CN113407450B - Interface testing method, device, equipment and medium based on parameter automatic identification - Google Patents
Interface testing method, device, equipment and medium based on parameter automatic identification Download PDFInfo
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Abstract
The invention discloses an interface testing method, device, equipment and medium based on parameter automatic identification, wherein the method comprises the following steps: capturing application program interface information generated after triggering the function node when a trigger instruction aiming at the function node in the system to be tested is detected; extracting request parameters in the application program interface information; identifying and outputting the type of the request parameter; identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model to output the types of the request parameters; parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program; and after the interface program is executed, a test case is obtained, the test case and the request parameters are bound to generate an interface to be tested, and the interface to be tested is executed to generate a test result. By adopting the method and the device, the testing efficiency of software testing can be improved.
Description
Technical Field
The present invention relates to the field of data consistency testing technologies, and in particular, to an interface testing method, device, equipment, and medium based on automatic parameter identification.
Background
The test of the application programming interface in the software test needs to be carried out according to the test flow of the conventional test, the boundary test, the abnormal test and the fault test, and each unit test of the test flow comprises a plurality of fine classifications, so that the interface test work is multi-dimensional, the test personnel needs to make analysis before the test, and test data preparation is carried out in advance so as to ensure more comprehensive coverage.
In the prior art, when an application programming interface in a software system code needs to be tested, the application programming interface needs to be converted into an executable interface test program capable of automatically performing overlay test, currently, when the application programming interface is converted, a test engineer builds the executable interface test program of each program programming interface in a parameter filling mode, as function nodes in the system are increasingly larger, the program programming interface needing to be built has explosive growth, and when the test engineer aims at the interface filling parameters to be tested, a great amount of time is required to build and perform parameterization work, so that the software test period is improved, and the test efficiency of the software test is reduced.
Disclosure of Invention
Based on this, it is necessary to provide an interface test method, device, equipment and medium based on automatic parameter identification, aiming at the problem of low security after the software system is on line.
An interface testing method based on parameter automatic identification, the method comprises: capturing application program interface information generated after triggering the function node when a trigger instruction aiming at the function node in the system to be tested is detected; the application program interface information comprises request parameters and request data; extracting request parameters in the application program interface information; identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model, and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program; and executing the parameterized interface program to obtain a test case, binding the test case with the request parameters to generate a to-be-tested interface, and executing the to-be-tested interface to generate a test result.
In one embodiment, when a trigger instruction for a functional node in a system to be tested is detected, capturing application program interface information generated after the functional node is triggered, including: connecting and accessing a system to be tested; when a trigger instruction aiming at a functional node in a system to be tested is detected, generating application program interface information according to the trigger instruction; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, when a trigger instruction for a functional node in a system to be tested is detected, capturing application program interface information generated after the functional node is triggered, including: connecting and accessing a system to be tested; receiving data information filled in aiming at a current page form in a system to be tested; when a submitting instruction aiming at the current page form is detected, generating application program interface information according to the filled data information; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, extracting the request parameters in the application program interface information includes: loading a preconfigured regular filter; inputting the data contained in the application program interface information into a regular filter one by one, and identifying the request parameters contained in the application program interface information; outputting request parameters; wherein generating a preconfigured regular filter comprises: collecting request parameters used in the field of software programming to generate a data dictionary; initializing a regular filter; and associating the data dictionary with the initialized regular filter to generate a preconfigured regular filter.
In one embodiment, identifying and outputting the type of request parameter includes: loading a parameter analyzer and a pre-trained parameter type recognition model; inputting the request parameters into a parameter analyzer, and outputting analysis results; outputting the type corresponding to the request parameter when the type corresponding to the request parameter exists in the analysis result; or when the type corresponding to the request parameter does not exist in the analysis result, inputting the request parameter into a pre-trained parameter type identification model; outputting the type corresponding to the request parameter.
In one embodiment, the pre-trained parameter type recognition model includes a presentation layer, a BiLSTM layer, and a CRF layer; inputting the requested parameters into a pre-trained parameter type recognition model, comprising: the presentation layer extracts the word vector of the request parameter, and generates a word vector set; the BiLSTM layer carries out label vectorization on each word vector in the word vector set to generate a label vector corresponding to each word vector; the CRF layer calculates a class value matrix of the label vector corresponding to each word vector; and determining the type of the request parameter according to the class value matrix of the label vector corresponding to each word vector.
