CN113760713A

CN113760713A - Test method, system, computer system and medium

Info

Publication number: CN113760713A
Application number: CN202011167413.0A
Authority: CN
Inventors: 吕梦圆
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2020-10-27
Filing date: 2020-10-27
Publication date: 2021-12-07
Anticipated expiration: 2040-10-27
Also published as: CN113760713B

Abstract

The present disclosure provides a test method, comprising: responding to a test instruction, and acquiring an actual operation link, wherein the actual operation link comprises N user behaviors which are actually operated in M user behaviors of a tested system and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N; determining a prediction operation link based on an actual operation link, wherein the prediction operation link comprises P user behaviors expected to be operated in M user behaviors of a system to be tested and an expected operation process of the P user behaviors, P is a positive integer, and M is larger than or equal to P; and generating and executing test cases based on the prediction operation link to respond to the test instruction, wherein the test cases comprise P test sub-cases, and the P test sub-cases correspond to P user behaviors one by one. In addition, the present disclosure also provides a test system, a computer system and a computer readable medium.

Description

Test method, system, computer system and medium

Technical Field

The present disclosure relates to the field of automated testing, and more particularly, to a testing method and system, a computer system, and a medium thereof.

Background

The test is an important means for ensuring the quality of the system, and the system test can find the system defects so as to modify the defects and achieve the aim of improving the quality level of the system. With the increasing scale of the system, the requirements for the refinement of the test and the test efficiency are higher and higher, so that the automatic test technology comes along.

The related art provides some automatic testing methods, such as the Selenium-based Web page automatic testing technology, and a tester can realize automatic testing by means of script recording and playback. However, the test cases are artificially determined by experience, which causes a problem of low fitting degree between the test scene and the actual use scene of the user.

Disclosure of Invention

In view of this, in order to overcome the problem that the fitting degree of the test scenario and the actual use scenario of the user is low due to the fact that the test case is determined empirically and manually in the related art, the present disclosure provides a test method and a system thereof, a computer system, and a computer readable medium.

One aspect of the present disclosure provides a test method, including: and responding to the test instruction, and acquiring an actual operation link, wherein the actual operation link comprises N user behaviors which are actually operated in M user behaviors of the tested system and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N. And determining a predicted operation link based on the actual operation link, wherein the predicted operation link comprises P user behaviors expected to be operated in M user behaviors of the system to be tested and an expected operation flow of the P user behaviors, P is a positive integer, and M is larger than or equal to P. And generating and executing a test case based on the prediction operation link to respond to the test instruction, wherein the test case comprises P test sub-cases, and the P test sub-cases correspond to the P user behaviors one by one.

According to an embodiment of the present disclosure, the test instruction includes a user characteristic of a specified user, and the obtaining the actual operation link in response to the test instruction includes: and responding to a test instruction, and acquiring Q user behaviors which are actually executed and operated in the M user behaviors of the tested system and an actual operation process of the Q user behaviors, wherein Q is a positive integer, and M is more than or equal to Q. And determining a reference user based on the user characteristics of the specified user, wherein the reference user and the specified user have the same and/or similar user characteristics. And screening out N user behaviors which are executed by the reference user from the Q user behaviors which are actually executed. And screening out the actual operation flows corresponding to the N user behaviors from the actual operation flows of the Q user behaviors.

According to an embodiment of the present disclosure, the determining the predicted operation link based on the actual operation link includes: and constructing an initial transition matrix based on the N user behaviors of the actual operation link, wherein the transition matrix is used for representing the transition probability from the initial user behavior to the end user behavior in the N user behaviors. And counting the transition probability values among the N user behaviors based on the actual operation process of the actual operation link. And filling the transition probability value into the initial transition matrix of the initial transition matrix to obtain a final transition matrix. And determining a prediction operation link based on the final transition matrix.

According to an embodiment of the present disclosure, the determining the predicted operation link of the designated user based on the final transition matrix includes: and obtaining a proposed prediction operation link based on the final transition matrix. And acquiring a preset threshold value of the transfer probability value. And determining the operation link meeting the preset threshold value in the proposed prediction operation links as the prediction operation link.

According to an embodiment of the present disclosure, the method further includes: and generating test sub-cases corresponding to each user behavior in advance aiming at the P user behaviors of the tested system so as to obtain the P test sub-cases.

According to an embodiment of the present disclosure, the generating and executing a test case based on the predicted operation link includes: and calling P test sub-cases corresponding to the P user behaviors expected to be operated. And arranging the P test sub-cases according to the predicted operation flows of the P user behaviors to generate and execute the test cases.

