Function verification method, device, equipment, medium and program product

Abstract

This application provides a functional verification method applicable to the fields of artificial intelligence or big data technology. The method includes: acquiring a preset knowledge graph, the knowledge graph including markers indicating differences between functional points in a first system and functional points in a second system, the differences including difference types; acquiring first system test cases corresponding to the differences; based on the difference types, executing a corresponding test case generation strategy on the first system test cases to obtain second system test cases; calculating the functional coverage of the second system with respect to the first system; and, if the functional coverage is greater than a preset coverage threshold, performing regression testing verification based on the second system test cases. This application also provides a functional verification device, equipment, storage medium, and program product.

Description

Technical Field

[0001] This application relates to the field of distributed systems, and more specifically to a functional verification method, apparatus, device, medium, and program product. Background Technology

[0002] Driven by business needs, the financial industry is implementing various system upgrades and transformations. During this process, it's necessary to migrate legacy systems to new distributed systems. To ensure the effectiveness of the migration, it's essential to verify the systems before and after the migration. Current technology relies on manually writing test cases and implementing various tests on both the old and new systems. However, this approach of verifying the old and new systems with manually written test cases suffers from low verification efficiency and incomplete full-function verification coverage. Summary of the Invention

[0003] In view of the above problems, this application provides functional verification methods, apparatus, equipment, media and program products that improve verification efficiency and verification coverage.

[0004] According to a first aspect of this application, a functional verification method is provided, comprising: acquiring a preset knowledge graph, the knowledge graph including marking differences between functional points in a first system and functional points in a second system, the differences including difference types; acquiring first system test cases corresponding to the differences; executing a corresponding test case generation strategy on the first system test cases based on the difference types to obtain second system test cases; calculating the functional coverage of the second system with respect to the first system; and performing regression testing verification based on the second system test cases if the functional coverage is greater than a preset coverage threshold.

[0005] According to an embodiment of this application, the method for establishing the preset knowledge graph includes: acquiring a first multi-dimensional element of the first system and a second multi-dimensional element of the second system; establishing a one-to-one corresponding knowledge graph based on the first multi-dimensional element and the second multi-dimensional element; performing semantic modeling based on the first multi-dimensional element and the second multi-dimensional element to obtain a first semantic set and a second semantic set; calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set; and filtering out discrepancies and corresponding discrepancy types based on the confidence set, and updating the knowledge graph.

[0006] According to an embodiment of this application, the step of performing semantic modeling based on the first multidimensional element and the second multidimensional element to obtain a first semantic set and a second semantic set includes: converting the first multidimensional element and the second multidimensional element into word vectors to obtain the first semantic set and the second semantic set.

[0007] According to an embodiment of this application, calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set includes: calculating a one-to-one similarity based on the first semantic set and the second semantic set to obtain the similarity; performing rule semantic analysis based on the first semantic set and the second semantic set to obtain rule categories; and obtaining the confidence based on the similarity and the rule categories.

[0008] According to an embodiment of this application, the step of filtering out discrepancies and their corresponding types based on the confidence set and updating the knowledge graph includes: marking the discrepancy type as completely identical when the similarity is greater than a first preset threshold; marking the discrepancy type as partially changed when the similarity is less than the first preset threshold but greater than a second preset threshold; and marking the discrepancy type as completely new when the similarity is less than a third preset threshold.

[0009] According to an embodiment of this application, the step of executing a corresponding test case generation strategy on the first system test case based on the difference point type to obtain a second system test case includes: when the difference point type is completely consistent, using the first system test case as the second system test case; when the difference point type is partially changed, generating the second system test case based on the first system test case; and when the difference point type is completely new, generating the second system test case.

[0010] According to an embodiment of this application, calculating the functional coverage of the second system with respect to the first system includes: obtaining a first number of function points of the first system and a second number of function points of the second system; and calculating the functional coverage based on the first number of function points and the second number of function points.

