CN116126704A - A mock automatic number creation method, device and medium based on semantic recognition - Google Patents

Abstract

The invention discloses a mock automatic number creation method, device and medium based on semantic recognition, including acquiring basic field information of an interface, inputting the basic field information into a semantic recognition model, and determining semantic similarity; generating field data based on semantic similarity , form the basic field library; configure the interface request information and the corresponding mock rules to form the mock rule library for the request interface to be created; receive the interface call request from the requesting party, and obtain the call message information; match the mock according to the call message information The corresponding mock rules in the rule base; according to the mock rules, determine the structure of the response message and the field values of the generated data to obtain the response message; and return the response message to the requester to complete the response. The data of the present invention can be flexibly changed to meet the requirement of covering multiple scenarios; at the same time, the labor cost of maintaining the generation rules of the response fields is reduced one by one, greatly improving the efficiency of number creation and the efficiency of interface testing.

Description

A mock automatic number generation method, device and medium based on semantic recognition

Technical Field

The present invention relates to the field of software testing, and is mainly applied to a method for verifying the connectivity and correctness of an interface by creating numbers in a mock manner in interface testing, and specifically relates to a mock automatic number creation method, device and medium based on semantic recognition.

Background Art

Software testing is an important part of software development, in which interface testing is used to verify the correctness of system interactions. In interface testing, the instability of third-party services or systems and the complexity of the environment will lead to low efficiency in testing using real services. Therefore, using mocks is a common interface testing method. By creating numbers to simulate the data return of the request system, the correctness of the interface can be verified, which can remove the dependence on the other party's services, reduce environmental maintenance costs, effectively improve testing efficiency, and quickly check interface defects.

In the existing mock technology, the main number-generating methods are the following two categories:

1. Create numbers manually. Create a mock object and manually configure the response information.

2. Generate responses automatically through automation. After the tester defines the structure and response field type on the mock platform, he configures the field generation rules through configuration. When requesting the interface, the mock tool automatically generates a response message according to the configured rules.

However, both of the above methods have some problems:

1. Relying on manual number creation, usually a mock object only corresponds to one mock response data, the data content is fixed and difficult to change flexibly. If you want to cover multiple test scenarios, you need to manually adjust the mock data multiple times, which greatly affects the test efficiency. When the expected result message body involved is more complex, the maintenance cost of the manual method is high. At the same time, due to the artificial simulation of the data, the data authenticity is low, and it is difficult to truly replace the actual return content of the caller.

2. The flexibility of data has been improved by automatically generating responses in a configurable way, but on the other hand, the accuracy of data depends on the accuracy of configuration. If the authenticity of data is to be improved, the cost of configuration will increase, resulting in low test efficiency. Especially when the structure of the response message involved is complex, the manual cost of maintaining the generation rules for each field one by one is extremely high.

Summary of the invention

The purpose of the present invention is to provide a mock automatic number generation method, device and medium based on semantic recognition. The present invention can automatically identify field semantics according to the configuration parameter field name and field type, and then automatically generate simulation data based on semantics. The data of the present invention can be flexibly changed to meet the needs of covering multiple scenarios; at the same time, it reduces the labor cost of maintaining the response field generation rules one by one, greatly improves the number generation efficiency, and thus improves the interface testing efficiency.

The present invention is achieved through the following technical solutions:

In a first aspect, the present invention provides a mock automatic number generation method based on semantic recognition, the method comprising:

Obtain basic field information of the interface, input the basic field information into the semantic recognition model, and determine the semantic similarity; generate field data based on the semantic similarity to form a basic field library;

For the request interface to be created, configure the interface request information and the corresponding mock rules to form a mock rule library; the basic field information of the interface is configured in the mock rules, and the generated field data is used to match the response result of the interface;

Receive the interface call request from the requester and obtain the call message information; match the corresponding mock rules in the mock rule library according to the call message information; determine the response message structure and the field values of the generated data according to the mock rules to obtain the response message; and return the response message to the requester to complete the response.

Furthermore, the basic field information of the acquisition interface is input into a semantic recognition model to determine semantic similarity; and field data is generated based on the semantic similarity to form a basic field library, including:

Get the basic field information of the interface;

Input the basic field information into the pre-trained semantic recognition model to obtain the semantic vector of the basic field and the semantic vector of the expected number-making result;

Determine semantic similarity based on the semantic vector of the basic field and the semantic vector of the expected number-making result;

Based on semantic similarity, field data is generated to form a basic field library.