One aspect of the present disclosure provides a test system, comprising: the obtaining module is used for responding to a test instruction and obtaining an actual operation link, wherein the actual operation link comprises N user behaviors which are actually operated in M user behaviors of a tested system and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N. And the determining module is used for determining a predicted operation link based on the actual operation link, wherein the predicted operation link comprises P user behaviors expected to be operated in the M user behaviors of the tested system and an expected operation flow of the P user behaviors, P is a positive integer, and M is larger than or equal to P. And the test module is used for generating and executing a test case based on the prediction operation link so as to respond to the test instruction, wherein the test case comprises P test sub-cases, and the P test sub-cases are in one-to-one correspondence with the P user behaviors.

According to an embodiment of the present disclosure, the above system further includes: and the generating module is used for generating test sub-cases corresponding to each user behavior in advance aiming at the P user behaviors of the tested system so as to obtain the P test sub-cases.

Another aspect of the present disclosure provides a computer system comprising: one or more processors; a storage device to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of the above.

Another aspect of the disclosure provides a computer-readable medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method of any of the above.

Through the embodiment of the disclosure, the predicted operation link is determined according to the user behavior which is actually operated in the user behaviors of the system to be tested and the actual operation flow of the operated user behavior, and the predicted operation flow based on the user behavior which is predicted to be operated in the user behaviors of the system to be tested and the predicted operation flow of the user behavior which is predicted to be operated is determined. Since the predicted operational link is predicted from the actual operational link, it is not determined empirically by a human. Therefore, according to the test case generated by the prediction operation link, the subjectivity of the generation of the test case determined by human is avoided, the test scene and the actual use scene have natural high fitting degree, the technical problem that the fitting degree of the test scene and the actual use scene of a user is low due to the fact that the generation of the test case is determined by human in the related technology can be at least partially solved, and the technical effect of improving the fitting degree of the test scene and the actual use scene can be achieved.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an exemplary system architecture to which the testing method and system of embodiments of the present disclosure may be applied;

FIG. 2 schematically illustrates a flow diagram of a testing method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow diagram of a testing method according to another embodiment of the present disclosure;

FIG. 4 schematically shows a block diagram of a test system according to an embodiment of the present disclosure; and

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the testing method and system thereof, according to an embodiment of the present disclosure.

In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

It should be noted that the figures are not drawn to scale and that elements of similar structure or function are generally represented by like reference numerals throughout the figures for illustrative purposes.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".

In an automatic test scene, the quality of a test case is high and low, and the quality of a test effect is directly related. The selection of the test cases in the related technology is artificially determined by experience, which often results in low fitting degree of the test scene and the actual use scene of the user.

Based on the above, the present disclosure provides a test method, which includes a prediction phase of a test case and a generation and execution phase of the test case. In the prediction process of the test case, responding to a test instruction, and acquiring an actual operation link, wherein the actual operation link comprises N user behaviors which are actually operated in M user behaviors of the tested system and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N. And determining a predicted operation link based on the actual operation link, wherein the predicted operation link comprises P user behaviors which are predicted to be operated in M user behaviors of the system to be tested and a predicted operation flow of the P user behaviors, P is a positive integer, and M is larger than or equal to P. And in the generation and execution stage of the test cases, generating and executing the test cases based on the obtained prediction operation link so as to respond to the test instruction, wherein the test cases comprise P test sub-cases, and the P test sub-cases are in one-to-one correspondence with P user behaviors.

And determining a predicted operation link according to the actually operated user behavior in the user behaviors of the tested system and the actual operation flow of the operated user behavior, and predicting the user behavior to be operated in the user behaviors of the tested system and the predicted operation flow of the user behavior to be operated. Since the predicted operational link is predicted from the actual operational link, it is not determined empirically by a human. Therefore, according to the test case generated by the prediction operation link, the subjectivity of the generation of the test case determined by human is avoided, the test scene and the actual use scene have natural high fitting degree, the technical problem that the fitting degree of the test scene and the actual use scene of a user is low due to the fact that the generation of the test case is determined by human in the related technology can be at least partially solved, and the technical effect of improving the fitting degree of the test scene and the actual use scene can be achieved.

