CN117171056B - Test method and device based on automatic interface - Google Patents
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Abstract
The invention relates to the technical field of interface testing, in particular to a testing method and a testing device based on an automatic interface, comprising the following steps: receiving a test instruction based on an automatic interface, acquiring an interface signature according to the test instruction, converting the interface signature into an interface parameter, wherein the interface parameter consists of an input parameter, an output parameter and a return parameter, performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, determining a data volume of the interface parameter according to the relation function, dividing the data volume to obtain a group of regional parameters, further selecting test data from each group of regional parameters according to a preset test coverage rate, substituting the test data into a test model to generate a test result, performing verification on the test result to obtain an interface decision, and finally transmitting the interface decision to a management user to finish the test based on the automatic interface. The invention can find the fault problem of the service interface in time, thereby improving the fault detection efficiency of the interface.
Description
Technical Field
The present invention relates to the field of interface testing technologies, and in particular, to a testing method and apparatus based on an automated interface.
Background
Currently, many network service providers provide software applications through service interfaces, however, the service interfaces are susceptible to network attacks, such as maliciously tampering with data, due to their own nature, so that failure problems of the service interfaces are caused, and in order to prevent such failure problems, interface tests must be performed. The interface test is mainly to verify whether the interaction points between systems in the software application run normally or not through selected test data, so that the test based on the automatic interface is realized, the fault problem of the service interface can be found timely, and meanwhile, the fault detection efficiency of the interface is improved.
The conventional interface test method at present is to detect the normal operation of the inspection interface by simulating a large number of users to access the interface at the same time, but the simulation of the simultaneous access of the interface by a large number of users is mainly manual simulation, which is easy to cause huge data pressure and even cause paralysis of the system, thereby not detecting the service interface in time. Therefore, how to realize the test based on the automatic interface and discover the fault problem of the service interface in time is a technical problem which needs to be solved urgently.
Disclosure of Invention
The invention provides a testing method and device based on an automatic interface, which mainly aim to discover the fault problem of a service interface in time and improve the fault detection efficiency of the interface.
In order to achieve the above object, the present invention provides a test method based on an automation interface, including:
receiving a test instruction based on an automatic interface, and acquiring an interface signature according to the test instruction;
converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters;
performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, wherein the relation function describes the data relation among the input parameter, the output parameter and the return parameter;
determining the data volume of the interface parameter according to the relation function, dividing the data volume to obtainA component partition parameter;
randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model, and generating a test result;
and executing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on the automatic interface.
Optionally, the converting the interface signature into the interface parameter based on a preset algorithm includes:
Receiving the interface signature, and executing verification on the interface signature, wherein the interface signature comprises a digital signature, an electronic original document and a verification key, and the electronic original document records a preset algorithm of the digital signature;
after verification is successful, a preset algorithm of the digital signature is obtained through the electronic original text;
according to the preset algorithm, executing parameter extraction on the digital signature by using an OpenSSL tool to obtain signature parameters;
and converting the signature parameters into readable modes by using a text editor, and generating interface parameters.
Optionally, the receiving the interface signature, performing verification on the interface signature, includes:
determining the interface signature, decrypting the digital signature by using the verification key, and obtaining an execution digital digest after the decryption is successful;
executing a hash algorithm on the electronic original text, generating a hash value, and combining the hash values to obtain a comparison digital abstract;
comparing the execution digital abstract with an execution abstract result of a comparison digital abstract, and if the abstract results are consistent, verifying successfully;
if the summary results are inconsistent, the verification fails, and a recheck is performed on the interface signature.
Optionally, the performing data analysis on the input parameter, the output parameter, and the return parameter generates a relationship function, including:
Matching a knowledge relation library with a history parameter set, and performing collaborative training after the matching is successful to obtain a parameter training set, wherein the knowledge relation library is a set of all history parameter relations of the same type of interfaces, and the history parameter set is a combination of all history parameter data of the same type of interfaces;
training by using the parameter training set to obtain a supervision classifier, wherein the supervision classifier can accurately generate all possible data relations among input parameters, output parameters and return parameters;
inputting the input parameter, the output parameter and the return parameter into a supervision classifier to generateA function to be determined, ->The possible waiting functions are marked +.>Wherein->Representing input parameters->Representing output parameters->Representing a return parameter;
calculating the input parameter, the output parameter and the return parameter as the firstThe function probability of the possible pending function;
and after successful calculation, selecting a pending function with the maximum probability of the function, and marking the pending function as a relation function.