Among them, the semantic similarity can measure the similarity and closeness of the semantic vector of each basic field with the semantic vector of the expected number-making result. In the subsequent interface call request obtained, the simulated data of the interface field can be generated based on the similarity, so that the generated result is highly similar to the expected result and is the desired number-making result.

Furthermore, the basic field information includes field name, field type, field length, and expected result value.

Furthermore, for each interface to be created (needs to generate test data), configure a mock rule that can uniquely identify the interface; filter the required interface return field data from the basic field library, and establish a corresponding relationship between the mock rule and the field data.

Furthermore, for the request interface to be created, the interface request information and the corresponding mock rules are configured to form a mock rule library, including:

Treat the request interface for number generation and obtain basic information of the interface;

Configure the corresponding mock rules according to the basic information of the interface; the mock rules include interface request information and response results; the configuration of the mock rules is as follows: first configure the interface request information, which includes the interface name and interface call path. The interface name is used to uniquely identify each request interface; the call path is used to receive the request and uniquely match the corresponding mock rule when initiating an interface call request; then configure the interface response result. The response result supports json format string. According to the predefined response format in the interface document, set the mock response expected message format.

The above technical solution selects the field names to be created (i.e., the field names that need to be created) from the basic field library and maintains them in the interface response content. In subsequent interface requests, the interface response content will replace the basic fields in the response result with the expected value of the creation according to the field values generated by the semantic recognition model, and assemble them into a response message and return it to the interface requester.

Furthermore, the basic information includes the interface name, the calling address and the response result.

Further, the receiving interface call request of the requesting party obtains the call message information; matches the corresponding mock rule in the mock rule library according to the call message information; determines the response message structure and the field value of the generated data according to the mock rule, and obtains the response message; and returns the response message to the requesting party to complete the response, including:

Receive the external http interface call request from the requester that calls the mock platform, and obtain the call message information according to the external http interface call request;

According to the call message information, the call path in the request message information is obtained; according to the received request address, the corresponding mock rule in the mock rule library is matched, that is, according to the received request address, the interface call path configured in the mock rule library is matched to determine the interface corresponding to the request and its corresponding mock rule;

According to the mock rules, determine the response information, that is, the response message structure;

According to the basic fields configured in the response message structure, the field values of the data are automatically generated based on the semantic recognition model; and the field values are assembled into the response message structure;

In response to the request interface, the assembled mock response message object with generated data is returned to the requester.

In a second aspect, the present invention further provides a mock automatic number generation device based on semantic recognition, which supports the mock automatic number generation method based on semantic recognition; the device comprises:

A basic field library forming unit is used to obtain basic field information of an interface, input the basic field information into a semantic recognition model, determine semantic similarity; generate field data based on the semantic similarity, and form a basic field library;

The mock rule library configuration unit is used to configure the interface request information and the corresponding mock rules for the request interface to form a mock rule library; the basic field information of the interface is configured in the mock rule, and the field data is generated to match the response result of the interface;

The interface request and response unit is used to receive the interface call request from the requester and obtain the call message information; match the corresponding mock rules in the mock rule library according to the call message information; determine the response message structure and the field values of the generated data according to the mock rules to obtain the response message; and return the response message to the requester to complete the response.

In a third aspect, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the mock automatic number generation method based on semantic recognition when executing the computer program.

In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the mock automatic number generation method based on semantic recognition is implemented.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

1. The present invention provides a mock automatic number generation method, device and medium based on semantic recognition. The present invention can automatically identify field semantics according to the configuration parameter field name and field type, and then automatically generate simulation data based on the semantics. The data of the present invention can be flexibly changed to meet the needs of covering multiple scenarios; at the same time, it reduces the labor cost of maintaining the response field generation rules one by one, greatly improves the number generation efficiency, and thus improves the interface testing efficiency.

2. The present invention improves the authenticity of mock data generation: since mock data is generated based on field features and semantic recognition simulation, it is closer to the actual response of the real external system.

3. The present invention improves the flexibility and diversity of mock number generation: the obtained response result can generate different field values based on each request, rather than being fixed, to ensure that the content of the response result obtained by the requester each time is different. The more times the call is made, the richer the value of the actual response result is, which helps to simulate the test scenario and use different response results to cover more verification branches.

4. The present invention is universal and can simulate all http requests to obtain response results; the response data structure in the mock rule supports flexible configuration, and the mock object can be arbitrarily configured according to the response required by the request.

