Interface joint debugging data generation method, device, equipment and computer-readable medium

Abstract

Embodiments of the present disclosure disclose an interface joint debugging data generation method, apparatus, device, and computer-readable medium. A specific implementation of the method includes: in response to receiving interface joint debugging test case query information, retrieving an interface test knowledge graph to obtain a test case query result; generating a test case editing page; in response to receiving test case editing information, determining an expected response result, and sending the expected response result to the user side for updating a test case filling form; in response to receiving use case confirmation information, obtaining a current timestamp, and performing splicing processing on a preset custom use case type identifier and the current timestamp to obtain a test case identifier; generating interface joint debugging data based on the test case identifier and the use case confirmation information. This implementation can ensure that users generate interface joint debugging data according to their own test requirements, timely discover problems existing in local code, and improve code maintenance efficiency.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method, apparatus, device, and computer-readable medium for generating interface joint debugging data. Background Art

[0002] Before a data demand user obtains demand data by connecting a local application to a data application interface, it is also necessary to perform joint debugging tests on the local application and the data application interface to ensure that the user side can normally obtain and successfully parse the interface response data. Currently, when performing interface joint debugging tests, the commonly used method is that the data application provider manually writes or automatically generates positive test case data and sends the positive test case data to the user side, so that the user side can perform joint debugging tests on the local application and the data application interface according to the positive test case data.

[0003] However, it is found in practice that when performing interface joint debugging tests in the above manner, the following technical problems often exist:

[0004] Since the positive test case data can only be used to test the expected normal behaviors or functions of the user-side code, it is difficult for the user side to test the reliability of the local code under wrong operations or abnormal conditions. Therefore, it is difficult for the user side to maintain the local code in a timely manner.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the concept of the present disclosure. Therefore, it may include information that does not form the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The content part of the present disclosure is used to briefly introduce concepts, which will be described in detail in the following detailed implementation part. The content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose a method, apparatus, device, and computer-readable medium for generating interface joint debugging data to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide an interface joint debugging data generation method, which includes: in response to receiving interface joint debugging test case query information corresponding to a target data application, retrieving a pre-generated interface test knowledge graph to obtain a test case query result, and sending the test case query result to the user terminal; in response to receiving an editing request for the test case query result, generating a test case editing page based on the editing request, and sending the test case editing page to the user terminal, where the test case editing page includes a test case filling form; in response to receiving test case editing information, determining an expected response result corresponding to the test case editing information, and sending the expected response result to the user terminal for updating the test case filling form; in response to receiving use case confirmation information for the updated test case filling form, obtaining a current timestamp, and performing a splicing process on a preset custom use case type identifier and the current timestamp to obtain a test case identifier; and generating interface joint debugging data based on the test case identifier and the use case confirmation information.

[0009] In a second aspect, some embodiments of the present disclosure provide an interface joint debugging data generation device, which includes: a retrieval and sending unit configured to, in response to receiving interface joint debugging test case query information corresponding to a target data application, retrieve a pre-generated interface test knowledge graph to obtain a test case query result, and send the test case query result to the user terminal; a first generation and sending unit configured to, in response to receiving an editing request for the test case query result, generate a test case editing page based on the editing request, and send the test case editing page to the user terminal, where the test case editing page includes a test case filling form; a determination and sending unit configured to, in response to receiving test case editing information, determine an expected response result corresponding to the test case editing information, and send the expected response result to the user terminal for updating the test case filling form; an obtaining and splicing processing unit configured to, in response to receiving use case confirmation information for the updated test case filling form, obtain a current timestamp, and perform a splicing process on a preset custom use case type identifier and the current timestamp to obtain a test case identifier; and a second generation unit configured to generate interface joint debugging data based on the test case identifier and the use case confirmation information.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, and when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method described in any implementation manner of the first aspect above.

[0011] Fourthly, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any implementation manner of the first aspect above.

[0012] The above embodiments of the present disclosure have the following beneficial effects: Through the interface joint debugging data generation method of some embodiments of the present disclosure, users can generate interface joint debugging data according to their own test requirements, which is convenient for timely discovering problems existing in the local code through joint debugging. Specifically, the reason why it is difficult for the user side to maintain the local code in a timely manner is that: since the positive test case data can only be used to test the expected normal behaviors or functions of the user-side code, it is difficult for the user side to test the reliability of the local code under wrong operations or abnormal conditions. Therefore, it is difficult for the user side to maintain the local code in a timely manner. Based on this, in the interface joint debugging data generation method of some embodiments of the present disclosure, first, in response to receiving the interface joint debugging test case query information corresponding to the target data application, retrieve the pre-generated interface test knowledge graph to obtain the test case query result, and send the above test case query result to the user side. Thus, the information of the test case that matches the user's joint debugging test requirements can be obtained. Then, in response to receiving the edit request for the above test case query result, based on the above edit request, generate a test case edit page, and send the above test case edit page to the user side. Among them, the above test case edit page includes a test case filling form. Thus, when the queried test case cannot meet the user's test requirements, a test case edit page can be provided for the user so that the user can modify the queried test case according to their own needs to create a test case that meets the user's requirements. After that, in response to receiving the test case edit information, determine the expected response result corresponding to the above test case edit information, and send the above expected response result to the above user side for updating the above test case filling form. Thus, the expected response data corresponding to the test case can be generated according to the information of the test case edited by the user. Next, in response to receiving the case confirmation information for the above updated test case filling form, obtain the current timestamp, and perform splicing processing on the preset custom case type identifier and the above current timestamp to obtain a test case identifier. Thus, a unique test case identifier can be assigned to the test case created by the user. Finally, based on the above test case identifier and the above case confirmation information, generate interface joint debugging data. Thus, the test data required for the user to perform joint debugging according to the self-created test case can be obtained. Therefore, in the interface joint debugging data generation method of some embodiments of the present disclosure, by providing a test case edit page for the user, when the user needs to test the reliability of the local code under wrong operations or abnormal conditions, the user can conveniently generate interface joint debugging data that meets their own joint debugging test requirements, thereby facilitating the user side to maintain the local code in a timely manner. Description of the Drawings

[0013] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flowchart of some embodiments of a method for generating interface debugging data according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of a device for generating interface debugging data according to the present disclosure;

[0016] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Specific Embodiments

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] In addition, it should be noted that for the sake of convenience of description, only the parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules, or units, and are not used to limit the order or interdependence relationship of the functions performed by these devices, modules, or units.