In one embodiment, a pre-trained parameter type recognition model is generated according to the following method, including: creating a parameter type identification model by adopting a BiLSTM-CRF algorithm; acquiring request parameters used in the field of software programming to generate a first training sample; preprocessing and expanding the first training sample to generate a second training sample; receiving a statistical analysis instruction aiming at a first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction; inputting the first training sample and the second training sample into a parameter type identification model for training, and outputting a loss value of the model; when the loss value and training times of the model reach preset values, generating a trained parameter type identification model; and adding various constraint rules into the CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model.
An interface testing device based on parameter automatic identification, the device comprises: the application program interface information capturing module is used for capturing the application program interface information generated after the function node is triggered when the trigger instruction aiming at the function node in the system to be tested is detected; the application program interface information comprises request parameters and request data; the request parameter extraction module is used for extracting request parameters in the application program interface information; the type identification module is used for identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; the interface program generating module is used for generating a parameterization method according to the type of the request parameter, and generating a parameterized interface program after splicing the parameterization method and the request data; the test result generation module is used for obtaining a test case after executing the parameterized interface program, binding the test case with the request parameters to generate a to-be-tested interface, and executing the to-be-tested interface to generate a test result.
An apparatus comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the above-described method for testing an interface based on automatic identification of parameters.
A medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the above-described method of interface testing based on automatic identification of parameters.
According to the interface testing method, the device, the equipment and the medium based on the parameter automatic identification, when the interface testing device based on the parameter automatic identification detects a trigger instruction aiming at a functional node in a system to be tested, firstly, the application program interface information generated after triggering the functional node is captured, the request parameter in the application program interface information is extracted, then the type of the request parameter is identified and output, the type of the request parameter is identified according to a parameter analyzer and a pre-trained parameter type identification model, the pre-trained parameter type identification model is generated based on a first training sample and a second training sample, the parameterization method is generated according to the type of the request parameter, the parameterization method and the request data are spliced to generate a parameterized interface program, finally, the interface program is executed to obtain a test case, the test case and the request parameter are bound to generate a tested interface, and the test result is generated by executing the tested interface. The type of the request parameter contained in the application program interface information is identified by adopting the regular filter and the pre-trained parameter type identification model, so that the request parameter contained in the application program interface information can automatically complete parameterization, thereby reducing the system test period and reducing the test efficiency of software test. Meanwhile, as the correlation is established between the first training sample and the second training sample, the identification precision of the trained parameter type identification model is more accurate, and the accuracy of the system test is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a diagram of an implementation environment of an interface test method based on automatic identification of parameters provided in one embodiment of the present application;
FIG. 2 is a schematic diagram of the internal structure of the device according to one embodiment of the present application;
FIG. 3 is a method diagram of an interface testing method based on automatic parameter identification according to one embodiment of the present application;
FIG. 4 is a process schematic diagram of an interface testing process based on automatic identification of parameters provided in one embodiment of the present application;
FIG. 5 is a method schematic diagram of a parameter type recognition model training method provided in another embodiment of the present application;
fig. 6 is a schematic device diagram of an interface testing device based on automatic parameter identification according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element.
Fig. 1 is a diagram of an implementation environment of an interface testing method based on parameter automatic identification provided in one embodiment, as shown in fig. 1, in the implementation environment, including a device 110 and a client 120.
The device 110 may be a server device, such as a device that caches requested parameters in parameter analyzer or application program interface information, or a server device that is used to deploy a pre-trained parameter type recognition model. When an interface test based on automatic parameter identification is required, when a trigger instruction for a functional node in a system to be tested is detected by the client 120, capturing application program interface information generated after the functional node is triggered, extracting a request parameter in the application program interface information by the client 120, caching the request parameter in the application program interface information into the device 110, acquiring a pre-trained parameter type identification model from the device 110 by the client 120, identifying and outputting the type of the request parameter cached in the device 110 by the client 120 according to a parameter analyzer and the pre-trained parameter type identification model, parameterizing by the client 120 according to the type of the request parameter to generate a parameterized method, splicing the parameterized method with the request data to generate a parameterized interface program, executing the parameterized interface program by the client 120 to obtain a test case, binding the test case with the request parameter to generate a to-be-tested interface, and executing the to-be-tested interface to generate a test result.
It should be noted that, the client 120 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The device 110 and the client 120 may be connected by bluetooth, USB (Universal Serial Bus ) or other communication connection, which is not limited herein.
Fig. 2 is a schematic diagram of the internal structure of the apparatus in one embodiment. As shown in fig. 2, the device includes a processor, a medium, a memory, and a network interface connected by a system bus. The medium of the device stores an operating system, a database and a computer readable instruction, the database can store a control information sequence, and the computer readable instruction can enable the processor to realize an interface test method based on parameter automatic identification when being executed by the processor. The processor of the device is used to provide computing and control capabilities to support the operation of the entire device. The memory of the device may have stored therein computer readable instructions that, when executed by the processor, cause the processor to perform an interface test method based on automatic identification of parameters. The network interface of the device is used for communicating with the terminal connection. It will be appreciated by those skilled in the art that the structure shown in fig. 2 is merely a block diagram of a portion of the structure associated with the present application and does not constitute a limitation of the apparatus to which the present application is applied, and that a particular apparatus may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components. Wherein the medium is a readable storage medium.