5. The present invention improves test efficiency: after the basic field library is maintained, the field variables can be applied to multiple mock rules, making the mock rule configuration more flexible. After completing the basic field library maintenance once, the basic field library can be reused. Different from the traditional mock rule configuration method, there is no need to configure data generation rules for each field, which reduces the complexity of mock configuration. Testers can focus on the impact of response results on test scenarios, improving test efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

FIG1 is a flow chart of a mock automatic number generation method based on semantic recognition according to the present invention.

FIG. 2 is a flow chart of step 1 of an embodiment of the present invention.

FIG3 is a flow chart of step 2 of an embodiment of the present invention.

FIG. 4 is a flow chart of step three of the embodiment of the present invention.

FIG5 is a schematic diagram of the structure of a mock automatic number-generating device based on semantic recognition according to the present invention.

DETAILED DESCRIPTION

In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary implementation modes of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

Example 1

As shown in FIG1 , the present invention provides a mock automatic number generation method based on semantic recognition, the method comprising:

Step 1: Obtain basic field information of the interface, input the basic field information into the semantic recognition model, determine the semantic similarity; generate field data based on the semantic similarity to form a basic field library; specifically include:

Get the basic field information of the interface; the basic field information includes field name, field type, field length and expected result value.

Input the basic field information into the pre-trained semantic recognition model to obtain the semantic vector of the basic field and the semantic vector of the expected number-making result;

Determine semantic similarity based on the semantic vector of the basic field and the semantic vector of the expected number-making result;

Based on semantic similarity, field data is generated to form a basic field library.

Among them, the semantic similarity can measure the similarity and closeness of the semantic vector of each basic field with the semantic vector of the expected number-making result. In the subsequent interface call request obtained, the simulated data of the interface field can be generated based on the similarity, so that the generated result is highly similar to the expected result and is the desired number-making result.

Step 2: For the request interface to be created, configure the interface request information and the corresponding mock rules to form a mock rule library; the basic field information of the interface is configured in the mock rule, and the field data is generated to match the response result of the interface; for each interface to be created (need to generate test data), configure a mock rule that can uniquely identify the interface; filter the required interface return field data from the basic field library, and establish the corresponding relationship between the mock rule and the field data. Specifically include:

For the request interface to be created, obtain the basic information of the interface; the basic information includes the interface name, call address and response result.

Configure the corresponding mock rules according to the basic information of the interface; the mock rules include interface request information and response results; the configuration of the mock rules is as follows: first configure the interface request information, which includes the interface name and interface call path. The interface name is used to uniquely identify each request interface; the call path is used to receive the request and uniquely match the corresponding mock rule when initiating an interface call request; then configure the interface response result. The response result supports json format string. According to the predefined response format in the interface document, set the mock response expected message format.

The above technical solution selects the field names to be created (i.e., the field names that need to be created) from the basic field library and maintains them in the interface response content. In subsequent interface requests, the interface response content will replace the basic fields in the response result with the expected value of the creation according to the field values generated by the semantic recognition model, and assemble them into a response message and return it to the interface requester.

Step 3: Receive the interface call request from the requester and obtain the call message information; match the corresponding mock rule in the mock rule library according to the call message information; determine the response message structure and the field value of the generated data according to the mock rule to obtain the response message; and return the response message to the requester to complete the response; specifically including:

Receive the external http interface call request from the requester that calls the mock platform, and obtain the call message information according to the external http interface call request;

According to the call message information, the call path in the request message information is obtained; according to the received request address, the corresponding mock rule in the mock rule library is matched, that is, according to the received request address, the interface call path configured in the mock rule library is matched to determine the interface corresponding to the request and its corresponding mock rule;

According to the mock rules, determine the response information, that is, the response message structure;

According to the basic fields configured in the response message structure, the field values of the data are automatically generated based on the semantic recognition model; and the field values are assembled into the response message structure;

In response to the request interface, the assembled mock response message object with generated data is returned to the requester.

The method of the present invention is described in detail below with reference to a case.