[0020] It should be noted that the modifications of "one" and "plural" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0023] Figure 1Flow 100 of some embodiments of the interface joint debugging data generation method according to the present disclosure is shown. The interface joint debugging data generation method includes the following steps:

[0024] Step 101, in response to receiving interface joint debugging test case query information corresponding to a target data application, retrieve a pre-generated interface test knowledge graph to obtain a test case query result, and send the test case query result to the client.

[0025] In some embodiments, the execution subject of the interface joint debugging data generation method (such as a computing device) can, in response to receiving interface joint debugging test case query information corresponding to a target data application, retrieve a pre-generated interface test knowledge graph in various ways to obtain a test case query result, and send the above test case query result to the client. Among them, the above target data application can be an application program that provides demand data (such as personal credit scores) to users. The above interface joint debugging test case query information can be information input by the user for querying test cases corresponding to the data application interface. The above interface test knowledge graph can be a knowledge graph with data applications, application interfaces, test cases, and application adaptation subject types as entities, and with the corresponding relationships between data applications and application adaptation subject types, the corresponding relationships between data applications and application interfaces, and the corresponding relationships between application interfaces and test cases as the relationships between entities. The above data application can be an application program that provides data resources or services to data requesters through application interfaces. The above test case query result can be information on the retrieved test case identifiers. The above test case identifier can be the unique identifier of the test case. The above application adaptation subject type can be an individual type or an organization type. The individual type can represent that the data application is suitable for providing personal credit data. The organization type can represent that the data application is suitable for providing organizational credit data. Each entity in the above interface test knowledge graph can have a corresponding entity identifier. The above entity identifier can be the unique identifier of the entity.

[0026] In some optional implementation manners of some embodiments, the above interface joint debugging test case query information can include a data application identifier and a query text. Among them, the above data application identifier can be the unique identifier of the data application. The above query text can be the text input by the user in the search box. The execution subject can retrieve a pre-generated interface test knowledge graph through the following steps to obtain a test case query result:

[0027] First step, perform keyword extraction processing on the query text included in the above interface joint debugging test case query information to obtain a keyword result set. Among them, the keyword extraction processing can be performed on the above word segmentation result set through a preset keyword extraction processing method to obtain a keyword result set. For example, the above keyword extraction processing method can be a part-of-speech-based extraction method.

[0028] Second step, for each keyword result in the above keyword result set, based on the above keyword result, retrieve the above interface test knowledge graph to obtain a first retrieval result. Among them, the above first retrieval result can be the result of test cases obtained by using the keyword result as the retrieval term. For each keyword result, the fuzzy matching method can be used to perform fuzzy retrieval on each entity information of the test case type in the above interface test knowledge graph to obtain a first retrieval result. Among them, the entity information can include but is not limited to entity identifiers.

[0029] Third step, based on the data application identifier included in the above interface joint debugging test case query information, retrieve the above interface test knowledge graph to obtain a second retrieval result set. Among them, the second retrieval result in the above second retrieval result set can be the result of test cases obtained by retrieving using the data application identifier included in the interface joint debugging test case query information as the retrieval term. The exact matching method can be used to accurately retrieve each entity of the data application type in the above interface test knowledge graph according to the data application identifier included in the interface joint debugging test case query information, and determine each test case associated with the retrieved data application entity as the second retrieval result set.

[0030] Fourth step, generate a test case query result based on the obtained first retrieval results and the above second retrieval result set. Among them, the intersection between each first retrieval result and the above second retrieval result set can be determined as the test case query result.

[0031] Optionally, the above interface test knowledge graph can be pre-generated through the following steps:

[0032] First step, obtain the application interface relationship information set, the interface test case relationship information set, and the data application information set. Among them, the interface test case relationship information in the above interface test case relationship information set may include an interface identifier, a test case identifier, and a test case type identifier. The above interface identifier may be the unique identifier of the interface of the data application. The above test case type identifier may be the unique identifier of the test case type. The above test case type may be one of the following: preset type, custom type. The above preset type may indicate that the corresponding test case is a pre-set test case that is not created by the user. The above custom type may indicate that the corresponding test case is a test case created by the user. The application interface relationship information in the above application interface relationship information set includes a data application identifier and an interface identifier. The data application information in the above data application information set includes a data application identifier and an adaptation subject type identifier.

[0033] Second step, determine each data application identifier in the above application interface relationship information set as the first entity identifier set.

[0034] Third step, determine each interface identifier in the above application interface relationship information set as the second entity identifier set.

[0035] Fourth step, determine each test case identifier in the above interface test case relationship information set as the third entity identifier set.

[0036] Fifth step, determine each adaptation subject type identifier in the above data application information set as the fourth entity identifier set.

[0037] Sixth step, for each application interface relationship information in the above application interface relationship information set, determine the first preset weight value, the data application identifier and the interface identifier included in the above application interface relationship information as the first entity relationship information. Among them, the above first preset weight value may indicate that the corresponding entity relationship is not affected by the user. For example, the above first preset weight value may be 0.

[0038] Seventh step, for each data application information in the above data application information set, determine the first preset weight value, the data application identifier and the adaptation subject type identifier included in the above data application information as the second entity relationship information.

[0039] Eighth step, for each interface test case relationship information in the above interface test case relationship information set, perform the following steps:

[0040] The first sub-step: In response to determining that the test case type identifier included in the above interface test case relationship information meets the preset custom type condition, determine the second preset weight value, the interface identifier and the test case identifier included in the above interface test case relationship information as the third entity relationship information. Among them, the above preset custom type condition may be that the test case type corresponding to the test case type identifier included in the interface test case relationship information is a custom type. The above second preset weight value may represent that the corresponding entity relationship is affected by the user. For example, the above second preset weight value may be 1.