Fig. 1 schematically illustrates an exemplary system architecture 100 to which the testing method and system of embodiments of the present disclosure may be applied. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the test method provided by the present disclosure may be generally executed by the

terminal device

101, 102, or 103. Accordingly, the test system provided by the present disclosure may be generally disposed in the

terminal device

101, 102, or 103. The test method provided by the present disclosure may also be performed by other terminal devices than the

terminal device

101, 102 or 103 and capable of communicating with the

terminal device

101, 102, 103 and/or the server 105. Accordingly, the test system provided by the present disclosure may also be provided in other terminal devices different from the

terminal device

101, 102, or 103 and capable of communicating with the

terminal device

101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Fig. 2 schematically shows a flow chart of a testing method according to an embodiment of the present disclosure.

As shown in fig. 2, the method may include operations S210 to S230.

In operation S210, an actual operation link is acquired in response to the test instruction.

According to the embodiment of the disclosure, the test instruction is used for instructing to execute the test operation on the system under test. The system under test may be a Web application system and the present disclosure may be directed to software testing automation implemented for Web applications. The tested system corresponds to M user behaviors, and the M user behaviors can be obtained by dividing according to the service characteristics of the tested system. It can be understood that M user behaviors matched with the tested system can be obtained according to different service characteristics corresponding to the tested system. The test of the Web application is specifically a test for each Web page, and accordingly, the user behavior refers to a page element that can be clicked and operated by a user on the Web page, and the operation of the user on the page element represents the user behavior.

In specific implementation, the system to be tested may be primarily divided according to the service modules to obtain a plurality of service modules, and then divided according to the user behavior with the finest granularity for each of the plurality of service modules to obtain the user behavior corresponding to each service module. Optionally, an operation link of the user behavior may also be determined, where the operation link is a representation of a link relationship, and is used to represent a front-back flow between the user behaviors, that is, a front-back operation sequence between the user behaviors. For example, the system under test is divided into 3 service modules, which are respectively a service module 1, a service module 2 and a service module 3. For the service module 1, according to the user behavior with the finest granularity, the user behavior obtained after refinement may include, but is not limited to, a, b, c, d, e, f, g, and correspondingly, the operation link may include, but is not limited to, a-c-d-f-g, b-c-d-e-f-g. The division of the user behavior of the service module 2 and the service module 3 can be realized with reference to the division method for the user behavior of the service module 1. The user behaviors obtained by respectively dividing the 3 service modules are M user behaviors matched with the tested system.

It should be noted that, for convenience of description, the user behaviors are replaced by the letters a, b, c, d, e, f and g, and the user behaviors replaced by the letters a, b, c, d, e, f and g may represent different meanings according to the service characteristics of the actual system to be tested. For example, a may represent searching for items, b may represent viewing items, c may represent entering an item rating page, d may represent adding items to a shopping cart, e represents canceling orders, f represents direct purchases, and g represents submitting orders.

According to the embodiment of the disclosure, the actual operation link comprises N user behaviors which are actually operated in M user behaviors of the system to be tested and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N. In the present disclosure, this may be obtained from historical operational behavior data for the system under test. Corresponding to the Web page test, the actual operation link is the page elements which are actually clicked by the user, and the click sequence among the page elements.

In specific implementation, the actual operation behavior data of the system to be tested can be acquired in a point burying mode of the system to be tested or an online flow recording mode, and then the actual operation behavior data is converted into an operation flow link set according to M user behaviors matched with the system to be tested. Optionally, according to the actual requirement of the test, the actual operation behavior data for the tested system in the preset time period can be acquired, so that the test resource can be saved while the test requirement is met and the actual operation link is acquired more specifically.

In operation S220, a predicted operation link is determined based on the actual operation link.

According to the embodiment of the disclosure, the predicted operation link comprises P user behaviors expected to be operated in M user behaviors of the system to be tested and a predicted operation flow of the P user behaviors, P is a positive integer, and M is larger than or equal to P. Corresponding to the Web page test, the predicted operation link is the page elements which are predicted to be clicked by the user and the click sequence among the page elements.

It should be noted that the number of user behaviors obtained by performing user behavior granularity division on the system under test is M, the number of actually operated user behaviors is N, and the number of user behaviors to be expected to be operated is P. Therefore, N and P are both positive integers not greater than M, but N and P are not limited in size, i.e., N may be greater than P, N may also be equal to P, and N may also be equal to or less than P.

In operation S230, a test case is generated and executed based on the predicted operation link in response to the test instruction.

According to the embodiment of the disclosure, random generation and automatic execution of the test cases can be realized, namely the test cases are generated and executed simultaneously. The P test sub-cases correspond to the P user behaviors one by one, and the test cases comprising the P test sub-cases can be generated on the basis of the prediction operation link comprising the P user behaviors, wherein one user behavior corresponds to one test sub-case. One prediction operation link corresponds to one test case.