[0011] A second aspect of this application provides a functional verification apparatus, the apparatus comprising: an acquisition module for acquiring a preset knowledge graph, the knowledge graph including markers of differences between functional points in a first system and functional points in a second system, the differences including difference types; the acquisition module further for acquiring test cases for the first system corresponding to the differences; a test case generation module for executing a corresponding test case generation strategy on the test cases for the first system based on the difference types to obtain test cases for the second system; a coverage testing module for calculating the functional coverage of the second system with respect to the first system; and a regression testing verification module for performing regression testing verification based on the test cases for the second system when the functional coverage is greater than a preset coverage threshold.

[0012] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0013] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0014] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0015] In the embodiments of this application, to address the technical problems of low verification efficiency and invalid verification results that easily occur during the verification of new and old systems in the prior art, the embodiments of this application, during the system verification process, guide the transformation of test cases from the first system to test cases from the second system using the differences in a pre-set knowledge graph that reflects the differences between the first and second systems. When the coverage between the two systems is significant, functional testing is considered feasible. Subsequently, regression testing of the second system is performed using the test cases, thereby achieving functional verification. In the embodiments of this application, at least the following beneficial effects can be achieved: 1. Through the system difference knowledge graph and intelligent mapping mechanism, the differences between corresponding functional points can be quickly found, and test cases can be generated based on these differences, significantly improving the efficiency of test case generation. 2. A knowledge graph-based functional coverage verification mechanism ensures the integrity of functional coverage: a knowledge graph-based functional coverage verification mechanism. Attached Figure Description

[0016] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1 The illustrations depict application scenarios of functional verification methods, apparatus, devices, media, and program products according to embodiments of this application.

[0018] Figure 2 A flowchart illustrating a functional verification method according to an embodiment of this application is shown schematically.

[0019] Figure 3 A schematic diagram illustrating the structure of a functional verification apparatus according to an embodiment of this application is shown; and

[0020] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a functional verification method according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0024] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0025] Before providing a detailed disclosure of this application, the key technical data involved in the embodiments of this application will be explained one by one, as follows:

[0026] The financial industry is constantly upgrading its systems, requiring functional verification of both systems after the upgrade to ensure the new system can perform the functions of the old system. Generally, the test cases for the old system are existing test cases, while the test cases for the new system need to be newly generated due to various differences between the old and new systems to verify the functions of the new system. In current technology, traditional methods are often used to manually analyze the test cases of the old system in order to rewrite the test cases of the new system and conduct verification tests. However, this approach in current technology leads to low efficiency in test case conversion, requiring an average of 30%-40% of the original testing workload in manpower, and is prone to missing many functional scenarios.

[0027] Embodiments of this application provide a functional verification method, the method comprising: acquiring a preset knowledge graph, the knowledge graph including marking functional points in a first system and functional points in a second system as differences, the differences including difference types; acquiring first system test cases corresponding to the differences; executing a corresponding test case generation strategy on the first system test cases based on the difference types to obtain second system test cases; calculating the functional coverage of the second system with respect to the first system; and performing regression testing verification based on the second system test cases if the functional coverage is greater than a preset coverage threshold.

[0028] In the embodiments of this application, to address the technical problems of low verification efficiency and invalid verification results that easily occur during the verification of new and old systems in the prior art, the embodiments of this application, during the system verification process, guide the transformation of test cases from the first system to test cases from the second system using the differences in a pre-set knowledge graph that reflects the differences between the first and second systems. When the coverage between the two systems is significant, functional testing is considered feasible. Subsequently, regression testing of the second system is performed using the test cases, thereby achieving functional verification. In the embodiments of this application, at least the following beneficial effects can be achieved: 1. Through the system difference knowledge graph and intelligent mapping mechanism, the differences between corresponding functional points can be quickly found, and test cases can be generated based on these differences, significantly improving the efficiency of test case generation. 2. A knowledge graph-based functional coverage verification mechanism ensures the integrity of functional coverage: a knowledge graph-based functional coverage verification mechanism.

[0029] Figure 1 The diagram illustrates an application scenario of the functional verification method according to an embodiment of this application.

[0030] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, and a server 105. Network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0031] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0032] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0033] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0034] It should be noted that the functional verification method provided in this application embodiment can generally be executed by server 105. Correspondingly, the functional verification device provided in this application embodiment can generally be located in server 105. The functional verification method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the transaction processing device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0035] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0036] The following will be based on Figure 1 The described scene, through Figure 2 The functional verification method according to the embodiments of this application will be described in detail.