Optionally, the matching the knowledge relation library with the historical parameter set, and performing collaborative training after successful matching to obtain the parameter training set includes:
matching the knowledge relation base with the historical parameter set once to obtain a marked data set ；
Performing secondary matching on the knowledge relational database and the historical parameter set to obtain a nonstandard data set；
Will annotate the datasetPerforming division to obtain two splitting data sets of +.>、/>Nonstandard dataset +.>Performing division to obtain two split data sets of +.>、/>；
From the unlabeled datasetSelecting a preset data volume of +.>Is->The nonstandard data pool is +.>Performing division to obtain two view data pools of +.>、/>；
For a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
for a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
acquiring allData and->Confidence of each data in the individual data will +.>The highest confidence in the dataData addition->And (3) add->The highest confidence in the data +.>Data addition->；
From the slaveDelete the data added to the data set and from +.>Selecting data corresponding to the deleted quantity to be continuously filled +.>And obtaining a parameter training set after filling.
Optionally, the calculating the input parameter, the output parameter and the return parameter is the firstA functional probability of a possible pending function, comprising:
the input parameter, the output parameter and the return parameter are calculated according to the following formula Functional probability of a possible pending function:
；
wherein,representing the input parameter, output parameter and return parameter as +.>The functional probability of the possible pending function,/->Representing normalization factor->Expressed as +.>An exponential function of the base +.>Indicate->Possible pending functions->Weight of->Representing the number of species of the relationship function.
Optionally, the data volume of the interface parameter is determined according to the relation function, and the data volume is divided to obtainA composition partitioning parameter comprising:
determining the relation function, and identifying the byte sequence of the data content according to the data content of the interface parameter;
determining the data volume of interface parameters, dividing the data volume by using the byte order to obtain parameter fragments containing the same byte packet number, and sequentially marking and sequencing the parameter fragments as followsWherein each parameter fragment contains a number of byte packets of +.>；
Splitting each parameter fragment into a number of parameter fragmentsAccording to the mark sequence, exchanging the random byte packet in each parameter fragment with the random byte packet in the following parameter fragment;
updating the original parameter fragments to obtain partition parameters, wherein the number of fragments of the partition parameters is as follows 。
Optionally, the verifying the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on the automated interface, including:
determining a test target, acquiring a test result, and comparing the test result with the test target to obtain a comparison result;
generating an interface decision according to the comparison result, wherein the interface decision describes countermeasure behavior to be adopted for the test result;
converting interface decisions intoA decision file, and a data transmission system between interface decision and management users is started;
using the data transmission systemAnd sequentially sending the decision files to a management user to finish the test based on the automatic interface.
Optionally, the data transmission system is utilized to transmit the dataThe individual decision files are sequentially sent to the management user, comprising:
determining a data transmission system, wherein the data transmission system consists of a digital front end board, a high-speed transmission unit and upper machine software;
transmitting a transmission signal of the decision file, and sampling the transmission signal by using the digital front end board;
after successful sampling, framing the decision file to obtain decision data framing;
The decision data frame is sent to a high-speed transmission unit, and the decision data frame is summarized through the high-speed transmission unit to obtain a decision data framing;
and receiving a transmission instruction of the upper machine software, uploading the decision data framing to the upper machine software in real time according to the transmission instruction, and transmitting to a management user after successful uploading.