Considering that the number of users operating the system to be tested is large, different users have different operation flows for the operation behaviors of the system to be tested, the operation flows directly reflect a plurality of user behaviors operated by the users, and the operation sequence among the user behaviors, namely the operation flows have certain regularity. For example, the operation behavior of women is often expressed as a habit of clicking "commodity join shopping cart" to add a plurality of commodities to the shopping cart, and then clicking "submit order" to complete the whole process of placing order and settlement after comparing a plurality of commodities, while the operation behavior of men is often expressed as clicking "submit order" to complete the whole process of placing order and settlement.

As an optional embodiment, the present disclosure filters users who actually execute operations of the system under test according to the user characteristics of the specified user in the test instruction, so that the screened users are more suitable for the test task. The comprehensive coverage of the test scene can be realized, and the prediction operation links of different users can be predicted so as to simulate the actual operation scenes of different user groups and avoid the omission of the test scene.

According to an embodiment of the present disclosure, the test instruction includes a user characteristic of a specified user, and the aforementioned operation S210 (obtaining an actual operation link in response to the test instruction) includes: and responding to the test instruction, and acquiring Q user behaviors which are actually executed and operated in the M user behaviors of the tested system and an actual operation process of the Q user behaviors, wherein Q is a positive integer, and M is more than or equal to Q. The reference user is determined based on the user characteristics of the specified user, wherein the reference user has the same and/or similar user characteristics as the specified user. And screening out N user behaviors which are executed by the reference user for operation from the Q user behaviors which are actually executed for operation. And screening out the actual operation flows corresponding to the N user behaviors from the actual operation flows of the Q user behaviors.

In the present disclosure, a given user may be determined based on the needs of the test, and user characteristics may include, but are not limited to, gender, age, region, occupation, hobbies. The user characteristics can be determined according to the registration information of the user, or the user can be marked according to the historical operation behavior of the user, and then the users with different marks are classified and divided correspondingly, which is not limited by the disclosure.

Through the embodiment of the disclosure, the actual operation link is obtained according to the user characteristics, so that the predicted operation link is more fit with the actual operation scene with the user characteristics. Meanwhile, test scenes with different user characteristics can be simulated according to different test instructions, so that the omnibearing coverage of the scenes is realized, and the test accuracy and pertinence are improved.

According to an embodiment of the present disclosure, determining the predicted operational link based on the actual operational link includes: and constructing an initial transition matrix based on the N user behaviors of the actual operation link, wherein the transition matrix is used for representing the transition probability from the initial user behavior to the end user behavior in the N user behaviors. And counting transition probability values among the N user behaviors based on the actual operation flow of the actual operation link. And filling the transition probability value into the initial transition matrix of the initial transition matrix to obtain a final transition matrix. Based on the final transition matrix, a predicted operational link is determined.

According to the embodiment of the disclosure, the user behaviors can be encoded according to the extracted historical operation data of the reference user and the user behaviors obtained by dividing the historical operation data according to the above, the flow sequence among the user behaviors is arranged, and then the user behavior state set, namely the actual operation link, can be obtained. And initializing all elements in the transition matrix to be 0 to obtain the initial transition matrix, wherein the transition probability among different user behaviors comprises the transition probability among two adjacent user behaviors in the whole actual operation link. And sequentially counting the transition probabilities among different user behaviors, and filling the transition probabilities to corresponding element positions to replace 0 to obtain a final transition matrix. In the final transition matrix, the transition probability of no transition relation is 0, and the transition probability of a transition relation is not 0. The predicted operational link can therefore be determined based on the user behavior for which the transition probability is not 0, according to the final transition matrix.

As an optional embodiment, according to the final transition matrix, operation links with different transition times can be obtained, and the different transition times correspond to different step numbers of the operation links. For example, the predicted operation link of the shortest link (step number S1) and the predicted operation link of the longest link (step number S2) may be obtained, and when the transition times sequentially go from S1 to S2, the flow link of the complete operation of the corresponding user may be obtained by knowing the initial user behavior, the final transition matrix, and the transition times.