[0037] Figure 2 A flowchart illustrating a functional verification method according to an embodiment of this application is shown schematically.

[0038] like Figure 2 As shown, the transaction processing method of this embodiment includes operations S210 to S250, and the transaction processing method can be executed by the server 105.

[0039] In operation S210, a preset knowledge graph is obtained. The knowledge graph includes marking the differences between functional points in the first system and functional points in the second system. The differences include the difference type.

[0040] In this context, the first system refers to the old system before the migration, and the second system refers to the new system after the migration. For example, the first system could be based on a centralized architecture, while the second system could be based on a distributed architecture. The first system integrates multiple functional points, and the second system integrates multiple functional points. Different functional points are responsible for different business functions. For example, a certain function might be responsible for accounting. Due to the system architecture, even functional points that perform accounting tasks will have some differences between the first and second systems. These differences are called discrepancies.

[0041] The preset knowledge graph includes at least all functional points of the first system, all functional points of the second system, the mapping relationship between all functional points of the first system and all functional points of the second system, the business rules of all functional points of the first system, the business rules of all functional points of the second system, and the difference point types corresponding to the differences in these one-to-one mapping relationships. It should be noted that the difference point types include at least completely consistent, partially changed, completely added, and completely obsolete.

[0042] The following section will provide a detailed explanation of the method for building this knowledge graph:

[0043] According to an embodiment of this application, the method for establishing the preset knowledge graph includes: acquiring a first multi-dimensional element of the first system and a second multi-dimensional element of the second system; establishing a one-to-one corresponding knowledge graph based on the first multi-dimensional element and the second multi-dimensional element; performing semantic modeling based on the first multi-dimensional element and the second multi-dimensional element to obtain a first semantic set and a second semantic set; calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set; and filtering out discrepancies and corresponding discrepancy types based on the confidence set, and updating the knowledge graph.

[0044] Among them, multi-dimensional elements refer to the multi-dimensional elements of the corresponding functional point. The multi-dimensional elements of each functional point include business rules, data structures, and interface protocols. Specifically, the business rules define the business execution logic of the functional point. Business rules can be, for example, clearing rules or accounting rules. Different business rules result in different execution logics, and the same business rule may also be executed differently in different systems. The data structure refers to the data structure used by the functional point during execution. The data structure can be, for example, message format, database field type and length. Different functional points use different data structures, and the same functional point may use different data structures in different systems. The interface protocol defines the backend interface interaction mode of the functional point during execution. The interface protocol dimension can be, for example, communication methods and security mechanisms. Different functional points use different interface protocols, and the same functional point may use different interface protocols in different systems.

[0045] Specifically, firstly, the first multi-dimensional elements of each functional point in the first system and the second multi-dimensional elements of each functional point in the second system are obtained. A knowledge graph structure is established based on the mapping relationship between the functional points in the first and second systems, and then the multi-dimensional elements of these functional points are added to the knowledge graph. Next, semantic modeling is performed on these texts to extract semantic information, forming a first semantic set belonging to the functional points in the first system and a second semantic set belonging to the functional points in the second system. This semantic information extraction can involve extracting key semantic information from the text or using the entire text data as the semantic set. Then, by comparing the first and second semantic sets, the confidence level between the same functional point is calculated, thereby quantifying the degree of difference of the same functional point in different systems. Finally, this confidence level is used to determine the type of difference between the two functional points.

[0046] It is understood that, in the embodiments of this application, multi-dimensional elements of the first and second systems are obtained, and a knowledge graph with a mapping mechanism is established through these multi-dimensional elements. Subsequently, through semantic modeling and other means, the difference types between various test points in the semantic set are automatically identified, and the labeling of the knowledge graph is updated based on the difference types. The establishment of the knowledge graph can effectively guide subsequent functional verification, accelerate verification efficiency and verification usability.

[0047] According to an embodiment of this application, the step of performing semantic modeling based on the first multidimensional element and the second multidimensional element to obtain a first semantic set and a second semantic set includes: converting the first multidimensional element and the second multidimensional element into word vectors to obtain the first semantic set and the second semantic set.