The following describes in detail the interface testing method based on automatic parameter identification according to the embodiment of the present application with reference to fig. 3 to fig. 5. The method may be implemented in dependence on a computer program and may be run on an interface testing device based on automatic identification of parameters based on von neumann systems. The computer program may be integrated in the application or may run as a stand-alone tool class application.
Referring to fig. 3, a flow chart of an interface testing method based on automatic parameter identification is provided for an embodiment of the present application. As shown in fig. 3, the method of the embodiment of the present application may include the following steps:
s101, capturing application program interface information generated after triggering a function node when a trigger instruction aiming at the function node in a system to be tested is detected; the application program interface information comprises request parameters and request data;
the system to be tested is a software system to be tested, and can be a web system, a webpage of an H5 webpage type, or an application program app. The trigger instruction is a signal generated by the user by clicking the relevant function key of the system to be tested. The application program interface is API (Application Programming Interface) for short, which is a contract for the connection of different components of the software system. The request parameter is parameter information required by the API, for example, the parameter information may be: { "name": "Zhang San", "description": "xxx", "phone": "130 xxxxx", "Email": "xxx@xxx.com" }. The request data at least comprises a request path url, a header information header and a request method.
In this embodiment of the present application, the trigger instruction may be a signal generated by clicking a function node key in the system to be tested by the user, or may be a signal generated by inputting related form information in a form filling page of the system to be tested by the user, and finally triggering a submit key of the form filling page.
In one possible implementation manner, when an interface test is required, a system to be tested is connected and accessed first, then a user triggers a functional node in the system to be tested to generate an instruction, and the instruction is processed by a code logic layer to generate application program interface information according to the instruction, and the application program interface information is captured by a network capture tool.
In another possible implementation manner, when interface test is needed, a system to be tested is connected and accessed, then a user fills in form page information in the system to be tested, a trigger instruction is generated by clicking a submit button on a form after the form is filled in, application program interface information is generated after code logic layer processing is performed according to the instruction, and application program interface information is captured by adopting a capture tool through a network.
S102, extracting request parameters in application program interface information;
Generally, the application program interface information includes information such as request parameters, request paths url, header information headers, request methods, and the like.
In one possible implementation, when the request parameter is extracted, a preconfigured regular filter is loaded first, then data contained in the application program interface information is input into the regular filter one by one, whether the data contained in the application program interface information is the request parameter is judged, and if the data is the request parameter, the data is output.
Specifically, when a preconfigured regular filter is generated, firstly, request parameters used in the field of software programming are collected to generate a data dictionary, then the regular filter is initialized, and finally, the data dictionary is associated with the initialized regular filter to generate the preconfigured regular filter.
In another possible implementation manner, when extracting the request parameters, firstly, collecting the request parameters used in the software programming field to generate a request parameter corpus sample, constructing a word segmentation dictionary based on the request parameter corpus sample, then, generating a word segmentation result after segmenting the application program interface information according to the word segmentation dictionary, and finally, determining the word segmentation result as the request parameters to output. Because a large number of request parameters used in the field of software programming are specially used for constructing the word segmentation dictionary, the constructed word segmentation dictionary can classify the request parameters included in the application program interface information.
S103, identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model, and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value;
the type of the request parameter is a type set in software programming, for example, the type of data in java programming includes a character type, a boolean type and a value type, a byte type is char, a boolean type is bootean, and a value type is byte, short, int, long, float, double.
In the embodiment of the application, when the type of the request parameter is identified, a parameter analyzer and a pre-trained parameter type identification model are loaded first, then the request parameter is input into the parameter analyzer to obtain the corresponding parameter type through the parameter analyzer, and when the parameter analyzer cannot identify the parameter type, the request parameter is input into the pre-trained parameter type identification model to be identified, and the type of the request parameter is output.
Further, the pre-trained parameter type recognition model includes a presentation layer, a BiLSTM layer, and a CRF layer.
Specifically, when the type of the request parameter is output, the request parameter is input into a representation layer to output a word vector set, then each word vector in the word vector set is input into a BiLSTM layer to output a label vector corresponding to each word vector, then the label vector corresponding to each word vector is input into a CRF layer to output a category value matrix corresponding to the request parameter, and finally the type of the request parameter is determined according to the category value matrix.
