, the quality parameters corresponding to each candidate interface can be determined to evaluate the quality of the candidate interface itself, thereby improving the effectiveness and usability of the determined application interfaces used for target application development to a certain extent, and thus ensuring the usability of the target application.

[0081] In one possible embodiment, the target parameter includes the sample coverage parameter; the method of obtaining the sample coverage parameter may include:

[0082] For each candidate interface, obtain the interface example corresponding to the candidate interface based on the identifier of the candidate interface.

[0083] Specifically, interface examples for each candidate interface can be determined from a pre-built interface library. This library can be an existing Web API service provider website. Interface examples for each Web API can be uploaded and stored, allowing for querying of candidate interfaces on the service provider website. For example, when uploading an interface example, its corresponding interface identifier can be associated simultaneously. Therefore, for a candidate interface, a query can be performed based on the candidate interface identifier, and the retrieved interface examples will be identified as the interface examples corresponding to that candidate interface.

[0084] For each candidate interface, the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interface and the total number of interface samples corresponding to all candidate interfaces.

[0085] As an example, the ratio of the number of interface samples corresponding to the candidate interface to the total number of interface samples corresponding to all candidate interfaces can be directly determined as the sample coverage parameter.

[0086] As another example, before the step of determining the sample coverage parameter based on the number of interface samples corresponding to the candidate interfaces and the total number of interface samples corresponding to all candidate interfaces, the method of obtaining the sample coverage parameter may further include:

[0087] Based on the interface sample corresponding to the candidate interface, determine the parameter sample in the interface sample that corresponds to each interface parameter in the candidate interface.

[0088] In generating interface samples, to further improve the granularity and readability of the samples, for the same interface, the interface sample can contain samples written for different parameters under that interface. The sample can also be associated with the interface parameters of its corresponding example. Based on this, for an interface sample corresponding to a candidate interface, the sample corresponding to the same interface parameter can be determined as the parameter sample based on the interface parameters associated with that interface sample.

[0089] The ratio of the number of interface parameters with parameter samples in the candidate interface to the total number of interface parameters in the candidate interface is determined as the first sample coverage parameter.

[0090] Accordingly, the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interfaces and the total number of interface samples corresponding to all candidate interfaces, which may include:

[0091] The ratio of the number of interface samples corresponding to the candidate interface to the total number of interface samples corresponding to all candidate interfaces is used as the second sample coverage parameter, and the sum of the first sample coverage parameter and the second sample coverage parameter is used as the sample coverage parameter.

[0092] For example, the sample coverage parameter can be determined using the following formula:

[0093]

[0094] Where SampleCoverageOfAPIs(a) is used to represent the sample coverage parameter corresponding to candidate interface a;

[0095] NumofSample(a) is used to represent the number of interface samples corresponding to candidate interface a;

[0096] ALLNumofSample is used to represent the total number of interface samples corresponding to all candidate interfaces, that is, the sum of the number of interface samples corresponding to each candidate interface;

[0097] NumOfParmWithSample is used to represent the number of interface parameters with parameter samples in candidate interface a;

[0098] NumOfParms(a) is used to represent the total number of interface parameters for candidate interface a.

[0099] Therefore, through the above technical solution, the sample coverage parameter corresponding to the candidate interface can be calculated by using the samples provided by the service providers corresponding to each interface. Based on the sample coverage parameter, the usability and learnability of the candidate interface can be characterized, thereby evaluating the quality of the interface from the perspective of sample coverage. This enables the evaluation of the interface quality in the interface library and provides data support for the subsequent determination of application interfaces.

[0100] In one possible embodiment, the target parameter includes the interface update parameter; the method of obtaining the interface update parameter may include:

[0101] For each candidate interface, determine the update information for each version corresponding to the candidate interface.

[0102] Based on the update information for each version, determine the number of updates corresponding to the candidate interface.

