Automatic interface testing platform and method

Abstract

The invention relates to the technical field of automatic testing, and particularly discloses an interface automatic testing platform and method, and the platform comprises a test demand obtaining module which is used for obtaining a test demand; the business modeling module is used for carrying out business modeling based on the test requirements to obtain a test model; the test path generation module is used for generating a test path based on the test model and converting the test path into an automatic test case; and the execution module is used for executing the test script of the automatic test case and generating a test report. Thus, content semantics and grammar structures required by testing can be fully utilized, a more accurate service flow chart is generated in an automatic mode, a basis is provided for subsequent generation of test cases meeting actual service requirements, complex service logic can be supported through the mode, and comprehensiveness and accuracy of testing can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of automated testing technology, and more specifically, to an interface automated testing platform and method. Background Art

[0002] With the rapid development of software development and Internet technology, the complexity of software products is increasing, which makes software testing more and more important. Especially in Web services and microservices architecture, interface testing has become one of the key links to ensure software quality. Traditional manual testing methods are not only time-consuming and labor-intensive, but also require frequent redesign or supplementation of mind maps and test cases, which is difficult to meet the testing requirements of rapid iteration and continuous integration. Therefore, interface automation testing has become an effective means to improve testing efficiency and test coverage.

[0003] Chinese patent CN117827669A discloses an interface automation testing device, whose testing steps are to build a test model after conducting business object analysis on the test requirements, perform testing based on the model, and use GraphWalker to generate a test path for the test model, which is converted into an automated test case, and further execute the test case to perform automated testing of the interface, thereby generating a test report.

[0004] In the above-mentioned interface automation testing device, although the interface automation testing can be realized without the need for testers to write test cases, the generation of the business process diagram depends on the correct understanding of the test requirements. If the requirements are not understood sufficiently or accurately, the generated business process diagram may contain errors, which will affect the subsequent test case generation and the accuracy of the test results. However, the solution in the above-mentioned patent only relies on traditional semantic analysis methods to analyze the test requirements to obtain the business process diagram. This method not only makes it difficult to deeply analyze and understand the contextual content of the test requirements, but also does not pay attention to the grammatical information in the test requirements. This cannot capture the implicit information and complex logic in the test requirements, resulting in an inaccurate understanding of complex test requirements, so that the generated business process diagram may have omissions or errors.

[0005] Therefore, an optimized interface automation testing platform is desired. Summary of the invention

[0006] The present application provides an interface automation testing platform and method, which can make full use of the content semantics and grammatical structure of the test requirements to generate a more accurate business process diagram in an automated manner, providing a basis for the subsequent generation of test cases that meet actual business needs. In this way, it can support complex business logic and help ensure the comprehensiveness and accuracy of the test.

[0007] In a first aspect, an interface automation testing platform is provided, comprising: a test requirement acquisition module for acquiring test requirements; a business modeling module for performing business modeling based on the test requirements to obtain a test model; a test path generation module for generating a test path based on the test model and converting the test path into an automated test case; an execution module for executing a test script of the automated test case and generating a test report; wherein the business modeling module comprises: a test requirement analysis unit for generating a business process diagram based on the test requirements, wherein the test requirement analysis unit comprises: a test requirement feature extraction subunit for performing semantic and grammatical feature extraction on the test requirements to obtain a test model; The test requirement content semantic features and the test requirement grammatical semantic features are obtained; a test requirement content-grammar joint semantic representation subunit is used to perform semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammar semantic features to obtain the test requirement content-grammar joint semantic representation; a business process diagram generation subunit is used to generate the business process diagram based on the test requirement content-grammar joint semantic representation; a model adaptation layer construction unit is used to construct a test application layer based on the business process diagram and business objects, and encapsulate the test application layer to obtain a model adaptation layer; a test model generation unit is used to update the business process diagram based on the model adaptation layer to obtain the test model.

[0008] In a second aspect, an interface automation testing method is provided, comprising: obtaining test requirements; performing business modeling based on the test requirements to obtain a test model; generating a test path based on the test model, and converting the test path into an automated test case; executing the test script of the automated test case, and generating a test report; wherein, performing business modeling based on the test requirements to obtain a test model comprises: generating a business process diagram based on the test requirements, wherein, generating a business process diagram based on the test requirements comprises: performing semantic and grammatical feature extraction on the test requirements to obtain test requirement content semantic features and test requirement grammatical semantic features; performing semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammatical semantic features to obtain a test requirement content-grammar joint semantic representation; generating the business process diagram based on the test requirement content-grammar joint semantic representation; constructing a test application layer based on the business process diagram and business objects, and encapsulating the test application layer to obtain a model adaptation layer; updating the business process diagram based on the model adaptation layer to obtain the test model.

[0009] The present application provides an interface automation testing platform and method, which includes: a test requirement acquisition module for acquiring test requirements; a business modeling module for performing business modeling based on the test requirements to obtain a test model; a test path generation module for generating a test path based on the test model and converting the test path into an automated test case; an execution module for executing the test script of the automated test case and generating a test report. In this way, the content semantics and grammatical structure of the test requirements can be fully utilized to generate a more accurate business process diagram in an automated manner, providing a basis for the subsequent generation of test cases that meet actual business requirements. In this way, complex business logic can be supported and it helps to ensure the comprehensiveness and accuracy of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application are briefly introduced below. Obviously, the drawings described below only relate to some embodiments of the present application, and are not intended to limit the present application.

[0011] Figure 1 A schematic block diagram of an interface automation testing platform according to an embodiment of the present application.

[0012] Figure 2 This is a schematic block diagram of a business modeling module in the interface automation testing platform of an embodiment of the present application.

[0013] Figure 3 This is a schematic block diagram of a test requirement analysis unit in an interface automation testing platform according to an embodiment of the present application.

[0014] Figure 4 This is a schematic diagram of the data flow of the test requirement analysis unit in the interface automation testing platform of an embodiment of the present application.

[0015] Figure 5 This is a schematic block diagram of a test requirement feature extraction subunit in the interface automation testing platform of an embodiment of the present application.