For example, the extracted request parameters are subjected to a parameter analyzer and a pre-trained parameter type recognition model to obtain corresponding parameter value types, such as
The parameter "name": type "Zhang Sano" [ name ], "phone": 130xxxxx "[ cell phone number ],
"Email" type xxx@xxx.com "[ mailbox ];
specifically, when a pre-trained parameter type recognition model is generated, firstly, a parameter type recognition model is created by adopting a BiLSTM-CRF algorithm, then a first training sample is generated by acquiring request parameters used in the field of software programming, then a second training sample is generated by preprocessing and expanding the first training sample, then a statistical analysis instruction for the first training sample is received, a plurality of constraint rules are generated based on the statistical analysis instruction, the first training sample and the second training sample are input into the parameter type recognition model for training, a loss value of the model is output, when the loss value and training times reach preset values, a trained parameter type recognition model is generated, and finally a plurality of constraint rules are added into a CRF layer in the trained parameter type recognition model to generate the pre-trained parameter type recognition model.
Specifically, the first training sample and the second training sample are input into the parameter type recognition model for training, when the loss value of the model is output, the first training sample and the second training sample are input into the parameter type recognition model, the first loss value of the first training sample and the second loss value of the second training sample are output, and then the average value of the first loss value and the second loss value is determined as the loss value of the model.
S104, parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program;
in one possible implementation manner, after obtaining the type to which the request parameter belongs, the test database is called first, then an automatic parameterization of the type to which the request parameter belongs is performed according to the test database to generate a parameterized method, request data (for example, url/headers/http methods) included in the application program interface information in step S101 are obtained, and finally the parameterized method and the request data are combined to generate the parameterized interface program.
S105, the parameterized interface program is executed to obtain a test case, the test case and the request parameters are bound to generate an interface to be tested, and the interface to be tested is executed to generate a test result.
In one possible implementation manner, after the parameterized interface program is generated, when an interface trigger instruction for the parameterized interface program is received, the parameterized interface program is executed to obtain a test case from the test database, the obtained test case is bound with the request parameter to generate an interface to be tested, and finally the interface to be tested is executed to generate a test result.
Further, the test cases obtained from the test database can be obtained randomly or one by one in a mode of increasing the ID.
For example, the type [ name ] is automatically parameterized as "name": fake.name (), and by executing this parameterization method fake.name (), the test case can be obtained from the test database.
Further, after the parameterized interface program is generated, the execution times of the parameterized interface program input by the user can be received, and the parameterized interface program is circularly executed according to the execution times to obtain a plurality of test cases.
For example, as shown in fig. 4, fig. 4 is a schematic process diagram of an interface testing process based on automatic parameter identification provided in this embodiment of the present application, when the process diagram is applied in a form page filling scene, a form filling page of a system to be tested (the system to be tested may be web/H5/app) is accessed, form information is filled, an interface triggered when the system page to be tested is accessed is captured by a network capture tool, the captured interface information is used to obtain form request parameter information through a regular, each parameter is analyzed by a parameter analyzer to obtain a value type of the corresponding parameter, if the parameter analyzer cannot identify the parameter, the parameter is identified in an input model, automatic parameterization of the corresponding type is executed according to the type of the parameter, and a triggerable interface testing program is generated after the parameterized parameter and the request data of the interface are combined.
In the embodiment of the application, when a trigger instruction for a functional node in a system to be tested is detected, an interface testing device based on parameter automatic identification captures application program interface information generated after the functional node is triggered, extracts a request parameter in the application program interface information, identifies and outputs a type to which the request parameter belongs, wherein the type to which the request parameter belongs is identified according to a parameter analyzer and a pre-trained parameter type identification model, the pre-trained parameter type identification model is generated based on a first training sample and a second training sample, a parameterization method is generated according to the type to which the request parameter belongs, the parameterization method and the request data are spliced to generate a parameterized interface program, finally the interface program is executed to obtain a test case, the test case and the request parameter are bound to generate a to-be-tested interface, and the test result is generated by executing the to-be-tested interface. The type of the request parameter contained in the application program interface information is identified by adopting the regular filter and the pre-trained parameter type identification model, so that the request parameter contained in the application program interface information can automatically complete parameterization, thereby reducing the system test period and reducing the test efficiency of software test.
As shown in fig. 5, fig. 5 is a training method of a parameter type recognition model provided in the present application, including the following steps:
s201, a parameter type identification model is established by adopting a BiLSTM-CRF algorithm;
the CRF is a common sequence labeling algorithm and can be used for tasks such as part-of-speech labeling, word segmentation, named entity recognition and the like. BiLSTM+CRF is a popular sequence labeling algorithm at present, and BiLSTM and CRF are combined together, so that the model can consider the correlation between the front and the back of the sequence like CRF, and can also have the characteristic extraction and fitting capability of LSTM.