In order to achieve the above object, the present invention further provides a testing device based on an automation interface, including:
the receiving instruction module is used for receiving a test instruction based on an automatic interface and acquiring an interface signature according to the test instruction;
the data analysis module is used for converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters, and data analysis is performed on the input parameters, the output parameters and the return parameters to generate a relation function, and the relation function describes the data relation among the input parameters, the output parameters and the return parameters;
a parameter partitioning module for determining the data volume of the interface parameter according to the relation function, and partitioning the data volume to obtainA component partition parameter;
the test judging module is used for randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model to generate a test result, executing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on an automatic interface.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the test method based on the automatic interface.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-mentioned test method based on an automation interface.
In order to solve the background technical problem, the embodiment of the invention firstly receives a test instruction based on an automatic interface, acquires an interface signature according to the test instruction, converts the interface signature into an interface parameter based on a preset algorithm, wherein the interface parameter consists of an input parameter, an output parameter and a return parameter, performs data analysis on the input parameter, the output parameter and the return parameter to generate a relational function, wherein the relational function describes a data relationship among the input parameter, the output parameter and the return parameter, and is explained, in order to search the data relationship among the input parameter, the output parameter and the return parameter, the embodiment of the invention generates a parameter training set through matching to serve as a basis for accurately searching the data relationship subsequently, and performs training on the parameter training set to obtain a supervision classifier, and the aim is to acquire all possible data relationships, find out the relational function with the maximum probability and the most suitable relation function in all the data relationships by using the preset algorithm, and the aim is to improve the accuracy of selecting subsequent test data, and further determine the data volume of the interface parameter according to the relational function, and divide the data volume to obtain the data volume The method comprises the steps of dividing data of interface parameters to obtain parameter fragments with the same byte packet number, reducing data load in the transmission process, randomly selecting test data from each group of partition parameters according to preset test coverage rate, establishing a test model, and substituting the test data into the test modelAnd then generating a test result, performing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user, wherein the sending process can reduce the error rate by utilizing a high-speed data transmission system, and can timely feed back the test result to the management user so as to facilitate the management user to make a decision on a fault problem and finally finish the test based on an automatic interface.
Drawings
FIG. 1 is a flow chart of an automated interface-based testing method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an automatic interface-based testing apparatus according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device implementing the test method based on an automation interface according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
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.
The embodiment of the application provides a test method based on an automatic interface. The execution subject of the test method based on the automation interface includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the test method based on the automation interface may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of an automated interface-based testing method according to an embodiment of the invention is shown. In this embodiment, the test method based on the automation interface includes:
S1, receiving a test instruction based on an automatic interface, and acquiring an interface signature according to the test instruction.
It should be explained that more and more network service providers provide software applications in a manner of enabling service interfaces, however, the service interfaces are easily subject to network attacks, such as malicious tampering with data, due to their own properties, so as to cause failure problems of the service interfaces, so that implementing the test based on the automation interfaces is helpful for timely finding failure problems of the service interfaces, and meanwhile, the failure detection efficiency of the interfaces is improved.
In order to realize the test based on the automatic interface, the embodiment of the invention sets the interface signature on the service interface, thereby not only effectively protecting the safety of the interface, but also providing an operation basis for the subsequent test. The interface signature comprises a digital signature, an electronic original text and a verification key, wherein the digital signature is composed of a plurality of interface parameters, and the interface parameters are implicit with relation functions which are the conditions of mutual constraint of a plurality of data.
In addition, the test instruction in the embodiment of the invention is generally initiated by a tester of a software company. For example, xiao Li is a tester of software a, and since software a is ready to disclose a new social software, the test instruction of Li Faqi is small, so as to detect whether there is a fault in the service interface in time, and improve the fault detection efficiency.
S2, converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters.
In detail, the converting the interface signature into interface parameters based on a preset algorithm includes:
receiving the interface signature, and executing verification on the interface signature, wherein the interface signature comprises a digital signature, an electronic original document and a verification key, and the electronic original document records a preset algorithm of the digital signature;
after verification is successful, a preset algorithm of the digital signature is obtained through the electronic original text;
according to the preset algorithm, executing parameter extraction on the digital signature by using an OpenSSL tool to obtain signature parameters;
and converting the signature parameters into readable modes by using a text editor, and generating interface parameters.