In specific implementation, taking an actual operating link a-c-d-f-g as an example, the 5 user behaviors are a, c, d, f and g, respectively. a represents initial user behavior, characterizes user behavior actually operated by the user at first, c, d and f represent intermediate user behavior, characterizes user behavior actually operated by the user at the middle, and g represents final user behavior, characterizes user behavior actually operated by the user at last. First, a 5 x 5 transition matrix may be constructed, which includes 25 elements, and the 25 elements are initialized to all 0, resulting in an initial transition matrix. The transition probability from the initial user behavior a to the end user behavior g includes a transition probability of the initial user behavior a transitioning to an intermediate user behavior c, a transition probability of the intermediate user behavior c transitioning to an intermediate user behavior d, a transition probability of the intermediate user behavior d transitioning to an intermediate user behavior f, and a transition probability of the intermediate user behavior f transitioning to the end user behavior g. The transition probabilities are sequentially counted, for example, the transition probability from the initial user behavior a to the intermediate user behavior c is counted, that is, the number of times of transition from the initial user behavior a to the intermediate user behavior c in the user historical behaviors can be counted, and a statistical value can be obtained. And performing statistics of other transition probabilities by analogy, and filling the statistics into the initial transition matrix to obtain a final transition matrix. The final transfer matrix comprises predicted operation links with transfer times of 2 times, 3 times and 4 times respectively, namely a predicted operation link of a shortest link (the step number is 3) and a predicted operation link of a longest link (the step number is 5), when the transfer times are sequentially from 2 times to 4 times, the predicted operation links a-c-g with the step number of 3, the predicted operation links a-c-f-g with the step number of 4 and the predicted operation links a-c-d-f-g with the step number of 5 can be obtained by knowing the initial user behavior a, the final transfer matrix and the transfer times of the user.

As an alternative embodiment, the predicted operational link may be determined from a Markov Chain (Markov Chain) based on the actual operational link. A markov chain is a stochastic process in probability theory and mathematical statistics that has a markov property and exists in a discrete set of indices and a state space. The markov chain has a markov property, which is a known current state, and future states are independent of past states. The transition of each state depends only on the first n states, and when n is 1, the transition of each state depends only on the last state, and the next state for the operation of the tested system is only related to the current state, no matter how the previous software works. Therefore, the Markov chain can be used for simulating the use scene of the tested system by the user to construct the use model. Markov chain usage models are a way of modeling from a starting state through many intermediate states to an ending state. There may be many links from the beginning of the test to the end of the test, and one link may represent one test case.

The first-order markov process mainly includes three parts, namely a state, an initial vector and a state transition matrix, and the following describes the implementation process of the three parts and a specific markov calculation process in detail.

(1) Status of state

The present disclosure divides the states into N states, where N is consistent with the number of user behaviors in the actual operational link described above.

(2) Initial vector

The user behavior data in a preset time period, for example, the user behavior data in several days before and after the current time, may be taken, and the initial operation behavior of the user, that is, the initial vector, is determined according to the service required by the current test.

(3) State transition matrix

The step of calculating the state transition matrix is as follows:

and (3-1) extracting the historical behaviors of the user, and arranging the historical behaviors of the user according to the user behavior coding and circulation sequence to obtain a user behavior state set.

And (3-2) sequentially counting the transition probabilities of different states of the user, for example, counting the probability of the state a to the state b, namely counting the times of the state a to the state b in the historical behaviors of the user.

And (3-3) confirming the final state g to be covered in the test to obtain the conversion probability of the state g, and filling the state g into the state transition matrix to obtain the final state transition matrix.

(4) Markov calculation process

The calculation steps are as follows:

and (4-1) acquiring the state, the initial vector and the state transition matrix according to (3-1), (3-2) and (3-3).

(4-2) obtaining the number of steps S1-S2 of the shortest and longest links according to the initial operation to the final operation of the test.

(4-3) when the conversion times are sequentially from S1 to S2, the initial vector, the state transition matrix and the conversion times are known, and the corresponding flow link of the complete operation of the user can be obtained.

According to an embodiment of the present disclosure, determining the predicted operational link of the designated user based on the final transition matrix includes: and obtaining a proposed prediction operation link based on the final transition matrix. And acquiring a preset threshold value of the transfer probability value. And determining the operation link meeting the preset threshold value in the proposed prediction operation links as the prediction operation link.

As an optional embodiment, in order to improve the rationality of the predicted operation link, when the predicted operation link is determined, the transition probability values may be screened, transitions with transition probabilities lower than a preset threshold are eliminated, and the predicted operation link is determined by a user behavior with a high transition probability.

In specific implementation, the preset threshold of the transition probability value can be set manually or determined according to historical operation data statistics for a tested system, and the method is not limited by the disclosure as long as the transition probability is screened.

According to an embodiment of the present disclosure, the test method may further include: and aiming at c user behaviors of the tested system, generating a test sub-case corresponding to each user behavior in advance to obtain c test sub-cases.