In general, biLSTM can predict the probability that each word belongs to a different label, and then use Softmax to obtain the label with the highest probability as the predicted value of the position. This ignores the association between tags at the time of prediction.
S202, acquiring request parameters used in the field of software programming to generate a first training sample;
s203, preprocessing and expanding the first training sample to generate a second training sample;
s204, receiving a statistical analysis instruction aiming at the first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction;
s205, inputting the first training sample and the second training sample into a parameter type identification model for training, and outputting a loss value of the model;
S206, when the loss value and training times of the model reach preset values, generating a trained parameter type identification model;
s207, adding various constraint rules into the CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model.
In the embodiment of the application, as the correlation is established between the first training sample and the second training sample, the identification precision of the trained parameter type identification model is more accurate, and the accuracy of the system test is improved.
The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.
Referring to fig. 6, a schematic structural diagram of an interface testing device based on automatic parameter identification according to an exemplary embodiment of the present invention is shown and applied to a server. The interface testing device based on automatic parameter identification can be realized into all or part of the equipment through software, hardware or a combination of the two. The device 1 comprises an application program interface information capturing module 10, a request parameter extracting module 20, a type identifying module 30, an interface program generating module 40 and a test result generating module 50.
The application program interface information capturing module 10 is configured to capture application program interface information generated after a function node is triggered when a trigger instruction for the function node in the system to be tested is detected; the application program interface information comprises request parameters and request data;
a request parameter extraction module 20, configured to extract a request parameter in the application program interface information;
a type recognition module 30, configured to recognize the request parameter according to a preset parameter analyzer and a pre-trained parameter type recognition model, and output a type of the request parameter; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value;
the interface program generating module 40 is configured to generate a parameterized method according to the type to which the request parameter belongs, and splice the parameterized method and the request data to generate a parameterized interface program;
the test result generating module 50 is configured to execute the parameterized interface program to obtain a test case, bind the test case with the request parameter to generate a to-be-tested interface, and execute the to-be-tested interface to generate a test result.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the embodiment of the application, when a trigger instruction for a functional node in a system to be tested is detected, an interface testing device based on parameter automatic identification captures application program interface information generated after the functional node is triggered, extracts a request parameter in the application program interface information, identifies and outputs a type to which the request parameter belongs, wherein the type to which the request parameter belongs is identified according to a parameter analyzer and a pre-trained parameter type identification model, the pre-trained parameter type identification model is generated based on a first training sample and a second training sample, a parameterization method is generated according to the type to which the request parameter belongs, the parameterization method and the request data are spliced to generate a parameterized interface program, finally the interface program is executed to obtain a test case, the test case and the request parameter are bound to generate a to-be-tested interface, and the test result is generated by executing the to-be-tested interface. The type of the request parameter contained in the application program interface information is identified by adopting the regular filter and the pre-trained parameter type identification model, so that the request parameter contained in the application program interface information can automatically complete parameterization, thereby reducing the system test period and reducing the test efficiency of software test.
In one embodiment, an apparatus is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of: capturing application program interface information generated after triggering the function node when a trigger instruction aiming at the function node in the system to be tested is detected; the application program interface information comprises request parameters and request data; extracting request parameters in the application program interface information; identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model, and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program; and executing the parameterized interface program to obtain a test case, binding the test case with the request parameters to generate a to-be-tested interface, and executing the to-be-tested interface to generate a test result.
In one embodiment, when the processor executes the trigger instruction for the functional node in the system to be tested, and captures the application program interface information generated after the functional node is triggered, the processor specifically executes the following operations: connecting and accessing a system to be tested; when a trigger instruction aiming at a functional node in a system to be tested is detected, generating application program interface information according to the trigger instruction; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, when the processor executes the trigger instruction for the functional node in the system to be tested, and captures the application program interface information generated after the functional node is triggered, the processor specifically executes the following operations: connecting and accessing a system to be tested; receiving data information filled in aiming at a current page form in a system to be tested; when a submitting instruction aiming at the current page form is detected, generating application program interface information according to the filled data information; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, the processor, when executing the request parameters in the extraction application program interface information, specifically performs the following operations: loading a preconfigured regular filter; inputting the data contained in the application program interface information into a regular filter one by one, and identifying the request parameters contained in the application program interface information; outputting request parameters; wherein generating a preconfigured regular filter comprises: collecting request parameters used in the field of software programming to generate a data dictionary; initializing a regular filter; and associating the data dictionary with the initialized regular filter to generate a preconfigured regular filter.