The present invention comprises the following steps:

1. Obtain and maintain the basic field information of the interface, and maintain the field information to be created (i.e., the field information that needs to be created) in the basic field library of the mock platform;

The following is the interface document information for generating simulation data, see Table 1:

Table 1 Interface document information table for expected generation of simulation data

Maintain the field information to be created (i.e., the field information that needs to be created) in the basic field library of the mock platform. The information that needs to be maintained is shown in Table 2:

2. Based on the semantic recognition model, automatically identify the basic field semantics of the configuration and generate field values:

Based on the pre-trained semantic recognition model, the basic field information, field name, data type, and field length of the maintained field information are used to identify the expected field value, and the similarity between the basic field semantics and the expected field value semantics is obtained. Based on this similarity, it can be used to generate data results that are highly similar to the expected basic field value. For example, the semantic recognition model identifies the semantics of the CompanyAddress company address field, and the randomly generated field values include "No. 3001, Maojiaqiao, Xihu District, Hangzhou City, Zhejiang Province", "No. 969, Wenyi West Road, Yuhang District, Hangzhou City, Zhejiang Province", "No. 11, Lugu Town, Mianning County, Liangshan Yi Autonomous Prefecture, Sichuan Province", etc.

3. Configure mock rules according to interface information. You need to configure the following:

Interface name: SearchCompanyInfo

Call path: /mock/test/SearchCompanyInfo

Response result:

Among them, "#field name#" identifies the field variable name that needs to be created from the basic field library. When the interface is called later, the generated field value replaces the variable in the response content and is assembled into a complete response message (i.e., response result) and returned to the requester.

4. Based on a certain test scenario, replace the call address of the target system with the mock platform address to shield the third-party system. For example, configure the interface call address as http://(mock platform server IP address)/mock/test/SearchCompanyInfo, initiate an http interface call, and the mock platform receives the call request and obtains the request path and request message information.

The obtained request path is: /mock/test/SearchCompanyInfo,

The request message obtained is:

{

"CerditNo":"XXXXXX"

}

5. According to the request path, the mock platform queries the configured mock rules and matches the mock rules that are consistent with the request path. The corresponding interface on the mock platform is found to be SearchCompanyInfo, and the response message corresponding to the interface is obtained.

6. According to the interface response message, obtain the basic field variables in the message and automatically generate data according to the semantic recognition model.

The basic field variables obtained are: #Name#, #IDcard#, #PhoneNo#, #CompanyName#, #Address#

Based on the semantic recognition model, the above basic fields can automatically and randomly generate data values for response. The generated data is different for each call, which can be used to generate batch interface call request data. For example, in the test, two requests are called to the mock platform interface, and two response messages are generated respectively as follows:

7. The mock platform responds to the requester and returns a response message with the generated data.

The mock automatic number generation method based on semantic recognition of the present invention has the following advantages:

First, improve the authenticity of mock data: Since mock data is generated based on field features and semantic recognition, it is closer to the actual response of the real external system.

Second, improve the flexibility and diversity of mock number generation: the response result can generate different field values based on each request, rather than being fixed, to ensure that the content of the response result obtained by the requester is different each time. The more times the call is made, the richer the value of the actual response result is, which helps to simulate the test scenario and use different response results to cover more verification branches;

Third, this method is universal and can simulate all http requests to obtain response results; the response data structure in the mock rule supports flexible configuration, and the mock object can be arbitrarily configured according to the response required by the request;

Fourth, improve test efficiency: After the basic field library is maintained, field variables can be applied to multiple mock rules, making mock rule configuration more flexible. After completing a basic field library maintenance, the basic field library can be reused. Different from the traditional mock rule configuration method, there is no need to configure data generation rules for each field, reducing the complexity of mock configuration. Testers can focus on the impact of response results on test scenarios and improve test efficiency.

Example 2

As shown in FIG5 , the difference between this embodiment and embodiment 1 is that this embodiment provides a mock automatic number generation device based on semantic recognition, and the device supports a mock automatic number generation method based on semantic recognition described in embodiment 1; the device includes:

A basic field library forming unit is used to obtain basic field information of an interface, input the basic field information into a semantic recognition model, determine semantic similarity; generate field data based on the semantic similarity, and form a basic field library;

The mock rule library configuration unit is used to configure the interface request information and the corresponding mock rules for the request interface to form a mock rule library; the basic field information of the interface is configured in the mock rule, and the field data is generated to match the response result of the interface;

The interface request and response unit is used to receive the interface call request from the requester and obtain the call message information; match the corresponding mock rules in the mock rule library according to the call message information; determine the response message structure and the field values of the generated data according to the mock rules to obtain the response message; and return the response message to the requester to complete the response.