[0103] When an interface is updated, it typically records the update information for that update. Therefore, update information for each version of the interface can be retrieved for subsequent judgment. For example, update fields can be pre-defined, such as the interface name, the number of interface parameters, and the type of interface parameters. Each change to an update field increments the update count. This allows the update count for the candidate interface to be determined based on the update information for each version.

[0104] For example, candidate interface 'a' has three versions: 1.0, 1.1, and 2.0. Version 1.1's update information records a button color update; this update is not for the updated field and can be ignored. Version 2.0's update information records that the interface parameter type changed from 'type1' to 'type2', and a new interface parameter 'x' was added. This update is for the updated field, so based on this update information, the update count can be increased by 2. Therefore, the update count can be determined based on the update information.

[0105] The result obtained by subtracting the update count ratio from 1 is determined as the interface update parameter, where the update count ratio is the ratio of the update count of the candidate interface to the total update count of all candidate interfaces.

[0106] For example, the update parameters for this interface can be determined using the following formula:

[0107]

[0108] Where StabilityOfAPIs(a) is used to represent the interface update parameters corresponding to candidate interface a;

[0109] NumOfChanged(a) is used to represent the number of updates corresponding to candidate interface a;

[0110] AllNumOfAPIsChanged is used to represent the total number of updates for all candidate APIs, that is, the sum of the number of updates for each candidate API.

[0111] Therefore, through the above technical solution, the interface update parameters corresponding to the candidate interfaces can be calculated by using the update information of each version of each interface. Generally, the fewer the number of times an interface is updated, the more stable the interface is. The stability of the candidate interface can be characterized based on the interface update parameters, thereby evaluating the quality of the interface from the perspective of the interface update parameters. This enables the evaluation of the interface quality in the interface library and provides data support for the subsequent determination of application interfaces.

[0112] In one possible embodiment, the target parameter includes the abnormal parameter; the method of obtaining the abnormal parameter may include:

[0113] Obtain the interface feedback information corresponding to the interface library;

[0114] Based on the feedback information from the interface, determine the exception information corresponding to each candidate interface.

[0115] As mentioned above, during the use of the interface, when an interface malfunctions, users can discuss and answer questions about the interface malfunction through the exception feedback service. The exception feedback service can be used to obtain interface feedback information and determine the interface corresponding to each interface feedback information based on the interface identifier associated with the interface feedback information, thereby identifying the exception information corresponding to each candidate interface.

[0116] The result obtained by subtracting the interface anomaly feedback ratio is determined as the anomaly parameter. The interface anomaly feedback ratio is the ratio of the number of anomaly information corresponding to the candidate interface to the total number of anomaly information corresponding to all candidate interfaces.

[0117] For example, the exception parameters corresponding to the candidate interface can be determined by the following formula;

[0118]

[0119] ProblemOfAPIs(a) is used to represent the exception parameters corresponding to candidate interface a;

[0120] NumOfProblemsOfAPIs(a) is used to represent the number of exception messages corresponding to candidate interface a;

[0121] NumOfProblems is used to represent the total number of exception messages corresponding to all candidate interfaces, that is, the sum of the number of exception messages corresponding to each candidate interface.

[0122] Therefore, through the above technical solution, the abnormal parameters corresponding to the candidate interfaces can be calculated by using the abnormal feedback data corresponding to each interface. Generally, the fewer the abnormal feedbacks of an interface, the higher the credibility of the interface's running results. The credibility of the candidate interface's running can then be characterized based on the abnormal parameters, thereby evaluating the quality of the interface from the perspective of abnormal parameters. This enables the evaluation of the interface quality in the interface library and provides data support for the subsequent determination of application interfaces.

[0123] In one possible embodiment, step 14, determining the application interface for developing the target application from the candidate interfaces based on the matching degree and quality parameters corresponding to each candidate interface, may include:

[0124] For each candidate interface, the matching degree and the quality parameter corresponding to the candidate interface are weighted and summed to obtain the fusion parameter corresponding to the candidate interface.