[0041] The second sub-step: In response to determining that the test case type identifier included in the above interface test case relationship information does not meet the above preset custom type condition, determine the above first preset weight value, the interface identifier and the test case identifier included in the above interface test case relationship information as the fourth entity relationship information.

[0042] The ninth step: Based on the above first entity identifier set, the above second entity identifier set, the above third entity identifier set, the above fourth entity identifier set, the obtained respective first entity relationship information, respective second entity relationship information, respective third entity relationship information and respective fourth entity relationship information, generate an interface test knowledge graph. Among them, each entity corresponding to the above first entity identifier set, the above second entity identifier set, the above third entity identifier set and the above fourth entity identifier set can be used as a node, and each relationship between entities corresponding to the respective first entity relationship information, respective second entity relationship information, respective third entity relationship information and respective fourth entity relationship information can be used as an edge to construct an interface test knowledge graph.

[0043] It should be noted that in the above interface test knowledge graph, in addition to the entity identifier, each entity is also associated with other attributes. For example, when the entity is a data application, the attributes of the entity may include, but are not limited to, the name of the entity and the version number of the entity; when the entity is a data application interface, the attributes of the entity may include, but are not limited to, the path; when the entity is a test case, the attributes of the entity may include, but are not limited to, the title, input parameters (test inputs), and output parameters (expected outputs).

[0044] Step 102: In response to receiving an edit request for the test case query result, generate a test case edit page based on the edit request and send the test case edit page to the user terminal.

[0045] In some embodiments, the above-mentioned execution entity may, in response to receiving an editing request for the query result of the above-mentioned test case, generate a test case editing page based on the above-mentioned editing request in various ways, and send the above-mentioned test case editing page to the above-mentioned client. Among them, the above-mentioned editing request may be a network request for obtaining test case detail information for editing. The above-mentioned editing request may include a test case identifier. The above-mentioned test case editing page may include a test case filling form. The above-mentioned test case filling form may be a form for creating a test case. The above-mentioned test case filling form may include various form item information. Each form item information may include a form item label and a form input element. The form item label may be a unique label for marking the form input element. For example, the above-mentioned form item label may be, but is not limited to, one of the following: test case title, interface identifier, interface URL (Uniform Resource Locator), test case input data, test case output (response) data, and verification code.

[0046] In some alternative implementation manners of some embodiments, the above-mentioned execution entity may generate a test case editing page based on the above-mentioned editing request through the following steps:

[0047] First step, obtain the test case detail information corresponding to the above-mentioned editing request. Among them, the above-mentioned test case detail information may include an attribute information group. The attribute information in the above-mentioned attribute information group may include an attribute identifier and an attribute value. The above-mentioned attribute identifier may be the unique identifier of the attribute of the test case. The attributes of the test case may include, but are not limited to, test case identifier, test case title, interface URL, test case input data, creation timestamp. The test case detail information including the test case identifier the same as that in the above-mentioned editing request may be selected from a preset test case detail information database. Among them, the above-mentioned test case detail information database may be a database for storing test case detail information.

[0048] Second step, for each form input element included in the preset test case form template, perform the following steps to update the above-mentioned form input element:

[0049] First sub-step, select the attribute information that matches the above-mentioned form input element from the attribute information group included in the above-mentioned test case detail information as the target attribute information. Among them, matching the above-mentioned form input element may mean that the attribute identifier included in the attribute information is the same as the form item label corresponding to the above-mentioned form input element.

[0050] The second sub-step is to determine the element value corresponding to the form input element based on the above target attribute information, and update the value attribute corresponding to the form input element. Among them, the above element value can be the value corresponding to the value attribute of the form input element. The attribute value included in the above target attribute information can be determined as the element value corresponding to the form input element, and the above element value can be assigned to the value attribute corresponding to the form input element to update the value attribute.

[0051] The third sub-step is to add a read-only attribute to the form input element in response to determining that the form input element meets the preset element condition. Among them, the above preset element condition can be that the form item label corresponding to the form input element is the form item label corresponding to the test case output data. The above read-only attribute can indicate that the corresponding form input element can only be viewed by the user.

[0052] The fourth sub-step is to set the content editable attribute corresponding to the form input element to true in response to determining that the form input element does not meet the above preset element condition.

[0053] In addition, in the process of adopting the technical solution to solve the technical problems in the background art part, considering that the creation of test cases is a sensitive operation, in order to reduce the risk of malicious attacks and improve data and system security, therefore, in combination with the technical advantages of the solution R & D team itself in the field of deep learning, the present disclosure decides to adopt the following steps:

[0054] The third step is to generate a verification code image to be recognized in response to detecting that all form input elements included in the test case form template have been updated. Among them, the above verification code image to be recognized can be an image including a verification code for the user to recognize. The execution subject can generate a verification code image to be recognized in various ways when detecting that all form input elements included in the test case form template have been updated.

[0055] Continuing, in the process of adopting the above steps to improve data and system security, there are often the following technical problems: how to increase the machine recognition difficulty of the verification code image to resist malicious attacks. For the above technical problems, the conventional solution is generally: adding perturbation information to the original verification code image to obtain a verification code image to be recognized. However, the conventional solution also has the following technical problems: if the addition method of the perturbation information is too fixed, it is easy to make the verification code image cracked, resulting in poor data security. If too much perturbation information is added, it is difficult to be recognized by the user, resulting in poor user experience. Therefore, in the face of the problems existing in the above conventional solutions, the present disclosure decides to adopt the following solutions:

[0056] In some optional implementation manners of some embodiments, the execution subject can generate a verification code image to be recognized through the following steps:

[0057] Step 1: Obtain an image perturbation method information set, a verification code image to be perturbed, and a verification code character sequence. Among them, the image perturbation method information in the above image perturbation method information set may include a perturbation method identifier. The above perturbation method identifier may be the unique identifier of the image perturbation method. The above image perturbation method may be a method of modifying the pixels of an image to achieve the purpose of interfering with the image. For example, the above image perturbation method may be, but is not limited to, one of the following: an image perturbation method based on JSMA (Jacobian-based Saliency Map Attack, gradient-based adversarial attack), an image perturbation method based on FGSM (Fast Gradient Sign Method). The above verification code image to be perturbed may be an image including a verification code that has not been interfered with by an image perturbation method. The above verification code character sequence may be a sequence in which each character in the verification code image to be perturbed is arranged in order from left to right. The image perturbation method information set, the verification code image to be perturbed, and the verification code character sequence may be obtained from a database.