Among them, the execution process of each unit can be executed according to the process steps of the mock automatic number generation method based on semantic recognition described in Example 1, and will not be repeated one by one in this embodiment.

At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the mock automatic number generation method based on semantic recognition when executing the computer program.

At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the mock automatic number generation method based on semantic recognition is implemented.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims (10)

1. A mock automatic number generation method based on semantic recognition, characterized in that the method comprises: Obtaining basic field information of the interface, inputting the basic field information into a semantic recognition model, and determining semantic similarity; generating field data based on the semantic similarity to form a basic field library; For the request interface to be created, configure the interface request information and the corresponding mock rules to form a mock rule library; Receive the interface call request from the requester and obtain the call message information; match the corresponding mock rules in the mock rule library according to the call message information; determine the response message structure and the field values of the generated data according to the mock rules to obtain the response message; and return the response message to the requester to complete the response. 2. According to claim 1, a mock automatic numbering method based on semantic recognition is characterized in that the basic field information of the interface is obtained, the basic field information is input into the semantic recognition model, and the semantic similarity is determined; based on the semantic similarity, field data is generated to form a basic field library, including: Get the basic field information of the interface; Inputting the basic field information into a pre-trained semantic recognition model to obtain a basic field semantic vector and a semantic vector of an expected number-making result; Determining semantic similarity according to the semantic vector of the basic field and the semantic vector of the expected number-making result; Based on the semantic similarity, field data is generated to form a basic field library. 3. According to a semantic recognition-based mock automatic number generation method according to claim 2, it is characterized in that the basic field information includes field name, field type, field length and expected result value. 4. According to a semantic recognition-based mock automatic number generation method according to claim 1, it is characterized in that for each interface to be generated, a mock rule that uniquely determines the interface is configured; the required interface return field data is screened from the basic field library, and a corresponding relationship between the mock rule and the field data is established. 5. According to the method of automatic mock number generation based on semantic recognition in claim 4, it is characterized in that the request interface to be generated configures interface request information and corresponding mock rules to form a mock rule library, including: Treat the request interface for number generation and obtain basic information of the interface; Configure corresponding mock rules according to the basic information of the interface; the mock rules include interface request information and response results; The configuration of the mock rule is as follows: first, configure the interface request information, which includes the interface name and the interface call path. The interface name is used to uniquely identify each request interface; the call path is used to receive the request and uniquely match the corresponding mock rule when initiating the interface call request; then configure the interface response result, which supports the json format string, and sets the mock response expected message format according to the predefined response format in the interface document. 6. According to the semantic recognition-based mock automatic number generation method of claim 5, it is characterized in that the basic information includes the interface name, the call address and the response result. 7. According to the semantic recognition-based mock automatic number generation method of claim 1, it is characterized in that the receiving interface call request of the requesting party obtains the call message information; according to the call message information, matches the corresponding mock rule in the mock rule library; according to the mock rule, determines the response message structure and the field value of the generated data to obtain the response message; and returns the response message to the requesting party to complete the response, including: Receive an external http interface call request from a requester that calls the mock platform, and obtain call message information according to the external http interface call request; According to the call message information, the call path in the request message information is obtained; according to the received request address, the corresponding mock rule in the mock rule library is matched, that is, according to the received request address, the interface call path configured in the mock rule library is matched to determine the interface corresponding to the request and its corresponding mock rule; According to the mock rules, determine the response information, that is, the response message structure; According to the basic fields configured in the response message structure, automatically generate field values of the data based on the semantic recognition model; and assemble the field values into the response message structure; In response to the request interface, the assembled mock response message object with generated data is returned to the requester. 8. A mock automatic number-generating device based on semantic recognition, characterized in that the device comprises: A basic field library forming unit is used to obtain basic field information of an interface, input the basic field information into a semantic recognition model, determine semantic similarity; generate field data based on the semantic similarity, and form a basic field library; The mock rule library configuration unit is used to configure the interface request information and corresponding mock rules for the request interface to form a mock rule library; The interface request and response unit is used to receive the interface call request of the requester and obtain the call message information; match the corresponding mock rules in the mock rule library according to the call message information; determine the response message structure and the field values of the generated data according to the mock rules to obtain the response message; and return the response message to the requester to complete the response. 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the mock automatic number generation method based on semantic recognition as described in any one of claims 1 to 7 is implemented. 10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a mock automatic number generation method based on semantic recognition as described in any one of claims 1 to 7.

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