It should be explained that, the preset algorithm of the digital signature is generally an RSA algorithm and a pkcs#1v1.5 format algorithm, so that the OpenSSL tool is used to perform parameter extraction on the digital signature, the obtained signature parameters are in binary format, and the text editor is required to convert the signature parameters into interface parameters in readable mode.
In detail, after the OpenSSL tool performs parameter extraction on the digital signature, the obtained signature parameters may be parameters such as expiration time, public key, private key, algorithm, and digest of the digital signature.
Further, the receiving the interface signature, performing verification on the interface signature, includes:
determining the interface signature, decrypting the digital signature by using the verification key, and obtaining an execution digital digest after the decryption is successful;
executing a hash algorithm on the electronic original text, generating a hash value, and combining the hash values to obtain a comparison digital abstract;
comparing the execution digital abstract with an execution abstract result of a comparison digital abstract, and if the abstract results are consistent, verifying successfully;
if the summary results are inconsistent, the verification fails, and a recheck is performed on the interface signature.
It can be appreciated that the principle of the embodiment of the present invention for performing verification of a port signature is: the interface signature is data to be verified, the specific data content comprises a digital signature, an electronic original document and a verification key, after the interface signature is received, the digital signature is firstly decrypted by the verification key, a first digital abstract is derived after decryption, then a hash algorithm is carried out on the electronic original document to obtain a second digital abstract, the two abstracts are compared, and if the results are the same, the verification is successful, otherwise, the two abstracts are invalid. In addition, the verification of the interface signature is carried out, so that the integrity of the interface signature can be confirmed, and whether the interface is attacked by the network or not and data maliciously tampered with can be timely detected.
And S3, performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, wherein the relation function describes the data relation among the input parameter, the output parameter and the return parameter.
In detail, the performing data analysis on the input parameter, the output parameter and the return parameter to generate a relationship function includes:
matching a knowledge relation library with a history parameter set, and performing collaborative training after the matching is successful to obtain a parameter training set, wherein the knowledge relation library is a set of all history parameter relations of the same type of interfaces, and the history parameter set is a combination of all history parameter data of the same type of interfaces;
training by using the parameter training set to obtain a supervision classifier, wherein the supervision classifier can accurately generate all possible data relations among input parameters, output parameters and return parameters;
inputting the input parameter, the output parameter and the return parameter into a supervision classifier to generateA function to be determined, ->The possible waiting functions are marked +.>Wherein->Representing input parameters->Representing output parameters->Representing a return parameter;
calculating the input parameter, the output parameter and the return parameter as the firstThe function probability of the possible pending function;
And after successful calculation, selecting a pending function with the maximum probability of the function, and marking the pending function as a relation function.
It should be explained that, in order to find the data relationship among the input parameter, the output parameter and the return parameter, the embodiment of the invention firstly combines the knowledge relationship library and the history parameter set to obtain the parameter training set, and the purpose is to construct an experience knowledge library as a knowledge base for accurately finding the data relationship subsequently; then training the parameter training set to obtain a supervision classifier, wherein the purpose of the supervision classifier is to acquire all possible data relations among the input parameters, the output parameters and the return parameters; and finally, finding out the most appropriate relation function with the maximum probability in all the data relations by using a preset algorithm.
Further, the matching the knowledge relation library with the historical parameter set, and performing collaborative training after successful matching to obtain a parameter training set includes:
matching the knowledge relation base with the historical parameter set once to obtain a marked data set；
Performing secondary matching on the knowledge relational database and the historical parameter set to obtain a nonstandard data set；
Will annotate the datasetPerforming division to obtain two splitting data sets of +.>、/>Nonstandard dataset +.>Performing division to obtain two split data sets of +. >、/>；
From the unlabeled datasetSelecting a preset data volume of +.>Is->The nonstandard data pool is +.>Performing division to obtain two view data pools of +.>、/>；
For a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
for a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
acquiring allData and->Confidence of each data in the individual data will +.>The highest confidence in the dataData addition->And (3) add->The highest confidence in the data +.>Data addition->；
From the slaveDelete the data added to the data set and from +.>Selecting data corresponding to the deleted quantity to be continuously filled +.>And obtaining a parameter training set after filling.