[0048] Specifically, in the process of semantic modeling, the information in the text of multi-dimensional elements can be fully converted into word vectors and then saved so that quantitative calculations can be directly performed later. It is understood that in the embodiments of this application, semantic modeling can adopt a relatively simple form of converting into word vectors. This approach is fast and efficient and suitable for the migration and upgrading of large-scale systems.

[0049] According to an embodiment of this application, calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set includes: calculating a one-to-one similarity based on the first semantic set and the second semantic set to obtain the similarity; performing rule semantic analysis based on the first semantic set and the second semantic set to obtain rule categories; and obtaining the confidence based on the similarity and the rule categories.

[0050] Specifically, the confidence score includes the confidence scores between corresponding functional points, which are composed of similarity and rule categories. For the similarity of the same functional point, similarity can be calculated by separately calculating the similarity between paragraphs or the entire text in business rules, data structures, and interface protocols. A weighted summation method can be used to comprehensively calculate the overall similarity of a functional point by combining the similarity scores of business rules, data structures, and interface protocols. For the rule categories of the same functional point, fixed fields in the business rules can be extracted and identified. For example, if there are fixed fields in the business rules that are marked with rule categories, these can be extracted and the corresponding rule categories identified. Finally, the similarity and rule categories are combined to form the confidence score of the functional point. It can be understood that the confidence score is formed by combining similarity and rule categories, and by evaluating the confidence score from multiple perspectives, the classification of differences in subsequent test points becomes more reasonable.

[0051] According to an embodiment of this application, the step of filtering out discrepancies and their corresponding types based on the confidence set and updating the knowledge graph includes: marking the discrepancy type as completely identical when the similarity is greater than a first preset threshold; marking the discrepancy type as partially changed when the similarity is less than the first preset threshold but greater than a second preset threshold; and marking the discrepancy type as completely new when the similarity is less than a third preset threshold.

[0052] Specifically, for any function point in the second system, its similarity to each function point in the first system is calculated, and the highest similarity among all similarities is selected for subsequent comparison with a preset threshold. If the similarity is greater than the first preset threshold, the difference point type is marked as completely identical; for example, if the similarity is greater than 99%, the two function points are marked as completely identical. If the similarity is less than the first preset threshold but greater than the second preset threshold, the difference point type is marked as partially changed; for example, if the similarity is less than 99% but greater than 40%, the two function points are marked as partially changed. If the similarity is less than the third preset threshold, the difference point type is marked as completely new; that is, the difference point is a new difference point in the second system, and there is no corresponding function point in the first system. For example, if the similarity is less than 20%, it indicates that the function point in the second system did not search for a consistent or similar function point in the first system, and the function point is a new function point, thus it is marked as completely new in the second system. Understandably, different marking methods are used for different types of differences. By combining similarity to mark differences, it is possible to quickly mark completely identical, partially changed, and completely new types of differences, thereby speeding up the marking efficiency.

[0053] In addition, there may be differences that exist in the first system but not in the second system. These differences can be marked as completely obsolete, and regression verification is not required for such functionalities.

[0054] In operation S220, the first system test case corresponding to the difference point is obtained.

[0055] Specifically, the first system test case corresponding to the difference point is obtained. The first test system test case is a test case for the functional point in the first system. The test case is used for regression testing. The test case includes at least the input data of the corresponding function and the expected output data of the corresponding function. The expected output data is used to verify whether the output data of the actual system in the regression test meets the expectations.

[0056] In operation S230, based on the difference point type, the corresponding test case generation strategy is executed on the first system test case to obtain the second system test case.

[0057] Specifically, the first system test cases can be transformed into second system test cases using certain transformation logic. During the generation strategy, different generation logic is executed based on the type of difference. The execution of the test case generation strategy can be done manually or by calling an external large model interface, using prompts and the first system test cases; details will not be elaborated further here.

[0058] According to an embodiment of this application, the step of executing a corresponding test case generation strategy on the first system test case based on the difference point type to obtain a second system test case includes: when the difference point type is completely consistent, using the first system test case as the second system test case; when the difference point type is partially changed, generating the second system test case based on the first system test case; and when the difference point type is completely new, generating the second system test case.