[0058] Step 2: For each image perturbation method information in the above image perturbation method information set, perform the following steps:

[0059] Sub-step 1: Use the image initial perturbation module included in the pre-deployed verification code image generation model to perturb the above verification code image to be perturbed based on the above image perturbation method information to obtain an initial perturbed verification code image. Among them, the above verification code image generation model may further include an image feature extraction module and a character recognition module. The above image initial perturbation module may be used to interfere with the verification code image to be perturbed. The above image feature extraction module may be used to extract the features of the verification code image to be perturbed. For example, the above image feature extraction module may be composed of a convolutional neural network. The above character recognition module may be used to recognize the characters in the verification code image to be perturbed. For example, the above character recognition module may be composed of a bidirectional long short-term memory network.

[0060] Sub-step 2: Input the above initial perturbed image into the above image feature extraction module to obtain a verification code image feature vector. Among them, the above verification code image feature vector may be a vector obtained by reducing the dimension of the multi-channel feature map corresponding to the initial perturbed image.

[0061] Sub-step 3: Input the above verification code image feature vector into the above character recognition module to obtain a predicted verification code character sequence.

[0062] Sub-step 4: Determine the loss value between the above verification code character sequence and the above predicted verification code character sequence. Among them, the above loss value can characterize the difference between the verification code character sequence and the above predicted verification code character sequence. The loss value between the above verification code character sequence and the above predicted verification code character sequence can be determined by the CTC (Connectionist Temporal Classification) loss function.

[0063] Step 3: Select the maximum value from the determined loss values as the target loss value.

[0064] Step 4: Based on the above target loss value, determine the interference image corresponding to the above verification code image to be perturbed. Among them, the interference image can be an image used to interfere with the verification code image to be perturbed, so that the perturbed verification code image is not easily recognized correctly by the machine learning model. The interference image corresponding to the above verification code image to be perturbed can be determined by the gradient descent algorithm based on the above target loss value.

[0065] Step 5: Perform a fusion process on the above interference image and the above verification code image to be perturbed to obtain a perturbed verification code image. Among them, the above perturbed verification code image can be the verification code image to be perturbed after being superimposed with the interference image. The above interference image and the above verification code image to be perturbed can be fused by a preset fusion method to obtain a perturbed verification code image. For example, the above fusion method can include but is not limited to at least one of: pixel weighted average method, wavelet transform fusion method.

[0066] Step 6: Use the pre-deployed interference image optimization model to perform feature extraction on the above verification code image to be perturbed to obtain a first low-level feature vector and a first global feature vector, and perform feature extraction on the above perturbed verification code image to obtain a second low-level feature vector and a second global feature vector. Among them, the above interference image optimization model can include a low-level feature extraction module and a global feature extraction module. The above low-level feature extraction module can be used to extract the low-level features of the image. The above global feature extraction module can be used to extract the global features of the image. Both the above low-level feature extraction module and the above global feature extraction module are composed of a deep convolutional neural network. For example, the above low-level feature extraction module can be composed of a VGG (Visual Geometry Group) network. The above global feature extraction module can be composed of a densely connected network.

[0067] Step 7: Determine the first feature loss value corresponding to the first underlying feature vector and the second underlying feature vector. The first feature loss value can characterize the difference in underlying features between the captcha image to be perturbed and the perturbed captcha image. First, determine the similarity between the first underlying feature vector and the second underlying feature vector through the cosine similarity method. Then, in response to determining that the similarity is less than or equal to a preset truncation threshold, determine the preset truncation threshold as the first feature loss value. The preset truncation threshold is a pre-set lower limit value used to constrain the similarity. In response to determining that the similarity is greater than the preset truncation threshold, determine the similarity as the first feature loss value.

[0068] Step 8: Determine the second feature loss value corresponding to the first global feature vector and the second global feature vector. The second feature loss value can characterize the difference in global features between the captcha image to be perturbed and the perturbed captcha image. The second feature loss value corresponding to the first global feature vector and the second global feature vector can be determined through the mean squared error loss function.

[0069] Step 9: Based on the first feature loss value and the second feature loss value, determine the optimized interference image corresponding to the perturbed captcha image. The optimized interference image can be an optimized interference image. First, determine the sum of the first feature loss value and the second feature loss value as the total loss value. Then, through the gradient descent algorithm, based on the total loss value, determine the optimized interference image corresponding to the perturbed captcha image.

[0070] Step 10: Perform a fusion process on the captcha image to be perturbed and the optimized interference image to obtain the captcha image to be recognized. The captcha image to be recognized can be obtained by performing a fusion process on the captcha image to be perturbed and the optimized interference image through the fusion method.