It will be appreciated that matching the knowledge relational library to the historical parameter set does not immediately result in a parameter training set, and therefore the matching process must be performed twice, wherein the first match is to combine data sets that are matched and fit together, labeled as labeled data setsThe second matching is to combine the data sets with completely non-fitting data matching, and mark the data sets as nonstandard data sets +.>After the matching is finished, in order to make the data of the parameter training set more accurate and full, the data is predicted and expanded according to the learning model, and finally the parameter training set is obtained.
Further, the calculation of the input parameter, the output parameter and the return parameter is the firstA functional probability of a possible pending function, comprising:
the input parameter, the output parameter and the return parameter are calculated according to the following formulaFunctional probability of a possible pending function:
；
wherein,representing the input parameter, output parameter and return parameter as +.>The functional probability of the possible pending function,/->Representing normalization factor->Expressed as +.>An exponential function of the base +.>Indicate->Possible pending functions->Weight of->Representing the number of species of the pending function.
S4, determining the data volume of the interface parameter according to the relation function, and dividing the data volume to obtainAnd (5) a component partition parameter.
It should be explained that, in order to select test data from the interface parameters later, data partitioning must be performed on the interface parameters, which has the effect that: firstly, the data volume is quite huge due to the existence of the relation function, and the data volume is divided to obtain parameter fragments with the same byte packet number, so that the data load in the transmission process can be reduced; secondly, the content of byte packets contained in each original parameter fragment is disturbed, malicious reading of external network attacks can be prevented, and the transmission safety of information is improved.
In detail, the data volume of the interface parameter is determined according to the relation function, and the data volume is divided to obtainA composition partitioning parameter comprising:
determining the relation function, and identifying the byte sequence of the data content according to the data content of the interface parameter;
determining the data volume of interface parameters, dividing the data volume by using the byte order to obtain parameter fragments containing the same byte packet number, and sequentially marking and sequencing the parameter fragments as followsWherein each parameter fragment contains a number of byte packets of +.>；
Splitting each parameter fragment into a number of parameter fragmentsAccording to the mark sequence, exchanging the random byte packet in each parameter fragment with the random byte packet in the following parameter fragment;
updating the original parameter fragments to obtain partition parameters, wherein the number of fragments of the partition parameters is as follows。
It can be understood that the design of partition parameters not only can effectively avoid the problem of overlarge test data volume caused by using all interface parameter tests, but also can effectively improve the pertinence of subsequent tests.
S5, randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, and substituting the test data into the test model to generate a test result.
It should be explained that the test coverage rate is set according to the test requirement of the interface to measure the integrity of the interface test, and the embodiment of the invention can select the representative data from each group of partition parameters as the test data in a random or custom mode according to the preset test coverage rate for the final test process.
In detail, the test model may include, but is not limited to: black box test model, white box test model, gray box test model, automated test model, random test model, pressure test model, etc. Specifically, the test model may be an IOTest test model, which may perform real-time non-invasive test on embedded software, support plug-in secondary development, and may satisfy test requirements of professional users. The IOTest test model is the prior art and will not be described herein.
And S6, performing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on the automatic interface.
In detail, the step of verifying the test result to obtain an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on an automated interface comprises the following steps:
Determining a test target, acquiring a test result, and comparing the test result with the test target to obtain a comparison result;
generating an interface decision according to the comparison result, wherein the interface decision describes countermeasure behavior to be adopted for the test result;
converting interface decisions intoA decision file, and a data transmission system between interface decision and management users is started;
using the data transmission systemAnd sequentially sending the decision files to a management user to finish the test based on the automatic interface.
It can be understood that the data acquisition system set in the embodiment of the method fully considers the data transmission rate and the possible error rate in the process of transmitting the interface decision to the management user, the data transmission efficiency is purposefully improved, the error rate is effectively reduced, and the acquired data can be quickly and accurately reconstructed, so that the data acquisition system is suitable for the data transmission process with higher requirements on the sampling rate and the transmission distance.