[0059] Specifically, for differences that are "completely identical," test cases from the first system are directly reused. For differences that are "partially changed," test data and expected results are adjusted to generate test cases adapted to the second system. This step can be performed manually or through a large model. For manual execution, the relevant first and second multi-dimensional elements, along with the first system test cases, are sent to relevant personnel for manual generation. For large-scale model execution, the relevant first and second multi-dimensional elements, along with the first system test cases, are sent to the large model, along with prompts, for automatic generation. For differences that are "completely new," this step can be performed manually or through a large model. For manual execution, the relevant second multi-dimensional elements are sent to relevant personnel for manual generation. For large-scale model execution, the second multi-dimensional elements and prompts are sent to the large model, which automatically generates the corresponding second system test cases. For differences that are "completely obsolete," they are marked as obsolete cases.

[0060] A large model is introduced to enhance the quality of test case generation. The differential knowledge graph is used as context input into the large model to generate test steps and expected results described in natural language.

[0061] It is understandable that the test case generation logic differs for different types of difference points. For some difference point types, the test cases of the second system can be directly generated from the test cases of the first system, reducing the time consumed in test case generation.

[0062] In operation S240, the functional coverage of the second system over the first system is calculated.

[0063] According to an embodiment of this application, calculating the functional coverage of the second system with respect to the first system includes: obtaining a first number of function points of the first system and a second number of function points of the second system; and calculating the functional coverage based on the first number of function points and the second number of function points.

[0064] The first set of function points refers to all function points in the first system, and the second set of function points refers to all function points in the second system that overlap with those in the first system. The first and second function point counts are used to calculate the functional coverage between the two systems. For example, if the first set of function points is 100 and the second set is 90, the functional coverage is 90%. It is understood that coverage testing is conducted using the function point counts from both systems to ensure the effectiveness of testing both the old and new systems.

[0065] In operation S250, if the functional coverage is greater than a preset coverage threshold, regression testing is performed based on the second system test case for verification.

[0066] Specifically, the functions of the first and second systems are not one-to-one. In many cases, the second system may not possess the functions of the first system, and vice versa. Therefore, it is necessary to calculate the functional coverage between the two systems to distinguish the differences in functionality. Regression verification testing is then performed when the functional coverage between the first and second systems exceeds a preset coverage threshold, thus demonstrating the effectiveness of the regression test. For example, when performing regression testing on the second system, various functions within the second system are executed using generated second system test cases. It should be noted that one second system test case is used to test a specific functional point.

[0067] Furthermore, the coverage rates of the two systems can be calculated in multiple ways to provide multi-dimensional statistical indicators for subsequent output. The first functional point count refers to the total number of functional points in the first system, while the second functional point count includes the number of functional points fully supported by the second system, the number of functional points partially supported by the second system, and the number of functional points not supported by the second system. Specifically, a mapping relationship between the complete set of functional points in the first system and the set of functional points in the second system can be constructed based on a system difference knowledge graph. A graph traversal algorithm can then be used to identify the first system functional points not covered in the second system. A first system functional point matrix is ​​constructed, including all functional points and their associated business scenarios, preconditions, and postconditions. A subset of implemented functional points in the second system is then determined using a graph traversal algorithm.

[0068] The indicator calculation is as follows:

[0069] Full coverage = Number of first-function points fully supported by the second system / Total number of function points of the first system

[0070] Partial coverage = Number of first function points partially supported by the second system / Total number of function points of the first system

[0071] Coverage Rate = Number of first function points not supported by the second system / Total number of function points in the first system

[0072] In the embodiments of this application, to address the technical problems of low verification efficiency and invalid verification results that easily occur during the verification of new and old systems in the prior art, the embodiments of this application, during the system verification process, guide the transformation of test cases from the first system to test cases from the second system using the differences in a pre-set knowledge graph that reflects the differences between the first and second systems. When the coverage between the two systems is significant, functional testing is considered feasible. Subsequently, regression testing of the second system is performed using the test cases, thereby achieving functional verification. In the embodiments of this application, at least the following beneficial effects can be achieved: 1. Through the system difference knowledge graph and intelligent mapping mechanism, the differences between corresponding functional points can be quickly found, and test cases can be generated based on these differences, significantly improving the efficiency of test case generation. 2. A knowledge graph-based functional coverage verification mechanism ensures the integrity of functional coverage: a knowledge graph-based functional coverage verification mechanism.