[0016] Figure 6 This is a schematic flowchart of the interface automation testing method according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0018] Chinese patent CN117827669A introduces an interface automation test device. The operation process of the device is to first conduct a detailed business object analysis of the test requirements, and then build a test model based on these analysis results. Then, the test model is used for testing, and the GraphWalker tool is used to generate test paths from the test model. These paths are then converted into automated test cases. Finally, the interface automation test is completed by executing these test cases, and the corresponding test report is output.

[0019] Although this type of interface automation testing equipment can realize automated testing without manually writing test cases, thereby improving testing efficiency, the generation of its business process diagram is highly dependent on the understanding of test requirements. If the understanding of the test requirements is not deep or accurate enough, it may cause deviations in the generated business process diagram, thereby affecting the quality of subsequent test case generation and the reliability of test results. The solution in this patent mainly uses traditional semantic analysis methods to process test requirements, which is inadequate when dealing with complex test requirements. Because traditional methods not only find it difficult to deeply parse and understand the contextual information of test requirements, but also ignore the grammatical structure in the test requirements, which makes it difficult to capture the implicit information and complex logic in the test requirements, ultimately resulting in the generated business process diagram being incomplete or containing errors.

[0020] In view of the above technical problems, in the technical solution of the present application, an interface automation testing platform is proposed, which includes: a test requirement acquisition module for acquiring test requirements; a business modeling module for performing business modeling based on the test requirements to obtain a test model; a test path generation module for generating a test path based on the test model and converting the test path into an automated test case; an execution module for executing the test script of the automated test case and generating a test report. Among them, the business modeling module includes: a test requirement analysis unit for generating a business process diagram based on the test requirements; a model adaptation layer construction unit for constructing a test application layer based on the business process diagram and business objects, and encapsulating the test application layer to obtain a model adaptation layer; a test model generation unit for updating the business process diagram based on the model adaptation layer to obtain the test model.

[0021] Figure 1 Schematic block diagram of the interface automation test platform of the embodiment of the present application. Figure 1As shown, the interface automation testing platform 1 includes: a test requirement acquisition module 10, used to acquire test requirements; a business modeling module 20, used to perform business modeling based on the test requirements to obtain a test model; a test path generation module 30, used to generate a test path based on the test model and convert the test path into an automated test case; an execution module 40, used to execute the test script of the automated test case and generate a test report.

[0022] In the above-mentioned interface automation testing platform, the test requirement acquisition module 10 is used to obtain test requirements. It should be understood that test requirements refer to the terminal's requirements for software test construction, which are sent by the terminal to the server. In the technical solution of the present application, these test requirements are obtained through the test requirement acquisition module. These test requirements can be functional, such as the input and output format of the interface, data validation rules, etc., or non-functional, such as performance indicators, security requirements, etc.

[0023] In the above-mentioned interface automation test platform, the business modeling module 20 is used to perform business modeling based on the test requirements to obtain a test model. It should be understood that the test model refers to a model used for software testing. Using a test model for software testing is actually MBT (Model-based Testing), which is a test method that models based on a description of certain aspects of the system under test, mainly functional, and then automatically generates test cases based on the model to automatically execute the verification process.

[0024] Optionally, in one embodiment of the present application, Figure 2 Schematic block diagram of the business modeling module in the interface automation test platform of the embodiment of the present application. Figure 2 As shown, the business modeling module 20 includes: a test requirement analysis unit 21, which is used to generate a business process diagram based on the test requirement; a model adaptation layer construction unit 22, which is used to construct a test application layer based on the business process diagram and business objects, and encapsulate the test application layer to obtain a model adaptation layer; a test model generation unit 23, which is used to update the business process diagram based on the model adaptation layer to obtain the test model.

[0025] In the above-mentioned interface automation test platform, the test requirement analysis unit 21 is used to generate a business process diagram based on the test requirements. It should be understood that test requirements are usually described in natural language, which may be vague and ambiguous. By generating a business process diagram, these natural language descriptions can be converted into a structured and visual form to help testers understand the business logic more accurately. The business process diagram graphically displays the various business steps and the relationship between them, making complex business logic intuitive and easy to understand. This helps testers quickly grasp the business process and avoid missing or misunderstanding key steps. At the same time, the business process diagram can clearly display all business steps and branch paths to ensure that the generated test cases can cover all possible business scenarios. This helps to avoid missing important test points and improve the comprehensiveness of the test.

[0026] In particular, in the above-mentioned interface automation testing platform, it is crucial to generate an accurate business process diagram based on the test requirements, because an accurate business process diagram can help testers better understand the business logic and ensure that the generated test cases meet the actual business requirements. If the business process diagram is inaccurate, the generated test cases may deviate from the actual requirements, resulting in unreliable test results. Specifically, the business process diagram is a visual representation of the test requirements, which can clearly show the various business steps and the relationship between them. By generating a detailed and accurate business process diagram, it can be ensured that the test cases cover all possible business scenarios and avoid missing important test points. In addition, the business process diagram can help developers and testers better understand the requirements, reduce communication barriers, and help testers design test cases that can cover all possible paths. By sharing the business process diagram, team members can more easily reach a consensus and avoid errors caused by inconsistent understanding.

[0027] Based on this, in the process of generating a business process diagram based on the test requirements, the technical concept of the present application is to capture and characterize the test requirements based on syntax and content semantics by using data processing and semantic understanding algorithms based on artificial intelligence and natural language processing technology, and automatically generate a business process diagram by using the joint significance fusion semantic representation between the syntax semantics and content semantics of the test requirements. In this way, the content semantics and grammatical structure of the test requirements can be fully utilized to generate a more accurate business process diagram in an automated manner, providing a basis for the subsequent generation of test cases that meet actual business needs. In this way, complex business logic can be supported and it helps to ensure the comprehensiveness and accuracy of the test.

[0028] Optionally, in one embodiment of the present application, Figure 3 This is a schematic block diagram of a test requirement analysis unit in an interface automation testing platform according to an embodiment of the present application. Figure 4This is a schematic diagram of the data flow of the test demand analysis unit in the interface automation test platform of the embodiment of the present application. Figure 3 and Figure 4 As shown, the test requirement analysis unit 21 includes: a test requirement feature extraction subunit 211, which is used to perform semantic and grammatical feature extraction on the test requirement to obtain test requirement content semantic features and test requirement grammatical semantic features; a test requirement content-grammar joint semantic representation subunit 212, which is used to perform semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammatical semantic features to obtain a test requirement content-grammar joint semantic representation; a business process diagram generation subunit 213, which is used to generate the business process diagram based on the test requirement content-grammar joint semantic representation.