[0071] The above steps for generating the verification code image to be recognized are an inventive point of an embodiment of the present disclosure, which solves the technical problems "poor data security or poor user experience" existing in the above conventional solutions. The reasons for the poor data security or poor user experience in the conventional solutions are as follows: If the way of adding perturbation information is too fixed, it is easy for the verification code image to be cracked. If too much perturbation information is added, it is difficult for users to recognize. If this solution solves the above problems, the effects of improving data security and user experience can be achieved. To achieve this effect, when adding perturbations to the original verification code image, first, for each image perturbation method, perform the following respectively: first act on the original verification code image, then perform machine recognition of the verification code in the initial perturbed verification code image after the action, and determine the loss value between the recognized verification code and the known correct verification code. Thus, the adversarial ability of different image perturbation methods can be determined. Secondly, select the largest loss value from each loss value for gradient backpropagation. Thus, an interference image with better performance against machine recognition can be obtained. Then, fuse the interference image and the original verification code image. Thus, a perturbed verification code image with perturbation information can be obtained. After that, determine the underlying features and global features of the original verification code image and the perturbed verification code image. Then, through a loss function based on cosine similarity, determine the loss value on the underlying features between the original verification code image and the perturbed verification code image, and through the mean square error loss function, determine the loss value on the global features between the original verification code image and the perturbed verification code image. Thus, the differences in underlying features and global features between the original verification code image and the perturbed verification code image can be determined. Finally, according to the differences in underlying features and global features, perform gradient backpropagation on the perturbed verification code image to obtain an optimized interference image, and fuse the optimized interference image and the original verification code image to obtain the verification code image to be recognized. Thus, the generated verification code image to be recognized can not only reduce the risk of malicious attacks, but also ensure that users can effectively recognize the verification code. Thus, data security can be improved, and user experience can be improved.

[0072] Fourthly, bind the above verification code image to be recognized to the verification code image element included in the above test case form template, and determine the updated test case form template as the test case filling form. Among them, the above verification code image element can be an image control on the web page for filling the verification code image to be recognized. The image path attribute value of the verification code image element included in the test case form template can be set to the image path corresponding to the above verification code image to be recognized.

[0073] Step 5: Fill the above test case filling form into a preset test case editing page template to obtain a test case editing page. Among them, the above preset test case editing page template can be a web page template preset with form variables. The above form variables can represent the test case filling form. The above test case filling form can be rendered onto the above preset test case editing page template through a template engine to obtain a test case editing page.

[0074] Optionally, the client can perform the following steps:

[0075] Step 1: In response to detecting a click operation on the response result generation button control included in the above test case editing page, determine a target form input element sequence. Among them, the above response result generation button control can be a button control for submitting a form to generate an expected response result. Each target form input element in the above target form input element sequence can be a form input element that does not meet the above preset element conditions. The target form input element sequence can be a sequence in which the target form input elements are arranged according to the arrangement order of the elements in the editing page. First, the form input elements that do not meet the above preset element conditions can be selected from each form input element included in the above test case editing page as target form input elements to obtain each target form input element. Then, each target form input element is sorted to obtain a target form input element sequence.

[0076] Step 2: For each target form input element in the above target form input element sequence, determine the element value and form item label corresponding to the above target form input element as input item information.

[0077] Step 3: Determine the test case editing information by using the input item information sequence and the current timestamp, and send the above test case editing information.

[0078] Step 103: In response to receiving the test case editing information, determine the expected response result corresponding to the test case editing information, and send the expected response result to the client for updating the test case filling form.

[0079] In some embodiments, the above execution entity can, in response to receiving the test case editing information, determine the expected response result corresponding to the above test case editing information in various ways, and send the above expected response result to the above client for updating the above test case filling form. Among them, the above expected response result can be a response result output by a data application according to the test case editing information.

[0080] In some alternative implementations of some embodiments, the above test case editing information may include an input element value sequence. Each input element value in the above input element value sequence may be an element value of a form input element. And the execution subject may determine the expected response result corresponding to the above test case editing information through the following steps:

[0081] First step, perform data cleaning processing on each input element value included in the input element value sequence in the above test case editing information to obtain a target input element value sequence. Each target input element value in the above target input element value sequence may be an input element value after data cleaning. The data cleaning processing on each input element value included in the input element value sequence in the above test case editing information may be performed through a preset data cleaning interface to obtain a target input element value sequence. The above data cleaning interface may be encapsulated with a data cleaning function.

[0082] Second step, perform splicing processing on each target input element value in the above target input element value sequence to obtain test case creation information. The above test case creation information may be a string formed by splicing each element value. The splicing processing on each target input element value in the above target input element value sequence may be performed through a preset character to obtain test case creation information. The above preset character may be a character set in advance. For example, the above preset character may be "|".

[0083] Third step, input the above test case creation information into a pre-trained expected response data generation model to obtain an expected response result. The above expected response data generation model may include an encoder and a decoder. The above encoder may be composed of a bidirectional gated recurrent unit based on an attention mechanism. The above decoder may be composed of a gated recurrent unit and a pointer generation network. The above expected response data generation model may be trained through a conventional training method according to a historical test case information set. The above historical test case information set may be information of each test case corresponding to data application within a past period of time.

[0084] Step 104, in response to receiving the case confirmation information for the updated test case filling form, obtain the current timestamp, and perform splicing processing on the preset custom case type identifier and the current timestamp to obtain a test case identifier.

[0085] In some embodiments, the above-mentioned execution entity may, in response to receiving the case confirmation information for filling in the form of the updated test case, obtain the current timestamp, and splice the preset custom case type identifier and the current timestamp to obtain a test case identifier. Among them, the above-mentioned case confirmation information may include, but is not limited to, the form input item information set. The form input item information in the form input item information set may include a form item label and an element value. The above-mentioned current timestamp may be the timestamp of the current moment obtained from the cloud server. The above-mentioned preset custom case type identifier may indicate that the test case type corresponding to the test case is a custom type.

[0086] The above-mentioned custom test case information may be the information of the test case created by the user. First, through the UUID (Universally Unique Identifier) algorithm, a test case identifier corresponding to the custom test case is generated. Then, the preset custom test case type identifier, the above-mentioned test case identifier, and the form input item information set included in the above-mentioned case confirmation information may be determined as the custom test case information.

[0087] Step 105: Generate interface joint debugging data based on the test case identifier and the case confirmation information.

[0088] In some embodiments, the above-mentioned execution entity may generate interface joint debugging data based on the above-mentioned test case identifier and the above-mentioned case confirmation information. Among them, the above-mentioned interface joint debugging data may be the data required when the user side conducts joint debugging with the data application interface. First, select the form input item information whose included form item label meets the preset label condition from the form input item information set included in the above-mentioned case confirmation information as the user joint debugging requirement data to obtain a user joint debugging requirement data group. Among them, the preset label condition may be that the form item label is the same as any one of the test case title, test case input data, test case output data, and interface URL. Then, the above-mentioned user joint debugging requirement data group and the above-mentioned test case identifier are determined as the interface joint debugging data.