[0073] Based on the above transaction processing method, this application also provides a transaction processing apparatus. The following will be combined with... Figure 3 The device is described in detail.

[0074] Figure 3 A schematic block diagram of a functional verification device according to an embodiment of this application is shown.

[0075] like Figure 3 As shown, the functional verification device 300 of this embodiment includes an acquisition module 310, a case generation module 320, a coverage testing module 330, and a regression testing verification module 340.

[0076] The acquisition module 310 is used in the first transaction phase to perform transaction processing on the target data and freeze it to obtain the first processing record. In one embodiment, the acquisition module 310 can be used to perform the operation S210 described above, which will not be repeated here.

[0077] The acquisition module 310 is further configured to generate a second processing record based on the first processing record, the second processing record including: processing status and failure retries, wherein the processing status is a first status value. In one embodiment, the acquisition module 310 may be used to perform the operation S220 described above, which will not be repeated here.

[0078] The case generation module 320 is used in the second transaction phase to obtain the transaction corresponding to the first state value and modify the first state value to the second state value. In one embodiment, the case generation module 320 can be used to perform the operation S230 described above, which will not be repeated here.

[0079] The coverage testing module 330 is used to perform business services based on transactions with the second state value. In one embodiment, the coverage testing module 330 can be used to perform the operation S240 described above, which will not be repeated here.

[0080] The regression testing and verification module 340 is used to update the second status value to a third status value when the business service processing is successful. The third status value is used to indicate that the business service processing is complete. In one embodiment, the regression testing and verification module 340 can be used to perform the operation S250 described above, which will not be repeated here.

[0081] In the embodiments of this application, to address the technical problems of low verification efficiency and invalid verification results that easily occur during the verification of new and old systems in the prior art, the embodiments of this application, during the system verification process, guide the transformation of test cases from the first system to test cases from the second system using the differences in a pre-set knowledge graph that reflects the differences between the first and second systems. When the coverage between the two systems is significant, functional testing is considered feasible. Subsequently, regression testing of the second system is performed using the test cases, thereby achieving functional verification. In the embodiments of this application, at least the following beneficial effects can be achieved: 1. Through the system difference knowledge graph and intelligent mapping mechanism, the differences between corresponding functional points can be quickly found, and test cases can be generated based on these differences, significantly improving the efficiency of test case generation. 2. A knowledge graph-based functional coverage verification mechanism ensures the integrity of functional coverage: a knowledge graph-based functional coverage verification mechanism.

[0082] According to an embodiment of this application, the method for establishing the preset knowledge graph includes: acquiring a first multi-dimensional element of the first system and a second multi-dimensional element of the second system; establishing a one-to-one corresponding knowledge graph based on the first multi-dimensional element and the second multi-dimensional element; performing semantic modeling based on the first multi-dimensional element and the second multi-dimensional element to obtain a first semantic set and a second semantic set; calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set; and filtering out discrepancies and corresponding discrepancy types based on the confidence set, and updating the knowledge graph.

[0083] According to an embodiment of this application, the step of performing semantic modeling based on the first multidimensional element and the second multidimensional element to obtain a first semantic set and a second semantic set includes: converting the first multidimensional element and the second multidimensional element into word vectors to obtain the first semantic set and the second semantic set.

[0084] According to an embodiment of this application, calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set includes: calculating a one-to-one similarity based on the first semantic set and the second semantic set to obtain the similarity; performing rule semantic analysis based on the first semantic set and the second semantic set to obtain rule categories; and obtaining the confidence based on the similarity and the rule categories.

[0085] According to an embodiment of this application, the step of filtering out discrepancies and their corresponding types based on the confidence set and updating the knowledge graph includes: marking the discrepancy type as completely identical when the similarity is greater than a first preset threshold; marking the discrepancy type as partially changed when the similarity is less than the first preset threshold but greater than a second preset threshold; and marking the discrepancy type as completely new when the similarity is less than a third preset threshold.