In specific implementation, the Selenium can be adopted to record a refined automation script so as to generate a test sub-case. The Selenium tool contains recording (Selenium IDE), writing and running (Selenium Remote Control) of tests and parallel processing (Selenium Grid) of tests. The automated script is recorded separately, for example, for the user action of "merchandise joined shopping cart" and numbered. The recording of other user actions is similar. The Selenium is a browser automatic testing framework and is a tool for testing Web application programs, and the steps of test cases written by software testers can be accurately reproduced by simulating various operations of users on Web pages. The Selenium test runs directly in the browser, just as a real user is operating. The browsers supported include, but are not limited to, IE (7, 8, 9, 10, 11), Mozilla FireFox, Safari, Goole, Chrome, Opera. And test scripts of different languages such as Net, Java, Perl and the like are supported to be automatically recorded and automatically generated.

According to an embodiment of the present disclosure, generating and executing a test case based on a predicted operational link includes: p test sub-cases corresponding to the P user behaviors expected to be operated are called. And arranging P test sub-cases according to the expected operation flows of the P user behaviors to generate and execute the test cases.

According to the predicted operation link determined by prediction, the corresponding fine test script generated in advance can be automatically executed after flow arrangement according to the link, and the tested system is automatically and accurately tested. In specific implementation, if the operation link is predicted to be a-c-g, the predicted 3 user behaviors to be operated include the user behaviors a, c and g, according to the embodiment of the disclosure, the test sub-case corresponding to the user behavior a is arranged first, then the test sub-case corresponding to the user behavior c is arranged, finally the test sub-case corresponding to the user behavior g is arranged, finally the test case is generated, and the test case is automatically executed, namely, the test instruction can be responded, so that the automatic test of the tested system is completed.

According to the embodiment of the disclosure, the flow arrangement is carried out on the refined automation script, namely the test sub-case, according to the predicted operation flow of the user behavior of the prediction operation link, and the reliable, flexible and efficient automation test can be realized.

FIG. 3 schematically shows a flow diagram of a testing method according to another embodiment of the present disclosure.

As shown in fig. 3, the method may include operations S310 to S360.

In operation S310, the tested system service module is divided and the service behavior is refined. The system function of the system to be tested can be analyzed, the division can be carried out according to the user behavior with the finest granularity, the user behavior of the system to be tested can be obtained, and data support is provided for obtaining the operation link.

In operation S320, the refinement step automation script is recorded. The Selenium can be used for recording the refined automatic script to generate the test sub-case, so that the pre-generated test sub-case can be conveniently and quickly called when the test case is generated.

In operation S330, the system under test acquires user operation behavior data at the embedded point. The user operation behavior data may be user operation behavior data within a preset time period.

In operation S340, a user operation behavior prediction is performed using a markov model. And constructing an operation model based on the Markov chain, and providing a basis for automatic generation of the software test case.

In operation S350, a flow layout configuration of the test script is performed according to the prediction result.

In operation S360, a refined automation test script is executed according to the flow arrangement configuration.

By the testing method provided by the disclosure, a Markov chain and a Selenium testing tool are combined, the operation flow of a specified user in a real tested system environment is simulated, and the automatic testing of a Web application program in the true sense is realized, namely, a test case is automatically generated and executed. Compiling a test script based on a Selenium test tool, referring to a constructed Markov chain model structure, correspondingly using each state in a model, carrying out module packaging on each state, then determining the state trend after the initial state by using a transition probability simulation algorithm, namely generating a test case, wherein the link can be executed after the state is determined, so that the fitting degree between the test case and the actual use scene of a user is improved, manpower and time can be saved to a great extent by automatic generation and execution, a more reliable test result is provided, and the test efficiency is further improved.

FIG. 4 schematically shows a block diagram of a test system according to an embodiment of the disclosure. As shown in FIG. 4, the system 400 includes an acquisition module 410, a determination module 420, and a testing module 430.

The obtaining module 410, in response to the test instruction, obtains an actual operation link, where the actual operation link includes N user behaviors that have been actually operated among M user behaviors of the system under test and an actual operation flow of the N user behaviors, M and N are positive integers, and M is greater than or equal to N.

Optionally, the obtaining module 410 may be configured to perform operation S210 described in fig. 2, for example, and is not described herein again.

The determining module 420 is configured to determine a predicted operation link based on the actual operation link, where the predicted operation link includes P user behaviors expected to be operated and a predicted operation flow of the P user behaviors from among the M user behaviors of the system under test, P is a positive integer, and M is greater than or equal to P.