In detail, the interface decision may include: interface release decisions, interface optimization decisions, interface security decisions, interface version management decisions, interface document update decisions, interface regression test decisions and the like, wherein the specific interface decisions depend on the purpose of interface test, comparison results and the like.
Further, the data transmission system is utilized to transmit the dataThe individual decision files are sequentially sent to the management user, comprising:
determining a data transmission system, wherein the data transmission system consists of a digital front end board, a high-speed transmission unit and upper machine software;
transmitting a transmission signal of the decision file, and sampling the transmission signal by using the digital front end board;
after successful sampling, framing the decision file to obtain decision data framing;
the decision data frame is sent to a high-speed transmission unit, and the decision data frame is summarized through the high-speed transmission unit to obtain a decision data framing;
and receiving a transmission instruction of the upper machine software, uploading the decision data framing to the upper machine software in real time according to the transmission instruction, and transmitting to a management user after successful uploading.
In order to solve the background technical problem, the embodiment of the invention firstly receives a test instruction based on an automatic interface, acquires an interface signature according to the test instruction, and converts the interface signature into an interface parameter based on a preset algorithm, wherein the interface parameter is composed of an input parameter, an output parameter and an interface parameterThe method comprises the steps of returning parameter composition, carrying out data analysis on input parameters, output parameters and return parameters to generate a relation function, wherein the relation function describes the data relation among the input parameters, the output parameters and the return parameters, and is to be interpreted, in order to seek the data relation among the input parameters, the output parameters and the return parameters, the embodiment of the invention generates a parameter training set through matching, which is used as a basis for accurately seeking the data relation subsequently, carries out training on the parameter training set to obtain a supervision classifier, and aims at acquiring all possible data relations, and finds out the relation function with the maximum probability and the most suitable relation function in all the data relations by utilizing a preset algorithm, so as to improve the accuracy of the selection of subsequent test data, further, determines the data volume of interface parameters according to the relation function, and divides the data volume to obtain The method comprises the steps of dividing data of the interface parameters to obtain parameter fragments with the same byte packet number, randomly selecting test data from each group of the partition parameters according to preset test coverage rate, establishing a test model, substituting the test data into the test model to generate a test result, verifying the test result to obtain an interface decision, and sending the interface decision to a management user.
FIG. 2 is a functional block diagram of an automated interface-based test apparatus according to an embodiment of the present invention.
The test device 100 based on the automated interface of the present invention may be installed in an electronic device. Depending on the functions implemented, the automation interface based test device 100 may include a receive instruction module 101, a data analysis module 102, a parameter partitioning module 103, and a test decision module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The receiving instruction module 101 is configured to receive a test instruction based on an automation interface, and obtain an interface signature according to the test instruction;
the data analysis module 102 is configured to convert the interface signature into an interface parameter based on a preset algorithm, where the interface parameter is composed of an input parameter, an output parameter and a return parameter, perform data analysis on the input parameter, the output parameter and the return parameter, and generate a relationship function, where the relationship function describes a data relationship among the input parameter, the output parameter and the return parameter;
the parameter partitioning module 103 is configured to determine a data volume of the interface parameter according to the relation function, and partition the data volume to obtainA component partition parameter;
the test decision module 104 is configured to randomly select test data from each group of partition parameters according to a preset test coverage rate, establish a test model, substitute the test data into the test model to generate a test result, perform verification on the test result, obtain an interface decision after successful verification, and send the interface decision to a management user to complete an automated interface-based test.
In detail, the modules in the testing device 100 based on an automated interface in the embodiment of the present invention use the same technical means as the testing method based on an automated interface described in fig. 1, and can generate the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device for implementing an automated interface-based testing method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus 12, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a test method program based on an automation interface.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard) or the like, provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as code of a test method program based on an automation interface, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CentralProcessingunit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 10 is a control unit (control unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., an automated interface-based test method program, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light-emitting diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The test method program based on an automation interface stored in the memory 11 of the electronic device 1 is a combination of instructions, which when run in the processor 10, can implement:
receiving a test instruction based on an automatic interface, and acquiring an interface signature according to the test instruction;
converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters;
Performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, wherein the relation function describes the data relation among the input parameter, the output parameter and the return parameter;
determining the data volume of the interface parameter according to the relation function, dividing the data volume to obtainA component partition parameter;
randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model, and generating a test result;
and executing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on the automatic interface.