[0029] In the above-mentioned interface automation testing platform, the test requirement feature extraction subunit 211 is used to perform semantic and grammatical feature extraction on the test requirement to obtain test requirement content semantic features and test requirement grammatical semantic features. It should be understood that by semantically parsing the test requirements, the key business objects and logical relationships in the test requirements can be extracted to generate a test requirement abstract syntax tree. This helps to accurately understand the meaning of the test requirements. Syntactic structure: Through grammatical parsing, the grammatical structure in the test requirements can be captured to ensure that each part is accurately parsed and represented. The grammatical structure provides a clear contextual relationship, which is convenient for subsequent processing and analysis. Through the combination of content semantic features and grammatical semantic features, the test requirements can be fully understood from multiple angles to support complex business logic.

[0030] Optionally, in one embodiment of the present application, Figure 5 Schematic block diagram of the test requirement feature extraction subunit in the interface automation test platform of the embodiment of the present application. Figure 5 As shown, the test requirement feature extraction subunit 211 includes: a test requirement semantic analysis secondary subunit 2111, which is used to perform semantic analysis on the test requirement to obtain a test requirement abstract syntax tree, wherein the test requirement abstract syntax tree includes a series of nodes and edges between nodes; a test requirement semantic encoding secondary subunit 2112, which is used to input the test requirement abstract syntax tree into a test requirement semantic encoder based on a tree-shaped long short-term memory network to obtain a test requirement grammatical enhancement semantic encoding vector as the test requirement grammatical semantic feature; a test requirement content semantic encoding secondary subunit 2113, which is used to perform word segmentation on the test requirement and then pass it through a semantic encoder containing a Bert model to obtain a test requirement content semantic encoding vector as the test requirement content semantic feature.

[0031] In the above-mentioned interface automation test platform, the test requirement semantic analysis secondary subunit 2111 is used to perform semantic analysis on the test requirement to obtain a test requirement abstract syntax tree, wherein the test requirement abstract syntax tree includes a series of nodes and edges between nodes. It should be understood that by performing semantic analysis on the test requirement, the test requirement in natural language form can be converted into structured data that can be understood and processed by a computer. This helps to eliminate ambiguity and ambiguity in natural language and ensure the accuracy and consistency of test requirements. In particular, the test requirement abstract syntax tree is a tree structure that can clearly represent the various words in the test requirement and the grammatical relationships between them. By constructing an abstract syntax tree, it can be ensured that each part of the test requirement is accurately parsed and represented, and the abstract syntax tree provides a clear grammatical structure to facilitate subsequent processing and analysis.

[0032] In the above-mentioned interface automation test platform, the test requirement semantic encoding secondary subunit 2112 is used to input the test requirement abstract syntax tree into the test requirement semantic encoder based on the tree-shaped long short-term memory network to obtain the test requirement syntax reinforcement semantic encoding vector as the test requirement syntax semantic feature. It should be understood that the test requirement semantic encoder based on the tree-shaped long short-term memory network can capture the contextual relationship between each tree node in the test requirement abstract syntax tree, thereby helping to better understand the contextual syntax structure of the test requirement and provide a data basis for the subsequent business process diagram generation.

[0033] In the above-mentioned interface automation testing platform, the test requirement content semantic encoding secondary subunit 2113 is used to perform word segmentation processing on the test requirement and then pass it through a semantic encoder containing a Bert model to obtain a test requirement content semantic encoding vector as the test requirement content semantic feature. It should be understood that after the test requirement is processed through word segmentation, semantic encoding is performed in a semantic encoder containing a Bert model to extract the contextual semantic association feature information between each word in the test requirement, thereby obtaining a test requirement content semantic encoding vector containing context information.

[0034] In the above-mentioned interface automation test platform, the test requirement content-grammar joint semantic representation subunit 212 is used to perform semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammar semantic features to obtain the test requirement content-grammar joint semantic representation. It should be understood that the test requirement content semantic encoding vector contains the contextual semantic association features of the content word granularity in the test requirement, and the test requirement grammar reinforcement semantic encoding vector contains the contextual grammar structure features in the test requirement. In interface automation testing, accurate analysis of test requirements is a prerequisite for generating high-quality test cases. In order to understand the test requirements more comprehensively and accurately, it is necessary to comprehensively utilize the contextual content semantics and grammar structure information in the test requirements, and perform significant feature interactive fusion of the information of the two, so as to generate an enhanced and more discriminative feature representation, which provides a basis for subsequent business process diagram generation and test case generation. Specifically, in the technical solution of the present application, the test requirement content semantic features and the test requirement grammar semantic features are further processed by semantic feature joint significant fusion to obtain the test requirement content-grammar joint semantic representation. The test requirement content-grammar joint semantic representation can better reflect the essential relationship and significant interactive fusion features between the test requirement content and the test requirement grammatical structure. It not only contains the contextual content semantics and grammatical structure information of the original test requirement, but also integrates the potential complex relationship between the two, strengthens the importance of the features, and reduces the interference of irrelevant information, thereby providing a more accurate foundation for subsequent business process diagram generation tasks, which is crucial to improving the accuracy and coverage of test case generation and improving system robustness.

[0035] Optionally, in one embodiment of the present application, the test requirement content-grammar joint semantic representation sub-unit includes: a joint implicit feature analysis secondary sub-unit, used to perform joint implicit feature analysis and activation processing on the test requirement content semantic coding vector and the test requirement grammar enhanced semantic coding vector to obtain a test requirement content-grammar joint semantic conditional feature; a joint feature interactive fusion secondary sub-unit, used to perform feature interactive fusion on the test requirement content semantic coding vector and the test requirement grammar enhanced semantic coding vector with the test requirement content-grammar joint semantic conditional feature as conditional information to obtain the test requirement content-grammar joint semantic representation.