[0125] For example, the fusion parameters can be represented as follows:

[0126] f(m,a)=λ1·f1(m,a)+λ2·f2(a)

[0127] Where f(m,a) represents the fusion parameters corresponding to the target application m and the candidate interface a;

[0128] λ1 is used to represent the weight corresponding to the matching degree;

[0129] f1(m,a) is used to represent the matching degree between the target application m and the candidate interface a;

[0130] λ2 is used to represent the weight corresponding to the quality parameter;

[0131] f2(a) is used to represent the quality parameter corresponding to candidate interface a.

[0132] The weights λ1 corresponding to the matching degree and λ2 corresponding to the quality parameter can be set according to the actual application scenario, and this disclosure does not impose any restrictions on them. The sum of λ1 and λ2 is 1.

[0133] The top N candidate interfaces are selected in descending order of the fusion parameters and determined as the application interfaces, where N is a positive integer.

[0134] Wherein, N can be preset according to the actual application scenario, and this disclosure does not limit it. Therefore, for each candidate interface, the suitability of the candidate interface for the development of the target application can be comprehensively judged from the perspectives of the matching accuracy between the candidate interface and the target application and the service quality of the candidate interface itself. This ensures the accuracy of the determined application interfaces and their availability, thereby improving the effectiveness and stability of the developed target application running on the service terminal, simplifying the development process of the target application, and enhancing the user experience.

[0135] This disclosure also provides an application interface determination device, such as... Figure 2 As shown, the device 10 includes:

[0136] The receiving module 100 is used to receive application description information of the target application to be developed.

[0137] The first determining module 200 is used to determine the matching degree between each candidate interface in the preset interface library and the target application based on the application description information.

[0138] The second determining module 300 is used to determine the quality parameter corresponding to each candidate interface, wherein the quality parameter is used to represent the quality of the service provided by the candidate interface.

[0139] The third determining module 400 is used to determine the application interface for developing the target application from the candidate interfaces based on the matching degree corresponding to each candidate interface and the quality parameter.

[0140] Optionally, the second determining module includes:

[0141] The first determining submodule is used to determine the target parameters corresponding to each candidate interface.

[0142] The second determining submodule is used to determine, for each candidate interface, a quality parameter corresponding to the candidate interface based on the target parameter corresponding to the candidate interface, wherein the target parameter includes at least one of the following: sample coverage parameter, interface update parameter, and anomaly parameter; wherein the sample coverage parameter is determined based on the number of interface samples corresponding to the candidate interface, the interface update parameter is determined based on the number of updates corresponding to the candidate interface, and the anomaly parameter is determined based on the number of interface anomaly feedbacks corresponding to the candidate interface.

[0143] Optionally, the target parameter includes the sample coverage parameter; the sample coverage parameter is obtained through a third determining submodule, the third determining submodule including:

[0144] The fourth determining submodule is used to obtain the interface sample corresponding to each candidate interface based on the identifier of the candidate interface.

[0145] The fifth determination submodule is used to determine the sample coverage parameter for each candidate interface based on the number of interface samples corresponding to the candidate interface and the total number of interface samples corresponding to all candidate interfaces.

[0146] Optionally, the third determining submodule further includes:

[0147] The sixth determining submodule is used to determine, before the fifth determining submodule determines the sample coverage parameter based on the interface samples corresponding to the candidate interface, the parameter sample in the interface sample that corresponds to each interface parameter in the candidate interface;

[0148] The seventh determination submodule is used to determine the ratio of the number of interface parameters with parameter samples in the candidate interface to the total number of interface parameters in the candidate interface as the first sample coverage parameter;

[0149] The fifth determining submodule is further used for:

[0150] The ratio of the number of interface samples corresponding to the candidate interface to the total number of interface samples corresponding to all candidate interfaces is used as the second sample coverage parameter, and the sum of the first sample coverage parameter and the second sample coverage parameter is used as the sample coverage parameter.

[0151] Optionally, the target parameter includes the interface update parameter; the interface update parameter is obtained through an eighth determining submodule, which includes:

[0152] The ninth determining submodule is used to determine the update information for each version of the candidate interface for each candidate interface.