[0086] According to an embodiment of this application, the step of executing a corresponding test case generation strategy on the first system test case based on the difference point type to obtain a second system test case includes: when the difference point type is completely consistent, using the first system test case as the second system test case; when the difference point type is partially changed, generating the second system test case based on the first system test case; and when the difference point type is completely new, generating the second system test case.

[0087] According to an embodiment of this application, calculating the functional coverage of the second system with respect to the first system includes: obtaining a first number of function points of the first system and a second number of function points of the second system; and calculating the functional coverage based on the first number of function points and the second number of function points.

[0088] According to embodiments of this application, any multiple modules among the acquisition module 310, case generation module 320, coverage testing module 330, and regression testing verification module 340 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the acquisition module 310, case generation module 320, coverage testing module 330, and regression testing verification module 340 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 310, the case generation module 320, the coverage testing module 330, and the regression testing verification module 340 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0089] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a transaction processing method according to an embodiment of this application.

[0090] like Figure 4 As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0091] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0092] According to embodiments of this application, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0093] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0094] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0095] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0096] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0097] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0098] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0099] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0101] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A functional verification method, characterized in that, The method includes: Obtain a preset knowledge graph, which includes marking the differences between functional points in the first system and functional points in the second system, and the differences include the type of differences. Obtain the first system test case corresponding to the aforementioned differences; Based on the aforementioned difference type, the corresponding test case generation strategy is executed on the first system test case to obtain the second system test case; and Calculate the functional coverage of the second system over the first system; and If the functional coverage is greater than a preset coverage threshold, regression testing is performed based on the second system test case for verification.

2. The method according to claim 1, characterized in that, The method for establishing the preset knowledge graph includes: Obtain the first multidimensional element of the first system and the second multidimensional element of the second system; A knowledge graph with a one-to-one correspondence is established based on the first multi-dimensional element and the second multi-dimensional element. Semantic modeling is performed based on the first multidimensional element and the second multidimensional element to obtain a first semantic set and a second semantic set. Calculate the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set; and Based on the confidence set, discrepancies and their corresponding types are selected, and the knowledge graph is updated.

3. The method according to claim 2, characterized in that, The semantic modeling based on the first multidimensional features and the second multidimensional features yields a first semantic set and a second semantic set, including: The first multidimensional element and the second multidimensional element are converted into word vectors to obtain the first semantic set and the second semantic set.

4. The method according to claim 2, characterized in that, The step of calculating the mapping confidence between the first semantic set and the second semantic set to obtain a confidence set includes: The similarity is obtained by calculating a one-to-one similarity between the first semantic set and the second semantic set; Based on the first semantic set and the second semantic set, perform rule semantic analysis to obtain rule categories; and The confidence level is obtained based on the similarity and the rule category.

5. The method according to claim 4, characterized in that, The step of filtering out discrepancies and their corresponding types based on the confidence set, and updating the knowledge graph, includes: If the similarity is greater than a first preset threshold, the difference points are marked as completely identical. If the similarity is less than a first preset threshold and greater than a second preset threshold, the difference point type is marked as a partial change; and If the similarity is less than a third preset threshold, the difference point type is marked as completely new.

6. The method according to claim 5, characterized in that, Based on the difference point type, the corresponding test case generation strategy is executed on the first system test case to obtain the second system test case, including: If the types of differences are completely identical, the first system test case will be used as the second system test case. When the difference type is a partial change, a second system test case is generated based on the first system test case; and If the difference point type is completely new, a second system test case is generated.

7. The method according to claim 1, characterized in that, The calculation of the functional coverage of the second system over the first system includes: Obtain the first function point count of the first system and the second function point count of the second system; and The function coverage is calculated based on the first function point count and the second function point count.

8. A functional verification device, characterized in that, The device includes: The acquisition module is used to acquire a preset knowledge graph, which includes marking the differences between functional points in the first system and functional points in the second system, and the differences include the type of differences. The acquisition module is also used to acquire the first system test case corresponding to the difference point; The test case generation module is used to execute a corresponding test case generation strategy on the first system test cases based on the difference point type to obtain the second system test cases; and The coverage testing module is used to calculate the functional coverage of the second system with respect to the first system; and The regression testing and verification module is used to perform regression testing and verification based on the second system test cases when the functional coverage is greater than a preset coverage threshold.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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