[0036] Optionally, in one embodiment of the present application, the joint implicit feature analysis secondary sub-unit includes: a joint implicit feature capture tertiary sub-unit, used to input the test requirement content semantic coding vector and the test requirement grammar reinforcement semantic coding vector into a joint implicit feature capture network to obtain a test requirement content-grammar joint semantic implicit feature vector; a joint implicit feature activation tertiary sub-unit, used to perform feature activation based on a Sigmoid function on the test requirement content-grammar joint semantic implicit feature vector to obtain a test requirement content-grammar joint semantic conditional feature vector as the test requirement content-grammar joint semantic conditional feature.

[0037] Optionally, in one embodiment of the present application, the joint implicit feature capture three-level sub-unit is used to: add the test requirement content semantic coding vector and the test requirement grammar enhancement semantic coding vector point by point, multiply the obtained test requirement content-grammar joint semantic addition vector with the weight matrix, and then add it positionally with the bias vector to obtain the test requirement content-grammar joint semantic joint interaction vector; use the tanh function to process the test requirement content-grammar joint semantic joint interaction vector to obtain the test requirement content-grammar joint semantic implicit feature vector.

[0038] Optionally, in one embodiment of the present application, the joint feature interaction fusion secondary sub-unit includes: a test requirement content semantic contribution calculation tertiary sub-unit, which is used to calculate the test requirement content semantic contribution of the test requirement content semantic coding vector relative to the test requirement content-grammar joint semantic condition feature vector; a test requirement grammar semantic contribution calculation tertiary sub-unit, which is used to calculate the test requirement grammar semantic contribution of the test requirement grammar reinforcement semantic coding vector relative to the test requirement content-grammar joint semantic condition feature vector; a weighted modulation processing tertiary sub-unit, which is used to normalize the test requirement content semantic contribution and the test requirement grammar semantic contribution, and use the normalized test requirement content semantic contribution and the normalized test requirement grammar semantic contribution to normalize the test requirement content semantic contribution The code vector and the test requirement grammar-enhanced semantic coding vector are weighted modulated to obtain a modulated test requirement content semantic coding vector and a modulated test requirement grammar-enhanced semantic coding vector; a three-level sub-unit for processing significant guided interaction between features is used to use the modulated test requirement content semantic coding vector as a query vector, the modulated test requirement grammar-enhanced semantic coding vector as a key vector and the test requirement content-grammar joint semantic condition feature vector as a value vector, and input the modulated test requirement content semantic coding vector, the modulated test requirement grammar-enhanced semantic coding vector and the test requirement content-grammar joint semantic condition feature vector into a converter-structured significant guided interaction module between features to obtain a test requirement content-grammar joint semantic significant coding vector as the test requirement content-grammar joint semantic representation.

[0039] Optionally, in one embodiment of the present application, the third-level sub-unit for calculating the semantic contribution of the test requirement content is used to: calculate the positional division of the test requirement content semantic encoding vector and the test requirement content-syntax joint semantic condition feature vector to obtain a test requirement content semantic contribution vector; calculate the logarithmic function value with base two of the absolute value of each eigenvalue of the test requirement content semantic contribution vector to obtain a test requirement content semantic contribution logarithmic vector; calculate the positional dot product of the test requirement content semantic encoding vector and the test requirement content semantic contribution logarithmic vector, and perform position-by-position multiplication on the obtained dot product vector to obtain a test requirement content semantic contribution value; calculate an exponential function with the natural constant e as the base, with the test requirement content semantic contribution value as the exponent, to obtain the test requirement content semantic contribution.

[0040] Optionally, in one embodiment of the present application, the weighted modulation processing third-level sub-unit is used to: calculate the sum of the test requirement content semantic contribution and the test requirement grammatical semantic contribution to obtain the total sum of the test requirement content-grammar joint semantic contribution; respectively divide the test requirement content semantic contribution and the test requirement grammatical semantic contribution by the total sum of the test requirement content-grammar joint semantic contribution to obtain the normalized test requirement content semantic contribution and the normalized test requirement grammatical semantic contribution; perform position point multiplication on the test requirement content semantic coding vector and the normalized test requirement content semantic contribution to obtain the modulated test requirement content semantic coding vector; perform position point multiplication on the test requirement grammar enhanced semantic coding vector and the normalized test requirement grammar semantic contribution to obtain the modulated test requirement grammar enhanced semantic coding vector.

[0041] Optionally, in one embodiment of the present application, the post-modulation test requirement content semantic coding vector is used as a query vector, the post-modulation test requirement grammar-enhanced semantic coding vector is used as a key vector, and the test requirement content-grammar joint semantic condition feature vector is used as a value vector, and the post-modulation test requirement content semantic coding vector, the post-modulation test requirement grammar-enhanced semantic coding vector and the test requirement content-grammar joint semantic condition feature vector are input into a feature-to-feature significant guided interaction module based on a converter structure to obtain a test requirement content-grammar joint semantic significant coding vector as the test requirement content-grammar joint semantic representation, including: the post-modulation test requirement content semantic coding vector Perform vector multiplication with the transposed vector of the post-modulation test requirement grammatical reinforcement semantic coding vector, divide the obtained post-modulation test requirement content-grammar joint semantic association matrix by the square root of the length of the post-modulation test requirement grammar reinforcement semantic coding vector by position to obtain a post-modulation test requirement content-grammar joint semantic association scaling matrix; use a softmax function to activate the post-modulation test requirement content-grammar joint semantic association scaling matrix, and multiply the obtained post-modulation test requirement content-grammar joint semantic association scaling activation matrix with the test requirement content-grammar joint semantic conditional feature vector to obtain the test requirement content-grammar joint semantic salient coding vector.

[0042] In summary, in the embodiment of the present application, the semantic feature joint significant fusion processing is performed on the test requirement content semantic feature and the test requirement grammatical semantic feature by the following semantic feature joint formula to obtain the test requirement content-grammar joint semantic representation; wherein the semantic feature joint formula is: .