[0153] The tenth determining submodule is used to determine the number of updates corresponding to the candidate interface based on the update information under each version.

[0154] The eleventh determination submodule is used to determine the result obtained by subtracting the update count ratio as the interface update parameter, wherein the update count ratio is the ratio of the update count of the candidate interface to the total update count of all candidate interfaces.

[0155] Optionally, the target parameter includes the abnormal parameter; the abnormal parameter is obtained through the twelfth acquisition submodule, and the twelfth determination submodule includes:

[0156] The acquisition submodule is used to acquire the interface feedback information corresponding to the interface library.

[0157] The thirteenth determination submodule is used to determine the abnormal information corresponding to each candidate interface based on the interface feedback information.

[0158] The fourteenth determination submodule is used to determine the result obtained by subtracting the interface anomaly feedback ratio as the anomaly parameter, wherein the interface anomaly feedback ratio is the ratio of the number of anomaly information corresponding to the candidate interface to the total number of anomaly information corresponding to all candidate interfaces.

[0159] Optionally, the first determining module includes:

[0160] The input submodule is used to input the application description information into the application vector model to obtain the application vector representation corresponding to the target application.

[0161] The fifteenth determining submodule is used to determine the matching degree corresponding to each candidate interface based on the interface vector representation and the application vector representation corresponding to the candidate interface.

[0162] Optionally, the third determining module includes:

[0163] The processing submodule is used to perform a weighted summation of the matching degree and the quality parameter corresponding to each candidate interface to obtain the fusion parameter corresponding to the candidate interface.

[0164] The selection submodule is used to select the top N candidate interfaces in descending order of the fusion parameters and determine them as the application interfaces, where N is a positive integer.

[0165] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0166] Figure 3 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. Figure 3 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0167] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the application interface determination method described above. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 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 storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0168] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the application interface determination method described above.

[0169] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the application interface determination method described above. For example, the computer-readable storage medium may be the memory 702 including program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the application interface determination method described above.

[0170] Figure 4 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 4 The electronic device 1900 includes a processor 1922, which may be one or more, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 1922 may be configured to execute the computer program to perform the application interface determination method described above.

[0171] Additionally, the electronic device 1900 may also include a power supply component 1926 and a communication component 1950. The power supply component 1926 can be configured to perform power management of the electronic device 1900, and the communication component 1950 can be configured to enable communication of the electronic device 1900, such as wired or wireless communication. Furthermore, the electronic device 1900 may also include an input / output (I / O) interface 1958. The electronic device 1900 can operate on an operating system stored in memory 1932.

[0172] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the application interface determination method described above. For example, the non-transitory computer-readable storage medium may be the memory 1932 including the program instructions, which may be executed by the processor 1922 of the electronic device 1900 to complete the application interface determination method described above.

[0173] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described application interface determination method when executed by the programmable device.

[0174] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0175] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0176] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. An application interface determination method, characterized by, The method comprises: receiving application description information of a target application to be developed; determining, according to the application description information, a matching degree of each candidate interface in a preset interface library corresponding to the target application; determining a quality parameter corresponding to each candidate interface, the quality parameter being used to represent a quality of a service provided by the candidate interface; determining, according to the matching degree and the quality parameter corresponding to each candidate interface, an application interface used to develop the target application from the candidate interfaces; wherein the determining of the quality parameter corresponding to each candidate interface comprises: determining, for each candidate interface, a target parameter corresponding to the candidate interface; determining, for each candidate interface, the quality parameter corresponding to the candidate interface according to the target parameter corresponding to the candidate interface; wherein the target parameter comprises at least one of a sample coverage parameter, an interface update parameter and an exception parameter; the sample coverage parameter is determined based on a number of interface samples corresponding to the candidate interface, the interface update parameter is determined based on a number of updates corresponding to the candidate interface, and the exception parameter is determined based on a number of interface exception feedbacks corresponding to the candidate interface.