Optionally, the determining module 420 may be configured to perform operation S220 described in fig. 2, for example, and is not described herein again.

The test module 430 is configured to generate and execute a test case based on the predicted operation link to respond to the test instruction, where the test case includes P test sub-cases, and the P test sub-cases correspond to P user behaviors one to one.

Optionally, the test module 430 may be configured to perform operation S230 described in fig. 2, for example, and is not described herein again.

According to an embodiment of the present disclosure, the test instruction includes a user characteristic of a specified user, and in response to the test instruction, the obtaining module 410 includes: and the obtaining submodule is used for responding to the test instruction and obtaining Q user behaviors which are actually executed and operated in the M user behaviors of the tested system and the actual operation process of the Q user behaviors, wherein Q is a positive integer, and M is more than or equal to Q. And the determining submodule is used for determining the reference user based on the user characteristics of the specified user, wherein the reference user and the specified user have the same and/or similar user characteristics. And the first screening submodule is used for screening the N user behaviors which are executed by the reference user from the Q user behaviors which are actually executed. And the second screening submodule is used for screening out the actual operation flows corresponding to the N user behaviors from the actual operation flows of the Q user behaviors.

According to an embodiment of the disclosure, the determining module includes: and the construction submodule is used for constructing an initial transition matrix based on the N user behaviors of the actual operation link, wherein the transition matrix is used for representing the transition probability from the initial user behavior to the final user behavior in the N user behaviors. And the statistic submodule is used for counting the transfer probability values among the N user behaviors based on the actual operation process of the actual operation link. And the filling submodule is used for filling the transition probability value into the initial transition matrix of the initial transition matrix so as to obtain a final transition matrix. And the determining submodule is used for determining the prediction operation link based on the final transfer matrix.

According to an embodiment of the present disclosure, determining the sub-module includes: and the obtaining unit is used for obtaining the proposed prediction operation link based on the final transfer matrix. And the acquisition unit is used for acquiring a preset threshold value of the transition probability value. And the determining unit is used for determining the operation link meeting the preset threshold value in the proposed prediction operation links as the prediction operation link.

According to an embodiment of the present disclosure, the system further comprises: and the generating module is used for generating test sub-cases corresponding to each user behavior in advance aiming at the P user behaviors of the tested system so as to obtain the P test sub-cases.

According to an embodiment of the present disclosure, a test module includes: the calling submodule is used for calling P test sub-cases corresponding to P user behaviors expected to be operated; and the test submodule is used for arranging P test sub-cases according to the predicted operation flows of the P user behaviors so as to generate and execute the test cases.

It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module in the system part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described herein again.

Any number of modules, sub-modules, units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units according to the embodiments of the present disclosure may be implemented at least partially as a hardware circuit, such as a field programmable gate array (FNGA), a programmable logic array (NLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, one or more of the modules, sub-modules, units according to embodiments of the disclosure may be implemented at least partly as computer program modules, which, when executed, may perform corresponding functions.

For example, the obtaining module, the determining module, the testing module, the obtaining sub-module, the determining sub-module, the first screening sub-module, the second screening sub-module, the constructing sub-module, the counting sub-module, the padding sub-module, the determining sub-module, the obtaining unit, the determining unit, the generating module, the calling sub-module, and the testing sub-module may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the disclosure, at least one of the obtaining module, the determining module, the testing module, the obtaining sub-module, the determining sub-module, the first screening sub-module, the second screening sub-module, the constructing sub-module, the counting sub-module, the populating sub-module, the determining sub-module, the obtaining unit, the determining unit, the generating module, the invoking sub-module, and the testing sub-module may be implemented at least partially as a hardware circuit, such as field programmable gate arrays (FNGAs), programmable logic arrays (NLAs), systems on a chip, systems on a substrate, systems on a package, Application Specific Integrated Circuits (ASICs), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuits, or in any one of three implementations, software, hardware and firmware, or in any suitable combination of any of them. Alternatively, at least one of the obtaining module, the determining module, the testing module, the obtaining sub-module, the determining sub-module, the first screening sub-module, the second screening sub-module, the constructing sub-module, the counting sub-module, the populating sub-module, the determining sub-module, the obtaining unit, the determining unit, the generating module, the invoking sub-module, and the testing sub-module may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the testing method and system thereof, according to an embodiment of the present disclosure. The computer system illustrated in FIG. 5 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.

As shown in fig. 5, a computer system 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. The processor 501 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 501 may also include onboard memory for caching purposes. The processor 501 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure as described above.