[0043] in, and are respectively the test requirement content semantic coding vector and the test requirement grammar reinforcement semantic coding vector, To add by location, and are the weight matrix and bias vector respectively, for function, is the test requirement content-syntactic joint semantic implicit feature vector, for function, represents the test requirement content-syntax joint semantic condition feature vector, The feature values ​​of each position in the semantic encoding vector of the test requirement content, The feature values ​​of each position in the semantic encoding vector for the test requirement grammar reinforcement, is the feature value of each position in the test requirement content-grammar joint semantic condition feature vector, is the number of feature values ​​in the test requirement content semantic encoding vector and the test requirement grammar enhanced semantic encoding vector, represents the logarithmic function value with base 2, represents an exponential function with the natural constant e as the base, To test the semantic contribution of content requirements, To test the grammatical and semantic contribution of requirements, and They are the normalized test requirement content semantic contribution and the normalized test requirement grammatical semantic contribution, and They are the semantic encoding vector of the content of the modulated test requirement and the semantic encoding vector of the grammatical reinforcement of the modulated test requirement. yes The transposed vector of yes Length, represents vector multiplication, yes function, is the test requirement content-syntactic joint semantically significant encoding vector.

[0044] More specifically, the steps of semantic feature joint significant fusion processing are as follows: first, by inputting the test requirement grammar-enhanced semantic encoding vector and the test requirement content semantic encoding vector into the joint implicit feature capture network, a test requirement content-grammar joint semantic implicit feature vector can be generated. This new vector not only contains the information of the original test requirement content semantics and grammatical structure, but also captures the intrinsic connection between the contextual content and grammatical structure of the test requirement, thereby generating a richer and more discriminative test requirement content-grammar joint semantic implicit feature representation. Subsequently, the test requirement content-grammar joint semantic implicit feature vector is activated using the Sigmoid function, which can amplify the most critical parts for describing the test requirement content-grammar joint semantic comprehensive conditions, while suppressing irrelevant noise information. In this way, it can be ensured that the model focuses on the essential key information of the test requirement and the features that are more important for the subsequent business process diagram generation task. Next, the contribution of the test requirement grammar-enhanced semantic encoding vector and the test requirement content semantic encoding vector to the test requirement content-grammar joint semantic condition feature vector is calculated, and the importance of each semantic feature to the final representation can be determined to obtain the test requirement content semantic contribution and the test requirement grammar semantic contribution. Through such an evaluation, the system can identify which features are more important for the final feature representation and the subsequent business process diagram generation task. Further, by normalizing the test requirement content semantic contribution and the test requirement grammar semantic contribution, and adjusting the original test requirement content semantic coding vector and the test requirement grammar enhanced semantic coding vector accordingly, it can be ensured that the more important test requirement content semantic features and test requirement grammar structure semantic features occupy a greater weight in the final feature representation. This helps the model to better understand and process the test requirement content semantic information and grammar structure information, and interact and match the semantic features of the two. Finally, through the significant guided interaction module between features based on the converter structure, the modulated feature vector is interactively fused. Specifically, the modulated test requirement content semantic coding vector is used as the query vector, the modulated test requirement grammar enhanced semantic coding vector is used as the key vector, and the test requirement content-grammar joint semantic condition feature vector is used as the value vector. The converter structure is used to fuse these features to generate a test requirement content-grammar joint semantic representation vector as the test requirement content-grammar joint semantic representation. This adaptive feature fusion method enables the model to examine the interaction between the semantic features of the test requirement content and the semantic features of the test requirement grammatical structure from multiple perspectives at the same time, and discover the most valuable information flow path.In particular, by performing joint significant fusion processing on the semantic features of the test requirement content semantic encoding vector and the test requirement grammatical reinforcement semantic encoding vector, the joint significant key features and potential connections between the test requirement content semantics and the test requirement grammatical reinforcement semantics can be integrated, thereby enhancing the expressive ability of semantic association features with higher relevance to subsequent tasks and reducing the interference of irrelevant semantic content information and irrelevant grammatical structure information, so as to facilitate a more comprehensive and accurate understanding of the test requirements, thereby providing a basis for subsequent business process diagram generation tasks.

[0045] In the above-mentioned interface automation test platform, the business process diagram generation subunit 213 is used to generate the business process diagram based on the test requirement content-grammar joint semantic representation. Optionally, in one embodiment of the present application, the business process diagram generation subunit is used to: input the test requirement content-grammar joint semantic significant coding vector into a decoder-based flowchart generator to obtain the business process diagram. That is to say, the semantic joint significant fusion representation between the test requirement content semantics and the grammatical structure semantics is used to perform decoding and generation, thereby automatically generating a business process diagram that meets the test requirements. In this way, the content semantics and grammatical structure of the test requirements can be fully utilized to generate a more accurate business process diagram in an automated manner, providing a basis for the subsequent generation of test cases that meet actual business requirements. In this way, complex business logic can be supported and it helps to ensure the comprehensiveness and accuracy of the test.

[0046] Preferably, the test requirement content-grammar joint semantically significant coding vector is input into a decoder-based flowchart generator to obtain a business process diagram, including: respectively calculating the first feature and value of all eigenvalues ​​of the test requirement content-grammar joint semantically significant coding vector, the second feature and value of all eigenvalues ​​of the test requirement grammar-enhanced semantic coding vector, and the third feature and value of all eigenvalues ​​of the test requirement content semantic coding vector.

[0047] After dividing the first feature sum value by the second feature sum value and the third feature sum value respectively, the square root of the sum of the squares of the two quotients is calculated to obtain the test requirement content-grammar joint semantic significant coding distribution probability value.

[0048] Calculate the product of the first test requirement content-grammar joint semantically significant coding feature value of the test requirement content-grammar joint semantically significant coding vector and the test requirement content-grammar joint semantically significant coding distribution probability value to obtain the first test requirement content-grammar joint semantically significant coding feature distribution probability product.

[0049] Calculate the product of the second test requirement content-grammar-joint semantically significant coding feature value of the test requirement content-grammar-joint semantically significant coding vector and the absolute value of the difference between one and the test requirement content-grammar-joint semantically significant coding distribution probability value to obtain the second test requirement content-grammar-joint semantically significant coding feature inverse distribution probability product.

[0050] Calculate the quotient of the first test requirement content-grammar joint semantically significant coding feature value divided by the absolute value of the difference between one and the test requirement content-grammar joint semantically significant coding distribution probability value to obtain the first test requirement content-grammar joint semantically significant coding feature inverse distribution probability quotient.