2. The method of claim 1, wherein, The target parameter comprises the sample coverage parameter; and the sample coverage parameter is obtained in the following manner: for each candidate interface, obtaining interface samples corresponding to the candidate interface according to an identifier of the candidate interface; determining the sample coverage parameter according to a number of the interface samples corresponding to the candidate interface and a total number of interface samples corresponding to all candidate interfaces.

3. The method of claim 2, wherein, Before the step of determining the sample coverage parameter according to the number of the interface samples corresponding to the candidate interface and the total number of interface samples corresponding to all candidate interfaces, the sample coverage parameter is obtained in the following manner: determining, according to the interface samples corresponding to the candidate interface, a parameter sample corresponding to each interface parameter in the candidate interface; determining a ratio of a number of interface parameters in the candidate interface having the parameter sample to a total number of interface parameters in the candidate interface as a first sample coverage parameter; determining the sample coverage parameter according to the number of the interface samples corresponding to the candidate interface and the total number of interface samples corresponding to all candidate interfaces comprises: taking a ratio of the number of the interface samples corresponding to the candidate interface to the total number of interface samples corresponding to all candidate interfaces as a second sample coverage parameter, and taking a sum of the first sample coverage parameter and the second sample coverage parameter as the sample coverage parameter.

4. The method of claim 1, wherein, The target parameter comprises the interface update parameter; The interface update parameter is obtained in the following manner: determining, for each candidate interface, update information under each version corresponding to the candidate interface; determining the number of updates corresponding to the candidate interface according to the update information under each version; and determining, for each candidate interface, the quality parameter corresponding to the candidate interface according to the target parameter corresponding to the candidate interface. The interface updating parameter is determined as a result of subtracting an updating frequency ratio from 1, the updating frequency ratio being a ratio of an updating frequency of the candidate interface to a total updating frequency corresponding to all candidate interfaces.

5. The method of claim 1, wherein, The target parameter comprises the exception parameter; and the exception parameter is obtained in the following manner: Obtaining interface feedback information corresponding to the interface library; Determining exception information corresponding to each candidate interface according to the interface feedback information; The exception parameter is determined as a result of subtracting an interface exception feedback ratio from 1, the interface exception feedback ratio being a ratio of a number of exception information corresponding to the candidate interface to a total amount of exception information corresponding to all candidate interfaces.

6. The method of claim 1, wherein, The matching degree of each candidate interface in the preset interface library to the target application is determined according to the application description information, including: Inputting the application description information into an application vector model to obtain an application vector representation corresponding to the target application; For each candidate interface, the matching degree corresponding to the candidate interface is determined according to the interface vector representation corresponding to the candidate interface and the application vector representation.

7. An application interface determination apparatus characterized by comprising: an application interface determination unit operable to determine an application interface based on a type of an application to be executed. The device comprises: A receiving module configured to receive application description information of a target application to be developed; A first determining module configured to determine, according to the application description information, a matching degree of each candidate interface in a preset interface library to the target application; A second determining module configured to determine a quality parameter corresponding to each candidate interface, the quality parameter being used to represent a quality of a service provided by the candidate interface; A third determining module configured to determine, according to the matching degree corresponding to each candidate interface and the quality parameter, an application interface used to develop the target application from the candidate interfaces; The second determining module comprises: A first determining submodule configured to determine, for each candidate interface, a target parameter corresponding to the candidate interface; A second determining submodule configured to determine, for each candidate interface, a quality parameter corresponding to the candidate interface according to the target parameter corresponding to the candidate interface, wherein the target parameter comprises at least one of a sample coverage parameter, an interface updating parameter and an exception parameter; the sample coverage parameter is determined based on a number of interface samples corresponding to the candidate interface, the interface updating parameter is determined based on an updating frequency corresponding to the candidate interface, and the exception parameter is determined based on a number of times of interface exception feedback corresponding to the candidate interface.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1-6.

9. An electronic device, comprising: Comprise: A memory having a computer program stored thereon; A processor configured to execute the computer program in the memory to implement the steps of the method of any one of claims 1-6.

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