In the RAM 503, various programs and data necessary for the operation of the system 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. The processor 501 performs various operations of the test method described above by executing programs in the ROM 502 and/or the RAM 503. Note that the program may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of the test methods described above by executing programs stored in one or more memories.

According to an embodiment of the present disclosure, system 500 may also include an input/output (I/O) interface 505, input/output (I/O) interface 505 also being connected to bus 504. The system 500 may also include one or more of the following components connected to the I/O interface 505: an input section 501 including a keyboard, a mouse, and the like. Including an output portion 507 such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc. A storage section 508 including a hard disk and the like. And a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

According to an embodiment of the present disclosure, the testing method described above with reference to the flow chart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program, when executed by the processor 501, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. According to embodiments of the present disclosure, a computer-readable medium may include ROM 502 and/or RAM 503 and/or one or more memories other than ROM 502 and RAM 503 described above.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments. Or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to perform the testing method.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A method of testing, comprising:

responding to a test instruction, and acquiring an actual operation link, wherein the actual operation link comprises N user behaviors which are actually operated in M user behaviors of a tested system and an actual operation process of the N user behaviors, M and N are positive integers, and M is larger than or equal to N;

determining a prediction operation link based on the actual operation link, wherein the prediction operation link comprises P user behaviors expected to be operated in M user behaviors of the system to be tested and an expected operation process of the P user behaviors, P is a positive integer, and M is larger than or equal to P;

and generating and executing a test case based on the predicted operation link to respond to the test instruction, wherein the test case comprises P test sub-cases, and the P test sub-cases are in one-to-one correspondence with the P user behaviors.

2. The method of claim 1, wherein the test instruction includes a user characteristic of a specified user, and wherein obtaining the actual operational link in response to the test instruction comprises:

responding to a test instruction, and acquiring Q user behaviors which are actually executed and operated in M user behaviors of the tested system and an actual operation process of the Q user behaviors, wherein Q is a positive integer, and M is more than or equal to Q;

determining a reference user based on the user characteristics of the specified user, wherein the reference user and the specified user have the same and/or similar user characteristics;

screening out N user behaviors which are operated by the reference user from the Q user behaviors which are actually operated;

and screening out the actual operation flows corresponding to the N user behaviors from the actual operation flows of the Q user behaviors.

3. The method of claim 1, wherein the determining a predicted operational link based on the actual operational link comprises:

constructing an initial transition matrix based on the N user behaviors of the actual operation link, wherein the transition matrix is used for representing transition probability from the initial user behavior to the end user behavior in the N user behaviors;

counting transition probability values among the N user behaviors based on an actual operation process of the actual operation link;

populating the transition probability value into an initial transition matrix of the initial transition matrix to obtain a final transition matrix;

based on the final transition matrix, a predicted operational link is determined.

4. The method of claim 3, wherein said determining a predicted operational link for the given user based on the final transition matrix comprises:

obtaining a proposed prediction operation link based on the final transfer matrix;

acquiring a preset threshold value of a transfer probability value;

determining an operating link of the proposed predicted operating links that meets the preset threshold as the predicted operating link.

5. The method of claim 1, wherein the method further comprises:

and generating test sub-cases corresponding to each user behavior in advance aiming at the P user behaviors of the tested system so as to obtain the P test sub-cases.

6. The method of claim 1, wherein generating and executing test cases based on the predicted operational link comprises:

calling P test sub-cases corresponding to the P expected user behaviors to be operated;

and arranging the P test sub-cases according to the expected operation flows of the P user behaviors to generate and execute the test cases.

7. A test system, comprising:

an obtaining module, configured to obtain an actual operation link in response to a test instruction, where the actual operation link includes N user behaviors that have been actually operated among M user behaviors of a system under test and an actual operation flow of the N user behaviors, M and N are positive integers, and M is greater than or equal to N:

a determining module, configured to determine a predicted operation link based on the actual operation link, where the predicted operation link includes P user behaviors expected to be operated among the M user behaviors of the system under test and an expected operation flow of the P user behaviors, P is a positive integer, and M is greater than or equal to P;

and the test module is used for generating and executing a test case based on the prediction operation link so as to respond to the test instruction, wherein the test case comprises P test sub-cases, and the P test sub-cases are in one-to-one correspondence with the P user behaviors.

8. The system of claim 7, wherein the system further comprises:

and the generating module is used for generating test sub-cases corresponding to each user behavior in advance aiming at the P user behaviors of the tested system so as to obtain the P test sub-cases.

9. A computer system, comprising:

one or more processors; and

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.

10. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.