[0051] The quotient of the second test requirement content-grammar joint semantically significant coding feature value and the test requirement content-grammar joint semantically significant coding distribution probability value is calculated to obtain the second test requirement content-grammar joint semantically significant coding feature distribution probability quotient.

[0052] After adding the product of the distribution probability of the first test requirement content-grammar joint semantically significant coding feature and the product of the inverse distribution probability of the second test requirement content-grammar joint semantically significant coding feature, subtract the difference between the inverse distribution probability quotient of the first test requirement content-grammar joint semantically significant coding feature and the distribution probability quotient of the second test requirement content-grammar joint semantically significant coding feature to obtain the test requirement content-grammar joint semantically significant coding correction feature value.

[0053] For each first test requirement content-grammar joint semantically significant coding feature value and second test requirement content-grammar joint semantically significant coding feature value of the test requirement content-grammar joint semantically significant coding vector, a test requirement content-grammar joint semantically significant coding correction feature matrix composed of the test requirement content-grammar joint semantically significant coding correction feature values ​​is matrix multiplied with the test requirement content-grammar joint semantically significant coding vector to obtain a corrected test requirement content-grammar joint semantically significant coding vector.

[0054] The corrected test requirement content-syntactic-joint semantically significant encoding vector is input into a decoder-based flowchart generator to obtain a business process diagram.

[0055] Among them, the test requires content-syntax joint semantic saliency coding vector The correction process is expressed as: ;in, represents the test requirement content-syntactic joint semantically significant encoding vector, The first one represents the test requirement content-syntactic joint semantic salient encoding vector The eigenvalues ​​at the positions, The first one represents the test requirement content-syntactic joint semantic salient encoding vector The eigenvalues ​​at the positions, Represents the probability value of the test requirement content-syntactic joint semantic significant encoding distribution, Represents the test requirement content-syntactic joint semantic significant encoding correction feature value, A test requirement content-grammar-joint semantically significant coding correction feature matrix representing the test requirement content-grammar-joint semantically significant coding correction feature values, represents matrix multiplication, a test requirement content-syntactic joint semantically significant encoding vector representing the correction; and, ;in, is the test requirement grammatical reinforcement semantic encoding vector, The first The eigenvalues ​​at the positions, and is the semantic encoding vector of the test requirement content, The first one represents the semantic encoding vector of the test requirement content The eigenvalues ​​at the positions, represents the test requirement content-syntactic joint semantically significant encoding vector, The first one represents the test requirement content-syntactic joint semantic salient encoding vector The eigenvalues ​​at the positions, Represents the distribution probability value of the test requirement content-syntactic joint semantic significant encoding.

[0056] That is, when the test requirement grammatical reinforcement semantic coding vector and the test requirement content semantic coding vector respectively represent the encoded semantic features of the abstract grammatical semantics and concrete textual semantics of the test requirement, when performing significant guided interaction between features with joint implicit features as conditional information, the test requirement content-grammar joint semantic significant coding vector will also cause interaction diversity based on guided significance due to the difference in the joint implicit semantic conditional patterns of abstract semantics and concrete semantics, thereby affecting the iterative consistency of the decoding mapping and reducing the accuracy of the decoding results.

[0057] Therefore, the present application targets the region-boundary integral relationship of the high-dimensional feature manifold of the test requirement content-grammar joint semantically significant coding vector, and approximates the high-dimensional manifold representation of the feature set of the test requirement content-grammar joint semantically significant coding vector composed of eigenvalue pairs by defining the feature set of the test requirement content-grammar joint semantically significant coding vector as a whole relative to the spatial distribution probability of the test requirement grammar enhanced semantic coding vector and the test requirement content semantic coding vector as a manifold constraint boundary, so as to avoid the ambiguity in the mapping of the diversified feature representation of the test requirement content-grammar joint semantically significant coding vector to the local manifold representation in the high-dimensional feature decoding regression convergence space, improve the execution iteration consistency of each local feature distribution in the mapping task, so as to improve the feature decoding regression convergence effect, and improve the image quality of the business process diagram obtained by the decoder-based flowchart generator.

[0058] In another embodiment of the present application, the test requirement content-syntactic joint semantically significant encoding vector is input into a decoder-based flowchart generator to obtain a business process diagram, including: using a sequence-to-sequence model (Seq2Seq model) to generate nodes of the business process diagram, using a pointer network to generate connection relationships between nodes, and using a visualization library to visualize the generated nodes and connection relationships to form a final business process diagram.

[0059] In the above interface automation test platform, the model adaptation layer construction unit 22 is used to construct a test application layer based on the business process diagram and business objects, and encapsulate the test application layer to obtain a model adaptation layer. It should be understood that the test application layer refers to the application entity responsible for interacting with the system under test. Specifically, it is extended on the basis of the business objects of the test business layer, and the operation / query interface provided by the server is encapsulated separately. Inside each operation class method, the corresponding operation interface of the server is first called. If the interface reports an error, an exception will be thrown directly, and then the method corresponding to the business object is called to update the attributes in the test object. For the corresponding query class interface, a query class method is first provided separately, and then an assertion method is provided on this basis to compare the current attributes of the test business object with the return value of the server interface, so as to achieve the purpose of full field verification. During execution, the test case will store the relevant IS of the object under test in the test suite context. When this information is needed, the required information will be obtained from the context first, and then a request will be sent to the test business layer for processing. In this embodiment, the model adaptation layer is a service for converting the test path generated by GraphWalker into an executable automation case. GraphWalker is a model-based open source testing tool that supports model creation and editing, and verifies the correctness and expected behavior of the model. The GraphWalker model contains two basic elements: vertices and edges; an edge represents an action, which can be an API call, a button click, certain conditions, etc., the behavior of the system under test from one state to the next state, and a vertex represents verification, which can verify whether the API call returns the correct value, whether the dialog box is closed when the button is clicked, or whether the system under test triggers the expected event when reaching the next state. ", explains how to "build a test application layer based on the business process diagram and business objects, and encapsulate the test application layer to obtain a model adaptation layer", providing another description.