In one embodiment, when the processor identifies and outputs the type of the request parameter, the following operations are specifically performed: loading a parameter analyzer and a pre-trained parameter type recognition model; inputting the request parameters into a parameter analyzer, and outputting analysis results; outputting the type corresponding to the request parameter when the type corresponding to the request parameter exists in the analysis result; or when the type corresponding to the request parameter does not exist in the analysis result, inputting the request parameter into a pre-trained parameter type identification model; outputting the type corresponding to the request parameter.
In one embodiment, the processor, when executing the input of the request parameters into the pre-trained parameter type identification model, specifically performs the following operations: the presentation layer extracts the word vector of the request parameter, and generates a word vector set; the BiLSTM layer carries out label vectorization on each word vector in the word vector set to generate a label vector corresponding to each word vector; the CRF layer calculates a class value matrix of the label vector corresponding to each word vector; and determining the type of the request parameter according to the class value matrix of the label vector corresponding to each word vector.
In one embodiment, the processor generates a pre-trained parameter type recognition model according to the following method, specifically: creating a parameter type identification model by adopting a BiLSTM-CRF algorithm; acquiring request parameters used in the field of software programming to generate a first training sample; preprocessing and expanding the first training sample to generate a second training sample; receiving a statistical analysis instruction aiming at a first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction; inputting the first training sample and the second training sample into a parameter type identification model for training, and outputting a loss value of the model; when the loss value and training times of the model reach preset values, generating a trained parameter type identification model; and adding various constraint rules into the CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model.
In the embodiment of the application, when a trigger instruction for a functional node in a system to be tested is detected, an interface testing device based on parameter automatic identification captures application program interface information generated after the functional node is triggered, extracts a request parameter in the application program interface information, identifies and outputs a type to which the request parameter belongs, wherein the type to which the request parameter belongs is identified according to a parameter analyzer and a pre-trained parameter type identification model, the pre-trained parameter type identification model is generated based on a first training sample and a second training sample, a parameterization method is generated according to the type to which the request parameter belongs, the parameterization method and the request data are spliced to generate a parameterized interface program, finally the interface program is executed to obtain a test case, the test case and the request parameter are bound to generate a to-be-tested interface, and the test result is generated by executing the to-be-tested interface. The type of the request parameter contained in the application program interface information is identified by adopting the regular filter and the pre-trained parameter type identification model, so that the request parameter contained in the application program interface information can automatically complete parameterization, thereby reducing the system test period and reducing the test efficiency of software test. Meanwhile, as the correlation is established between the first training sample and the second training sample, the identification precision of the trained parameter type identification model is more accurate, and the accuracy of the system test is improved.
In one embodiment, a medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: capturing application program interface information generated after triggering the function node when a trigger instruction aiming at the function node in the system to be tested is detected; the application program interface information comprises request parameters and request data; extracting request parameters in the application program interface information; identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model, and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program; and executing the parameterized interface program to obtain a test case, binding the test case with the request parameters to generate a to-be-tested interface, and executing the to-be-tested interface to generate a test result.
In one embodiment, when the processor executes the trigger instruction for the functional node in the system to be tested, and captures the application program interface information generated after the functional node is triggered, the processor specifically executes the following operations: connecting and accessing a system to be tested; when a trigger instruction aiming at a functional node in a system to be tested is detected, generating application program interface information according to the trigger instruction; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, when the processor executes the trigger instruction for the functional node in the system to be tested, and captures the application program interface information generated after the functional node is triggered, the processor specifically executes the following operations: connecting and accessing a system to be tested; receiving data information filled in aiming at a current page form in a system to be tested; when a submitting instruction aiming at the current page form is detected, generating application program interface information according to the filled data information; and capturing the application program interface information by adopting a network packet capturing tool.
In one embodiment, the processor, when executing the request parameters in the extraction application program interface information, specifically performs the following operations: loading a preconfigured regular filter; inputting the data contained in the application program interface information into a regular filter one by one, and identifying the request parameters contained in the application program interface information; outputting request parameters; wherein generating a preconfigured regular filter comprises: collecting request parameters used in the field of software programming to generate a data dictionary; initializing a regular filter; and associating the data dictionary with the initialized regular filter to generate a preconfigured regular filter.
In one embodiment, when the processor identifies and outputs the type of the request parameter, the following operations are specifically performed: loading a parameter analyzer and a pre-trained parameter type recognition model; inputting the request parameters into a parameter analyzer, and outputting analysis results; outputting the type corresponding to the request parameter when the type corresponding to the request parameter exists in the analysis result; or when the type corresponding to the request parameter does not exist in the analysis result, inputting the request parameter into a pre-trained parameter type identification model; outputting the type corresponding to the request parameter.