[0089] Optionally, the above-mentioned execution entity may also determine the above-mentioned test case identifier and the form input item information set included in the above-mentioned case confirmation information as the test case detail information, and store the above-mentioned test case detail information in the test case detail information database.

[0090] Optionally, the above-mentioned execution entity may also perform the following steps:

[0091] First step: Based on the above interface joint debugging data, update the above interface test knowledge graph to obtain an updated interface test knowledge graph. First, the test cases corresponding to the above interface joint debugging data can be added as entity nodes to the above interface test knowledge graph. Then, according to the interface identifiers corresponding to the above interface joint debugging data, determine the entity nodes to be connected. After that, connect the added entity nodes and the entity nodes to be connected. Finally, determine the connected interface test knowledge graph as the updated interface test knowledge graph.

[0092] Second step: In response to receiving a joint debugging request for the above target data application, based on the above updated interface test knowledge graph, generate a sequence of test data to be jointly debugged, and send the sequence of test data to be jointly debugged to the above user terminal for the user terminal to perform data joint debugging with the data application interface corresponding to the above target data application. Among them, the above joint debugging request can be a network request sent by the user terminal for joint debugging with the target data application through the data application interface. The test data to be jointly debugged in the above sequence of test data to be jointly debugged can be the interface joint debugging data corresponding to the above target data application. First, perform an accurate retrieval process on the above updated interface test knowledge graph according to the data application identifier corresponding to the above target data application to obtain each test case identifier associated with the above target data application. Then, for each test case identifier among the above test case identifiers, determine the interface joint debugging data corresponding to the test case identifier as the test data to be jointly debugged. After that, sort the test data to be jointly debugged in the order of the timestamps when the interface joint debugging data was generated to obtain a sequence of test data to be jointly debugged. Finally, send the above sequence of test data to be jointly debugged to the above user terminal. The user can, through the user terminal, initiate an interface request based on the received test data to be jointly debugged and perform data joint debugging with the data application interface corresponding to the above target data application.

[0093] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the interface joint debugging data generation method of some embodiments of the present disclosure, users can generate interface joint debugging data according to their own test requirements, which is convenient for timely discovering problems existing in local code through joint debugging. Specifically, the reason why it is difficult for the user side to maintain the local code in a timely manner is that: since the positive test case data can only be used to test the expected normal behaviors or functions of the user-side code, it is difficult for the user side to test the reliability of the local code under wrong operations or abnormal conditions. Therefore, it is difficult for the user side to maintain the local code in a timely manner. Based on this, in the interface joint debugging data generation method of some embodiments of the present disclosure, first, in response to receiving the interface joint debugging test case query information corresponding to the target data application, retrieve the pre-generated interface test knowledge graph to obtain the test case query result, and send the above test case query result to the user side. Thus, the information of the test case that matches the user's joint debugging test requirements can be obtained. Then, in response to receiving the edit request for the above test case query result, based on the above edit request, generate a test case edit page, and send the above test case edit page to the user side. Among them, the above test case edit page includes a test case filling form. Thus, when the queried test case does not meet the user's test requirements, a test case edit page can be provided for the user so that the user can modify the queried test case according to their own needs to create a test case that meets the user's requirements. After that, in response to receiving the test case edit information, determine the expected response result corresponding to the above test case edit information, and send the above expected response result to the above user side for updating the above test case filling form. Thus, the expected response data corresponding to the test case can be generated according to the information of the test case edited by the user. Next, in response to receiving the case confirmation information for the above updated test case filling form, obtain the current timestamp, and splice the preset custom case type identifier and the above current timestamp to obtain a test case identifier. Thus, a unique test case identifier can be assigned to the test case created by the user. Finally, based on the above test case identifier and the above case confirmation information, generate interface joint debugging data. Thus, the test data required for the user to perform joint debugging according to the self-created test case can be obtained. Therefore, in the interface joint debugging data generation method of some embodiments of the present disclosure, by providing a test case edit page for the user, when the user needs to test the reliability of the local code under wrong operations or abnormal conditions, the user can conveniently generate interface joint debugging data that meets their own joint debugging test requirements, thereby facilitating the user side to maintain the local code in a timely manner.

[0094] Further referring to Figure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an interface joint debugging data generation device. These device embodiments are related toFigure 1 correspond to the method embodiments shown, and the interface joint debugging data generation device 200 can be specifically applied to various electronic devices.

[0095] As Figure 2 shown, the interface joint debugging data generation device 200 in some embodiments includes: a retrieval and sending unit 201, a first generation and sending unit 202, a determination and sending unit 203, an acquisition and splicing processing unit 204, and a second generation unit 205. Among them, the retrieval and sending unit 201 is configured to retrieve the pre-generated interface test knowledge graph in response to receiving the interface joint debugging test case query information corresponding to the target data application, obtain the test case query result, and send the test case query result to the user terminal; the first generation and sending unit 202 is configured to generate a test case editing page based on the editing request in response to receiving the editing request for the test case query result, and send the test case editing page to the user terminal, where the test case editing page includes a test case filling form; the determination and sending unit 203 is configured to determine the expected response result corresponding to the test case editing information in response to receiving the test case editing information, and send the expected response result to the user terminal for updating the test case filling form; the acquisition and splicing processing unit 204 is configured to obtain the current timestamp in response to receiving the case confirmation information for the updated test case filling form, and perform splicing processing on the preset custom case type identifier and the current timestamp to obtain a test case identifier; the second generation unit 205 is configured to generate interface joint debugging data based on the test case identifier and the case confirmation information.

[0096] It can be understood that the units described in the interface joint debugging data generation device 200 correspond to the respective steps in the method described with reference to Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the interface joint debugging data generation device 200 and the units included therein, and will not be repeated here.