[0060] In the above-mentioned interface automation testing platform, the test model generation unit 23 is used to update the business process diagram based on the model adaptation layer to obtain the test model. That is, the nodes and edges in the business process diagram are mapped to the business objects and methods in the test application layer. The labels in the business process diagram are updated to labels that can be recognized by the model adaptation layer. For example, update "user registration" to "register", update "user login" to "login", etc. Debug the updated business process diagram to ensure that each node and edge can correctly call the method in the test application layer, and the verification results meet expectations.

[0061] In the above interface automation test platform, the test path generation module 30 is used to generate a test path based on the test model and convert the test path into an automated test case. It should be understood that after determining the circuit required for the test, several test cases can be generated according to the operation class method, query class assertion method and constraint conditions integrated in the model adaptation layer. The tester no longer needs to write the test case manually, and the generated test case can better take into account every detail of the test and improve the test coverage. Specifically, according to the traversal algorithm or strategy of the model, the corresponding test path is generated. Some special edges such as init / reset / end will be added in the processing of the edge. These can be determined by the respective projects to distinguish or control the boundaries of the use case. Generally, the process from an init to a reset or end is understood as a complete automated test process use case. In this process use case, an operation and its corresponding assertion often represent a test node, which can also be understood as a minimum unit of test case. The assertion is a collection of multiple test points, each independent and independent of each other, and verifies whether the corresponding operation is normal from their respective perspectives.

[0062] In the above interface automation test platform, the execution module 40 is used to execute the test script of the automated test case and generate a test report. That is, according to the generated test case, the corresponding test script is executed. These scripts can be codes written based on a specific test framework (such as JUnit, TestNG, etc.), or they can be a custom scripting language. During the execution process, real user behavior will be simulated, requests will be sent to the target interface, and the returned results will be captured. During the execution process, the execution results of each test case are recorded, including the success or failure status, error information, response time, etc. This information will be used to generate a detailed test report. After the test is completed, a report containing all test results is generated. The report can include the execution status of the test case, coverage statistics, performance indicators, etc. For example, the report can show the execution time, pass rate, failure reason, etc. of each test case to help testers quickly locate problems.

[0063] In summary, the interface automation testing platform according to the embodiment of the present application is explained, which uses data processing and semantic understanding algorithms based on artificial intelligence and natural language processing technology to capture and characterize the features of test requirements based on syntax and content semantics, and uses the joint significance fusion semantic representation between the syntax semantics and content semantics of the test requirements to automatically generate business process diagrams. In this way, the content semantics and grammatical structure of the test requirements can be fully utilized to generate more accurate business process diagrams in an automated manner, providing a basis for the subsequent generation of test cases that meet actual business needs. In this way, complex business logic can be supported and it helps to ensure the comprehensiveness and accuracy of the test.

[0064] Figure 6 Schematic flow chart of the interface automation testing method of the embodiment of the present application. Figure 6 As shown, the interface automation testing method includes: S1, obtaining test requirements; S2, performing business modeling based on the test requirements to obtain a test model; S3, generating a test path based on the test model, and converting the test path into an automated test case; S4, executing the test script of the automated test case and generating a test report.

[0065] Optionally, in one embodiment of the present application, business modeling is performed based on the test requirements to obtain a test model, including: generating a business process diagram based on the test requirements; constructing a test application layer based on the business process diagram and business objects, and encapsulating the test application layer to obtain a model adaptation layer; and updating the business process diagram based on the model adaptation layer to obtain the test model.

[0066] Optionally, in one embodiment of the present application, a business process diagram is generated based on the test requirements, including: performing semantic and grammatical-based feature extraction on the test requirements to obtain test requirement content semantic features and test requirement grammatical semantic features; performing semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammatical semantic features to obtain a test requirement content-grammar joint semantic representation; and generating the business process diagram based on the test requirement content-grammar joint semantic representation.

[0067] Here, those skilled in the art can understand that the specific operations of each step in the above interface automation testing method have been referred to above. Figures 1 to 5 The interface automation test platform is described in detail in the description of the interface automation test platform, and therefore, its repeated description will be omitted.

[0068] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0069] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the module division is only a logical function division, and there may be other division methods in actual implementation. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0070] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0072] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units stated in the device claim can also be implemented by one unit through software or hardware.

[0073] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An interface automation testing platform, characterized in that: include: Test requirement acquisition module, used to obtain test requirements; A business modeling module, used for performing business modeling based on the test requirements to obtain a test model; A test path generation module, used to generate a test path based on the test model and convert the test path into an automated test case; An execution module is used to execute the test script of the automated test case and generate a test report; wherein the business modeling module includes: a test requirement analysis unit, which is used to generate a business process diagram based on the test requirement, wherein the test requirement analysis unit includes: a test requirement feature extraction subunit, which is used to perform semantic and grammatical feature extraction on the test requirement to obtain test requirement content semantic features and test requirement grammatical semantic features; a test requirement content-grammar joint semantic representation subunit, which is used to perform semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammatical semantic features to obtain a test requirement content-grammar joint semantic representation; a business process diagram generation subunit, which is used to generate the business process diagram based on the test requirement content-grammar joint semantic representation; a model adaptation layer construction unit, which is used to construct a test application layer based on the business process diagram and business objects, and encapsulate the test application layer to obtain a model adaptation layer; a test model generation unit, which is used to update the business process diagram based on the model adaptation layer to obtain the test model.

2. The interface automation test platform according to claim 1, characterized in that: The test requirement feature extraction subunit includes: a test requirement semantic parsing secondary subunit, which is used to perform semantic parsing on the test requirement to obtain a test requirement abstract syntax tree, wherein the test requirement abstract syntax tree includes a series of nodes and edges between nodes; a test requirement semantic encoding secondary subunit, which is used to input the test requirement abstract syntax tree into a test requirement semantic encoder based on a tree-shaped long short-term memory network to obtain a test requirement grammatical enhancement semantic encoding vector as the test requirement grammatical semantic feature; a test requirement content semantic encoding secondary subunit, which is used to perform word segmentation processing on the test requirement and then pass it through a semantic encoder containing a Bert model to obtain a test requirement content semantic encoding vector as the test requirement content semantic feature.