In one embodiment, the processor, when executing the input of the request parameters into the pre-trained parameter type identification model, specifically performs the following operations: the presentation layer extracts the word vector of the request parameter, and generates a word vector set; the BiLSTM layer carries out label vectorization on each word vector in the word vector set to generate a label vector corresponding to each word vector; the CRF layer calculates a class value matrix of the label vector corresponding to each word vector; and determining the type of the request parameter according to the class value matrix of the label vector corresponding to each word vector.
In one embodiment, the processor generates a pre-trained parameter type recognition model according to the following method, specifically: creating a parameter type identification model by adopting a BiLSTM-CRF algorithm; acquiring request parameters used in the field of software programming to generate a first training sample; preprocessing and expanding the first training sample to generate a second training sample; receiving a statistical analysis instruction aiming at a first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction; inputting the first training sample and the second training sample into a parameter type identification model for training, and outputting a loss value of the model; when the loss value and training times of the model reach preset values, generating a trained parameter type identification model; and adding various constraint rules into the CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model.
In the embodiment of the application, when a trigger instruction for a functional node in a system to be tested is detected, an interface testing device based on parameter automatic identification captures application program interface information generated after the functional node is triggered, extracts a request parameter in the application program interface information, identifies and outputs a type to which the request parameter belongs, wherein the type to which the request parameter belongs is identified according to a parameter analyzer and a pre-trained parameter type identification model, the pre-trained parameter type identification model is generated based on a first training sample and a second training sample, a parameterization method is generated according to the type to which the request parameter belongs, the parameterization method and the request data are spliced to generate a parameterized interface program, finally the interface program is executed to obtain a test case, the test case and the request parameter are bound to generate a to-be-tested interface, and the test result is generated by executing the to-be-tested interface. The type of the request parameter contained in the application program interface information is identified by adopting the regular filter and the pre-trained parameter type identification model, so that the request parameter contained in the application program interface information can automatically complete parameterization, thereby reducing the system test period and reducing the test efficiency of software test. Meanwhile, as the correlation is established between the first training sample and the second training sample, the identification precision of the trained parameter type identification model is more accurate, and the accuracy of the system test is improved.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a computer readable medium, which when executed may comprise the steps of the embodiments of the methods described above. The medium may be a nonvolatile medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. An interface testing method based on automatic parameter identification, which is characterized by comprising the following steps:
capturing application program interface information generated after triggering a functional node in a system to be tested when detecting the triggering instruction aiming at the functional node; wherein the application program interface information comprises request parameters and request data;
extracting request parameters in the application program interface information;
identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model, and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; wherein,
the identifying and outputting the type of the request parameter includes:
loading a parameter analyzer and a pre-trained parameter type recognition model;
inputting the request parameters into the parameter analyzer, and outputting analysis results;
when the type corresponding to the request parameter does not exist in the analysis result, inputting the request parameter into a pre-trained parameter type identification model, and outputting the type corresponding to the request parameter;
The pre-trained parameter type recognition model comprises a representation layer, a BiLSTM layer and a CRF layer;
inputting the request parameters into a pre-trained parameter type identification model, and outputting the type corresponding to the request parameters, wherein the method comprises the following steps:
inputting the request parameters into a representation layer, outputting a word vector set, inputting each word vector in the word vector set into a BiLSTM layer, outputting a label vector corresponding to each word vector, inputting the label vector corresponding to each word vector into a CRF layer, outputting a category value matrix corresponding to the request parameters, and determining the type of the request parameters according to the category value matrix;
generating a pre-trained parameter type recognition model according to the following method, comprising:
creating a parameter type identification model by adopting a BiLSTM-CRF algorithm;
acquiring request parameters used in the field of software programming to generate a first training sample;
preprocessing and expanding the first training sample to generate a second training sample;
receiving a statistical analysis instruction for the first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction;
inputting the first training sample and the second training sample into the parameter type identification model for training, and outputting a loss value of the model;
When the loss value and training times of the model reach preset values, generating a trained parameter type identification model;
adding the constraint rules into a CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model;
parameterizing according to the type of the request parameter to generate a parameterized method, and splicing the parameterized method and the request data to generate a parameterized interface program;
and executing the parameterized interface program to obtain a test case, binding the test case with the request parameters to generate an interface to be tested, and executing the interface to be tested to generate a test result.