[0097] Further referring to Figure 3 which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not impose any limitation on the functions and usage scopes of the embodiments of the present disclosure.

[0098] As Figure 3As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 302 or a program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0099] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be implemented or had alternatively. Figure 3 Each block shown in may represent one device or, as needed, multiple devices.

[0100] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above functions defined in the methods of some embodiments of the present disclosure are executed.

[0101] It should be noted that, in some embodiments of the present disclosure, the above-mentioned computer-readable medium may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0102] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0103] The above computer-readable medium may be included in the above device; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is caused to: retrieve a pre-generated interface test knowledge graph in response to receiving interface joint debugging test case query information corresponding to a target data application, obtain a test case query result, and send the test case query result to the user terminal; generate a test case editing page based on the editing request and send the test case editing page to the user terminal in response to receiving an editing request for the test case query result, wherein the test case editing page includes a test case filling form; determine an expected response result corresponding to the test case editing information and send the expected response result to the user terminal for updating the test case filling form in response to receiving the test case editing information; obtain a current timestamp and perform a splicing process on a preset custom case type identifier and the current timestamp to obtain a test case identifier in response to receiving a case confirmation information for the updated test case filling form; generate interface joint debugging data based on the test case identifier and the case confirmation information.

[0104] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by connecting through the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in an order different from that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0106] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes: a retrieval and sending unit, a first generation and sending unit, a determination and sending unit, an acquisition and splicing processing unit, and a second generation unit. Among them, the names of these units do not constitute a limitation to the unit itself in some cases. For example, the retrieval and sending unit can also be described as "a unit that retrieves a pre-generated interface test knowledge graph to obtain a test case query result and sends the above test case query result to the user side".

[0107] The functions described above can be at least partially performed by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), and so on.

[0108] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with technical features (but not limited to) having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An interface joint debugging data generation method, comprising: In response to receiving interface joint debugging test case query information corresponding to a target data application, retrieving a pre-generated interface test knowledge graph to obtain a test case query result, and sending the test case query result to a user terminal; In response to receiving an edit request for the test case query result, generating a test case edit page based on the edit request, and sending the test case edit page to the user terminal, wherein the test case edit page includes a test case filling form; In response to receiving test case edit information, determining an expected response result corresponding to the test case edit information, and sending the expected response result to the user terminal for updating the test case filling form; In response to receiving a use case confirmation information for the updated test case filling form, obtaining a current timestamp, and performing a splicing process on a preset custom use case type identifier and the current timestamp to obtain a test case identifier, where the use case confirmation information includes a form input item information set, and the form input item information in the form input item information set includes a form item label and an element value; Generating interface joint debugging data based on the test case identifier and the use case confirmation information; Wherein, the generating a test case edit page based on the edit request includes: Obtaining test case details information corresponding to the edit request, where the test case details information includes an attribute information group; For each form input element included in a preset test case form template, perform the following steps to update the form input element: select, from the attribute information group included in the test case details information, attribute information that matches the form input element as target attribute information; determine, based on the target attribute information, an element value corresponding to the form input element, and update a value attribute corresponding to the form input element; in response to determining that the form input element meets a preset element condition, add a read-only attribute to the form input element; in response to determining that the form input element does not meet the preset element condition, set an editable attribute corresponding to the content of the form input element to true; In response to detecting that each form input element included in the test case form template has been updated, generating a captcha image to be recognized; Binding the captcha image to be recognized to a captcha image element included in the test case form template, and determining the updated test case form template as a test case filling form; Filling the test case filling form into a preset test case edit page template to obtain a test case edit page; Wherein, the generating a captcha image to be recognized includes: Obtaining an image perturbation method information set, a captcha image to be perturbed, and a captcha character sequence; For each piece of image perturbation method information in the image perturbation method information set, perform the following steps: Use the image initial perturbation module included in the pre-deployed perturbed verification code image generation model to perturb the to-be-perturbed verification code image based on the image perturbation method information to obtain an initial perturbed verification code image; input the initial perturbed image into the image feature extraction module to obtain a verification code image feature vector; input the verification code image feature vector into the character recognition module to obtain a predicted verification code character sequence; determine the loss value between the verification code character sequence and the predicted verification code character sequence; Select the maximum value from the determined loss values as the target loss value; Based on the target loss value, determine the interference image corresponding to the to-be-perturbed verification code image; Perform a fusion process on the interference image and the to-be-perturbed verification code image to obtain a perturbed verification code image; Use the pre-deployed interference image optimization model to perform feature extraction processing on the to-be-perturbed verification code image to obtain a first bottom-layer feature vector and a first global feature vector, and perform feature extraction processing on the perturbed verification code image to obtain a second bottom-layer feature vector and a second global feature vector; Determine the first feature loss value corresponding to the first bottom-layer feature vector and the second bottom-layer feature vector; Determine the second feature loss value corresponding to the first global feature vector and the second global feature vector; Based on the first feature loss value and the second feature loss value, determine the optimized interference image corresponding to the perturbed verification code image; Perform a fusion process on the to-be-perturbed verification code image and the optimized interference image to obtain an to-be-identified verification code image.

2. The method according to claim 1, wherein, The method further includes: Update the interface test knowledge graph based on the interface joint debugging data to obtain an updated interface test knowledge graph; In response to receiving a joint debugging request for the target data application, based on the updated interface test knowledge graph, generate a to-be-joint-debugged test data sequence, and send the to-be-joint-debugged test data sequence to the user terminal for the user terminal to perform data joint debugging with the data application interface corresponding to the target data application.

3. The method according to claim 1, wherein, The interface joint debugging test case query information includes a data application identifier and a query text; And the retrieval of the pre-generated interface test knowledge graph to obtain a test case query result, including: Perform keyword extraction processing on the query text included in the interface joint debugging test case query information to obtain a keyword result set; For each keyword result in the keyword result set, retrieve the interface test knowledge graph based on the keyword result to obtain a first retrieval result; Retrieve the interface test knowledge graph based on the data application identifier included in the interface joint debugging test case query information to obtain a second retrieval result set; Generate a test case query result based on the obtained first retrieval results and the second retrieval result set.