Specifically, the specific implementation method of the above instructions by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 3, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
receiving a test instruction based on an automatic interface, and acquiring an interface signature according to the test instruction;
converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters;
performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, wherein the relation function describes the data relation among the input parameter, the output parameter and the return parameter;
determining the data volume of the interface parameter according to the relation function, dividing the data volume to obtainA component partition parameter;
randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model, and generating a test result;
and executing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on the automatic interface.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (4)
1. A test method based on an automation interface, the method comprising:
Receiving a test instruction based on an automatic interface, and acquiring an interface signature according to the test instruction;
converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters;
performing data analysis on the input parameter, the output parameter and the return parameter to generate a relation function, wherein the relation function describes the data relation among the input parameter, the output parameter and the return parameter;
determining the data volume of the interface parameter according to the relation function, dividing the data volume to obtainA component partition parameter;
randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model, and generating a test result;
performing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on an automatic interface;
the data analysis is performed on the input parameter, the output parameter and the return parameter to generate a relation function, which comprises the following steps:
matching a knowledge relation library with a history parameter set, and performing collaborative training after the matching is successful to obtain a parameter training set, wherein the knowledge relation library is a set of all history parameter relations of the same type of interfaces, and the history parameter set is a combination of all history parameter data of the same type of interfaces;
Training by using the parameter training set to obtain a supervision classifier, wherein the supervision classifier can accurately generate all possible data relations among input parameters, output parameters and return parameters;
inputting the input parameter, the output parameter and the return parameter into a supervision classifier to generateA function to be determined, ->The possible waiting functions are marked +.>Wherein->Representing input parameters->Representing output parameters->Representing a return parameter;
calculating the input parameter, the output parameter and the return parameter as the firstThe function probability of the possible pending function;
after successful calculation, selecting a pending function with the maximum probability of the function, and marking the pending function as a relation function;
the matching of the knowledge relation library and the historical parameter set, and the cooperative training after successful matching, to obtain the parameter training set, comprises the following steps:
matching the knowledge relation base with the historical parameter set once to obtain a marked data set；
Performing secondary matching on the knowledge relational library and the historical parameter set to obtainTo untagged data sets；
Will annotate the datasetPerforming division to obtain two splitting data sets of +.>、/>Nonstandard dataset +.>Performing division to obtain two split data sets of +. >、/>；
From the unlabeled datasetSelecting a preset data volume of +.>Is->Pooling the untagged dataPerforming division to obtain two view data pools of +.>、/>；
For a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
for a pair ofTraining is performed to obtain a learning model->And use +.>For->Predicting to obtain->Data;
acquiring allData and->Confidence of each data in the individual data will +.>The highest confidence in the data +.>Data addition->And (3) add->The highest confidence in the data +.>Data addition->；
From the slaveDelete the data added to the data set and from +.>Selecting data corresponding to the deleted quantity to be continuously filled +.>Obtaining a parameter training set after filling;
the input parameter, the output parameter and the return parameter are calculated as the firstA functional probability of a possible pending function, comprising:
the input parameter, the output parameter and the return parameter are calculated according to the following formulaFunctional probability of a possible pending function:
；
wherein,representing the input parameter, output parameter and return parameter as +.>The functional probability of the possible pending function,/->Representing normalization factor- >Expressed as +.>An exponential function of the base +.>Indicate->Possible pending functions->Weight of->Representing the number of species of the function to be determined;
determining the data volume of the interface parameter according to the relation function, and dividing the data volume to obtainA composition partitioning parameter comprising:
determining the relation function, and identifying the byte sequence of the data content according to the data content of the interface parameter;