3. The interface automation test platform according to claim 2, characterized in that: The test requirement content-grammar joint semantic representation subunit includes: a joint implicit feature analysis secondary subunit, which is used to perform joint implicit feature analysis and activation processing on the test requirement content semantic coding vector and the test requirement grammar enhanced semantic coding vector to obtain the test requirement content-grammar joint semantic conditional feature; a joint feature interactive fusion secondary subunit, which is used to perform feature interactive fusion on the test requirement content semantic coding vector and the test requirement grammar enhanced semantic coding vector with the test requirement content-grammar joint semantic conditional feature as conditional information to obtain the test requirement content-grammar joint semantic representation.

4. The interface automation test platform according to claim 3, characterized in that: The joint implicit feature analysis secondary sub-unit includes: a joint implicit feature capture tertiary sub-unit, which is used to input the test requirement content semantic coding vector and the test requirement grammar reinforcement semantic coding vector into a joint implicit feature capture network to obtain a test requirement content-grammar joint semantic implicit feature vector; a joint implicit feature activation tertiary sub-unit, which is used to perform feature activation based on the Sigmoid function on the test requirement content-grammar joint semantic implicit feature vector to obtain a test requirement content-grammar joint semantic conditional feature vector as the test requirement content-grammar joint semantic conditional feature.

5. The interface automation test platform according to claim 4, characterized in that: The joint implicit feature capture three-level sub-unit is used to: add the test requirement content semantic coding vector and the test requirement grammar reinforcement semantic coding vector point by point, multiply the obtained test requirement content-grammar joint semantic addition vector with the weight matrix, and then add it with the bias vector point by point to obtain the test requirement content-grammar joint semantic joint interaction vector; use the tanh function to process the test requirement content-grammar joint semantic joint interaction vector to obtain the test requirement content-grammar joint semantic implicit feature vector.

6. The interface automation test platform according to claim 5, characterized in that: The joint feature interaction fusion secondary sub-unit includes: a test requirement content semantic contribution calculation tertiary sub-unit, which is used to calculate the test requirement content semantic contribution of the test requirement content semantic coding vector relative to the test requirement content-grammar joint semantic condition feature vector; a test requirement grammar semantic contribution calculation tertiary sub-unit, which is used to calculate the test requirement grammar semantic contribution of the test requirement grammar reinforcement semantic coding vector relative to the test requirement content-grammar joint semantic condition feature vector; a weighted modulation processing tertiary sub-unit, which is used to normalize the test requirement content semantic contribution and the test requirement grammar semantic contribution, and use the normalized test requirement content semantic contribution and the normalized test requirement grammar semantic contribution to normalize the test requirement content semantic coding vector and the test requirement The requirement syntax-enhanced semantic coding vector is weighted modulated to obtain a post-modulation test requirement content semantic coding vector and a post-modulation test requirement syntax-enhanced semantic coding vector; a three-level sub-unit for processing significant guided interaction between features is used to use the post-modulation test requirement content semantic coding vector as a query vector, the post-modulation test requirement syntax-enhanced semantic coding vector as a key vector and the test requirement content-grammar joint semantic condition feature vector as a value vector, and input the post-modulation test requirement content semantic coding vector, the post-modulation test requirement syntax-enhanced semantic coding vector and the test requirement content-grammar joint semantic condition feature vector into a feature-significant guided interaction module based on a converter structure to obtain a test requirement content-grammar joint semantic significant coding vector as the test requirement content-grammar joint semantic representation.

7. The interface automation test platform according to claim 6, characterized in that: The third-level sub-unit for calculating the semantic contribution of the test requirement content is used to: calculate the positional division of the test requirement content semantic encoding vector and the test requirement content-syntax joint semantic condition feature vector to obtain a test requirement content semantic contribution vector; calculate the logarithmic function value with base two of the absolute value of each eigenvalue of the test requirement content semantic contribution vector to obtain a test requirement content semantic contribution logarithmic vector; calculate the positional dot product of the test requirement content semantic encoding vector and the test requirement content semantic contribution logarithmic vector, and perform positional multiplication on the obtained dot product vector to obtain a test requirement content semantic contribution value; calculate an exponential function with the natural constant e as the base, with the test requirement content semantic contribution value as the exponent, to obtain the test requirement content semantic contribution degree.

8. The interface automation test platform according to claim 7, characterized in that: The weighted modulation processing tertiary sub-unit is used to: calculate the sum of the test requirement content semantic contribution and the test requirement grammar semantic contribution to obtain the total sum of the test requirement content-grammar joint semantic contribution; The test requirement content semantic contribution and the test requirement grammatical semantic contribution are respectively divided by the total sum of the test requirement content-grammar joint semantic contributions to obtain the normalized test requirement content semantic contribution and the normalized test requirement grammatical semantic contribution; the test requirement content semantic coding vector is multiplied by the normalized test requirement content semantic contribution at the position point to obtain the modulated test requirement content semantic coding vector; the test requirement grammar-enhanced semantic coding vector is multiplied by the normalized test requirement grammar-enhanced semantic contribution at the position point to obtain the modulated test requirement grammar-enhanced semantic coding vector.

9. The interface automation test platform according to claim 8, characterized in that: The business process diagram generating subunit is used to: input the test requirement content-grammar joint semantically significant coding vector into a decoder-based flowchart generator to obtain the business process diagram.

10. An interface automation testing method, characterized in that: include: Obtain test requirements; Perform business modeling based on the test requirements to obtain a test model; Generate a test path based on the test model, and convert the test path into an automated test case; Execute the test script of the automated test case and generate a test report; wherein, business modeling is performed based on the test requirements to obtain a test model, including: generating a business process diagram based on the test requirements, wherein, generating a business process diagram based on the test requirements includes: performing semantic and grammatical feature extraction on the test requirements to obtain test requirement content semantic features and test requirement grammatical semantic features; performing semantic feature joint significant fusion processing on the test requirement content semantic features and the test requirement grammatical semantic features to obtain a test requirement content-grammar joint semantic representation; generating the business process diagram based on the test requirement content-grammar joint semantic representation; constructing a test application layer based on the business process diagram and business objects, and encapsulating the test application layer to obtain a model adaptation layer; updating the business process diagram based on the model adaptation layer to obtain the test model.

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