4. The method according to claim 1, wherein, The test case editing information includes an input element value sequence; And the determination of the expected response result corresponding to the test case editing information, including: Perform data cleaning processing on each input element value included in the input element value sequence in the test case editing information to obtain a target input element value sequence; Perform splicing processing on each target input element value in the target input element value sequence to obtain test case creation information; Input the test case creation information into a pre-trained expected response data generation model to obtain an expected response result, where the expected response data generation model includes an encoder and a decoder, the encoder is composed of a bidirectional gated recurrent unit based on an attention mechanism, the decoder is composed of a gated recurrent unit and a pointer generation network, and the expected response data generation model is trained according to a historical test case information set.

5. The method according to any one of claims 1 to 4, wherein The interface test knowledge graph is pre-generated through the following steps: Obtain an application interface relationship information set, an interface test case relationship information set, and a data application information set, where the interface test case relationship information in the interface test case relationship information set includes an interface identifier, a test case identifier, and a test case type identifier, the application interface relationship information in the application interface relationship information set includes a data application identifier and an interface identifier, and the data application information in the data application information set includes a data application identifier and an adaptation subject type identifier; Determine each data application identifier in the application interface relationship information set as a first entity identifier set; Determine each interface identifier in the application interface relationship information set as a second entity identifier set; Determine each test case identifier in the interface test case relationship information set as a third entity identifier set; Determine each adaptation subject type identifier in the data application information set as a fourth entity identifier set; For each application interface relationship information in the application interface relationship information set, determine a first preset weight value, the data application identifier and the interface identifier included in the application interface relationship information as first entity relationship information; For each data application information in the data application information set, determine the first preset weight value, the data application identifier and the adaptation subject type identifier included in the data application information as second entity relationship information; For each interface test case relationship information in the interface test case relationship information set, perform the following steps: In response to determining that the test case type identifier included in the interface test case relationship information meets a preset custom type condition, determine a second preset weight value, the interface identifier and the test case identifier included in the interface test case relationship information as third entity relationship information; In response to determining that the test case type identifier included in the interface test case relationship information does not meet the preset custom type condition, determine the first preset weight value, the interface identifier and the test case identifier included in the interface test case relationship information as fourth entity relationship information; Generate an interface test knowledge graph based on the first entity identification set, the second entity identification set, the third entity identification set, the fourth entity identification set, the obtained first entity relationship information, second entity relationship information, third entity relationship information, and fourth entity relationship information.

6. An interface joint debugging data generation device, comprising: A retrieval and sending unit, configured to retrieve a pre-generated interface test knowledge graph in response to receiving interface joint debugging test case query information corresponding to a target data application, obtain a test case query result, and send the test case query result to a user terminal; A first generation and sending unit, configured to generate a test case editing page based on the edit request and send the test case editing page to the user terminal in response to receiving an edit request for the test case query result, wherein the test case editing page includes a test case filling form; A determination and sending unit, configured to determine an expected response result corresponding to the test case editing information and send the expected response result to the user terminal for updating the test case filling form in response to receiving the test case editing information; An acquisition and splicing processing unit, configured to obtain a current timestamp and perform splicing processing on a preset custom use case type identifier and the current timestamp to obtain a test case identifier in response to receiving a use case confirmation information for the updated test case filling form, wherein the use case confirmation information includes a form input item information set, and the form input item information in the form input item information set includes a form item label and an element value; A second generation unit, configured to generate interface joint debugging data based on the test case identifier and the use case confirmation information; Wherein, generating the test case editing page based on the edit request includes: Obtain test case details information corresponding to the edit request, wherein the test case details information includes an attribute information group; For each form input element included in a preset test case form template, perform the following steps to update the form input element: select, from the attribute information group included in the test case details information, attribute information that matches the form input element as target attribute information; determine, based on the target attribute information, an element value corresponding to the form input element, and update a value attribute corresponding to the form input element; add a read-only attribute to the form input element in response to determining that the form input element meets a preset element condition; set an editable attribute corresponding to the content of the form input element to true in response to determining that the form input element does not meet the preset element condition; Generate a captcha image to be recognized in response to detecting that each form input element included in the test case form template has been updated; Bind the captcha image to be recognized to a captcha image element included in the test case form template, and determine the updated test case form template as the test case filling form; Fill the test case filling form into a preset test case editing page template to obtain a test case editing page; Among them, the generation of the verification code image to be recognized includes: Obtain an image perturbation method information set, a verification code image to be perturbed, and a verification code character sequence; For each image perturbation method information in the image perturbation method information set, perform the following steps: Use the image initial perturbation module included in the pre-deployed perturbation verification code image generation model to perturb the verification code image to be perturbed based on the image perturbation method information to obtain an initial perturbed verification code image; Input the initial perturbed image into the image feature extraction module to obtain a verification code image feature vector; Input the verification code image feature vector into the character recognition module to obtain a predicted verification code character sequence; Determine the loss value between the verification code character sequence and the predicted verification code character sequence; Select the maximum value from the determined loss values as the target loss value; Based on the target loss value, determine the interference image corresponding to the verification code image to be perturbed; Perform a fusion process on the interference image and the verification code image to be perturbed to obtain a perturbed verification code image; Use the pre-deployed interference image optimization model to perform feature extraction processing on the verification code image to be perturbed to obtain a first underlying feature vector and a first global feature vector, and perform feature extraction processing on the perturbed verification code image to obtain a second underlying feature vector and a second global feature vector; Determine the first feature loss value corresponding to the first underlying feature vector and the second underlying feature vector; Determine the second feature loss value corresponding to the first global feature vector and the second global feature vector; Based on the first feature loss value and the second feature loss value, determine the optimized interference image corresponding to the perturbed verification code image; Perform a fusion process on the verification code image to be perturbed and the optimized interference image to obtain a verification code image to be recognized.

7. An electronic device, including: One or more processors; A storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-5.

8. A computer-readable medium having a computer program stored thereon, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-5.

Images