determining the data volume of interface parameters, dividing the data volume by using the byte order to obtain parameter fragments containing the same byte packet number, and sequentially marking and sequencing the parameter fragments as followsWherein each parameter fragment contains a number of byte packets of +.>；
Splitting each parameter fragment into a number of parameter fragmentsAccording to the mark sequence, exchanging the random byte packet in each parameter fragment with the random byte packet in the following parameter fragment;
updating the original parameter fragments to obtain partition parameters, wherein the number of fragments of the partition parameters is as follows；
And performing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on an automatic interface, wherein the method comprises the following steps of:
Determining a test target, acquiring a test result, and comparing the test result with the test target to obtain a comparison result;
generating an interface decision according to the comparison result, wherein the interface decision describes countermeasure behavior to be adopted for the test result;
converting interface decisions intoA decision file, and a data transmission system between interface decision and management users is started;
using the data transmission systemSequentially sending the individual decision files to a management user to finish the test based on the automatic interface;
the data transmission system is utilized to transmit dataThe individual decision files are sequentially sent to the management user, comprising:
determining a data transmission system, wherein the data transmission system consists of a digital front end board, a high-speed transmission unit and upper machine software;
transmitting a transmission signal of the decision file, and sampling the transmission signal by using the digital front end board;
after successful sampling, framing the decision file to obtain decision data framing;
the decision data frame is sent to a high-speed transmission unit, and the decision data frame is summarized through the high-speed transmission unit to obtain a decision data framing;
and receiving a transmission instruction of the upper machine software, uploading the decision data framing to the upper machine software in real time according to the transmission instruction, and transmitting to a management user after successful uploading.
2. The automated interface-based testing method of claim 1, wherein converting the interface signature into interface parameters based on a preset algorithm comprises:
receiving the interface signature, and executing verification on the interface signature, wherein the interface signature comprises a digital signature, an electronic original document and a verification key, and the electronic original document records a preset algorithm of the digital signature;
after verification is successful, a preset algorithm of the digital signature is obtained through the electronic original text;
according to the preset algorithm, executing parameter extraction on the digital signature by using an OpenSSL tool to obtain signature parameters;
and converting the signature parameters into readable modes by using a text editor, and generating interface parameters.
3. The automated interface-based testing method of claim 2, wherein the receiving the interface signature, performing verification on the interface signature, comprises:
determining the interface signature, decrypting the digital signature by using the verification key, and obtaining an execution digital digest after the decryption is successful;
executing a hash algorithm on the electronic original text, generating a hash value, and combining the hash values to obtain a comparison digital abstract;
comparing the execution digital abstract with an execution abstract result of a comparison digital abstract, and if the abstract results are consistent, verifying successfully;
If the summary results are inconsistent, the verification fails, and a recheck is performed on the interface signature.
4. An automation interface based testing apparatus for implementing an automation interface based testing method according to any of claims 1-3, the apparatus comprising:
the receiving instruction module is used for receiving a test instruction based on an automatic interface and acquiring an interface signature according to the test instruction;
the data analysis module is used for converting the interface signature into interface parameters based on a preset algorithm, wherein the interface parameters consist of input parameters, output parameters and return parameters, and data analysis is performed on the input parameters, the output parameters and the return parameters to generate a relation function, and the relation function describes the data relation among the input parameters, the output parameters and the return parameters;
a parameter partitioning module for determining the data volume of the interface parameter according to the relation function, and partitioning the data volume to obtainA component partition parameter;
the test judging module is used for randomly selecting test data from each group of partition parameters according to the preset test coverage rate, establishing a test model, substituting the test data into the test model to generate a test result, executing verification on the test result, obtaining an interface decision after successful verification, and sending the interface decision to a management user to complete the test based on an automatic interface.
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Denomination of invention: Testing methods and devices based on automated interfaces Granted publication date: 20240109 Pledgee: Guotou Taikang Trust Co.,Ltd. Pledgor: Green City Technology Industry Service Group Co.,Ltd. Registration number: Y2024980011350 |