2. The method according to claim 1, wherein capturing the application program interface information generated after triggering the functional node when the triggering instruction for the functional node in the system to be tested is detected, comprises:
connecting and accessing a system to be tested;
when a trigger instruction aiming at a functional node in the system to be tested is detected, generating application program interface information according to the trigger instruction;
and capturing the application program interface information by adopting a network packet capturing tool.
3. The method according to claim 1, wherein capturing the application program interface information generated after triggering the functional node when the triggering instruction for the functional node in the system to be tested is detected, comprises:
connecting and accessing a system to be tested;
receiving data information filled in aiming at a current page form in the system to be tested;
when a submitting instruction aiming at the current page form is detected, generating application program interface information according to the filled data information;
and capturing the application program interface information by adopting a network packet capturing tool.
4. The method of claim 1, wherein extracting the request parameters in the application program interface information comprises:
loading a preconfigured regular filter;
inputting the data contained in the application program interface information into the regular filter one by one, and identifying the request parameters contained in the application program interface information;
outputting the request parameters; wherein,
generating a preconfigured canonical filter according to the steps comprising:
collecting request parameters used in the field of software programming to generate a data dictionary;
initializing a regular filter;
And associating the data dictionary with the initialized regular filter to generate a preconfigured regular filter.
5. The method according to claim 1, wherein the method further comprises:
and outputting the type corresponding to the request parameter when the type corresponding to the request parameter exists in the analysis result.
6. The method of claim 5, wherein said inputting the request parameters into a pre-trained parameter type recognition model comprises:
the presentation layer extracts the word vector of the request parameter and generates a word vector set;
the BiLSTM layer carries out label vectorization on each word vector in the word vector set to generate a label vector corresponding to each word vector;
the CRF layer calculates a class value matrix of the label vector corresponding to each word vector;
and determining the type of the request parameter according to the class value matrix of the label vector corresponding to each word vector.
7. An interface testing device based on automatic parameter identification, the device comprising:
the system comprises an application program interface information capturing module, a function node detection module and a function node detection module, wherein the application program interface information capturing module is used for capturing application program interface information generated after triggering the function node when a triggering instruction aiming at the function node in a system to be detected is detected; wherein the application program interface information comprises request parameters and request data;
The request parameter extraction module is used for extracting request parameters in the application program interface information;
the type identification module is used for identifying the request parameters according to a preset parameter analyzer and a pre-trained parameter type identification model and outputting the types of the request parameters; the pre-trained parameter type recognition model is generated based on training of a first training sample and a second training sample, and the similarity of the first training sample and the second training sample is larger than a preset value; wherein,
the identifying and outputting the type of the request parameter includes:
loading a parameter analyzer and a pre-trained parameter type recognition model;
inputting the request parameters into the parameter analyzer, and outputting analysis results;
when the type corresponding to the request parameter does not exist in the analysis result, inputting the request parameter into a pre-trained parameter type identification model, and outputting the type corresponding to the request parameter;
the pre-trained parameter type recognition model comprises a representation layer, a BiLSTM layer and a CRF layer;
inputting the request parameters into a pre-trained parameter type identification model, and outputting the type corresponding to the request parameters, wherein the method comprises the following steps:
Inputting the request parameters into a representation layer, outputting a word vector set, inputting each word vector in the word vector set into a BiLSTM layer, outputting a label vector corresponding to each word vector, inputting the label vector corresponding to each word vector into a CRF layer, outputting a category value matrix corresponding to the request parameters, and determining the type of the request parameters according to the category value matrix;
generating a pre-trained parameter type recognition model according to the following method, comprising:
creating a parameter type identification model by adopting a BiLSTM-CRF algorithm;
acquiring request parameters used in the field of software programming to generate a first training sample;
preprocessing and expanding the first training sample to generate a second training sample;
receiving a statistical analysis instruction for the first training sample, and generating a plurality of constraint rules based on the statistical analysis instruction;
inputting the first training sample and the second training sample into the parameter type identification model for training, and outputting a loss value of the model;
when the loss value and training times of the model reach preset values, generating a trained parameter type identification model;
adding the constraint rules into a CRF layer in the trained parameter type recognition model to generate a pre-trained parameter type recognition model;
The interface program generating module is used for generating a parameterization method according to the type of the request parameter, and generating a parameterized interface program after splicing the parameterization method and the request data;
and the test result generation module is used for obtaining a test case after executing the parameterized interface program, binding the test case with the request parameters to generate an interface to be tested, and executing the interface to be tested to generate a test result.
8. An electronic device comprising a memory and a processor, the memory having stored therein computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the parameter automatic identification based interface test method of any one of claims 1 to 6.
9. A medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the parameter auto-identification based interface test method of any one of claims 1 to 6.
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