CN109101414B - Massive UI test generation method and device based on buried point data - Google Patents
Massive UI test generation method and device based on buried point data Download PDFInfo
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- CN109101414B CN109101414B CN201810615205.9A CN201810615205A CN109101414B CN 109101414 B CN109101414 B CN 109101414B CN 201810615205 A CN201810615205 A CN 201810615205A CN 109101414 B CN109101414 B CN 109101414B
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- G06F11/36—Preventing errors by testing or debugging software
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- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
Abstract
The invention provides a massive UI test generation method and device based on buried point data, and belongs to the technical field of software testing. It has solved the unreasonable scheduling problem of prior art design. The massive UI test generation method and device based on the buried point data comprise the following steps: modeling buried point data, and collecting user data; checking the result reversely to obtain a test case model irrelevant to the platform; and generating a UI test case, and outputting data obtained in buried point data modeling and result reverse inspection as the test case. The massive UI test generation method and the device based on the buried point data have the advantages that: the problem that PIT depends on PIM excessively is solved, and the PIT can simulate the use behavior of a user to a high degree.
Description
Technical Field
The invention belongs to the technical field of software testing, and particularly relates to a massive UI test generation method and device based on buried point data.
Background
With the rapid development of the mobile internet, competition among internet products is more and more prominent. Therefore, the ease of use and stability of internet products will become one of the important considerations for the public whether to select the product. The UI-based test method is a test scheme closest to a user group, and whether the product is stable in the using process can be observed more intuitively. Even a back-end tester can use the UI test means as an important functional verification scheme in the regression test, but due to the limitation of personnel and time, the existing UI-based test verification means has few cases, much time consumption and incapability of simulating the actual use conditions of various real users, so that part of BUG omission still exists, and the user experience is seriously influenced.
In order to solve the problems of the prior art, people have long searched for and put forward various solutions. The model-driven test is an improved software testing technology, and the model-driven test is a method that a model is firstly established for a program to be tested according to a functional requirement specification, and then a test case is generated by analyzing the model. Fig. 6 shows a test case generation process based on a model-driven test technology, where PIM is a platform-independent system model diagram, a PIT model is a platform-independent test model, where the PIT model is obtained by horizontally converting PIM, and finally a test case is automatically generated according to the obtained test model and test data given by corresponding testers, Nmodle is a relatively mature model-driven test case automatic generation tool on the market, and the principle of model creation is as follows: 1. programs are used to process data, which may also be referred to as states; 2. a user processes data through an operation interface provided by a program, and the operation interface can also be called an Action (Action); 3. the alteration of data in turn affects whether some actions can be performed.
When a tester performs PIM modeling, actions and states need to be abstracted, for example, the states of a program in a user payment scenario should be as follows: 1. program state: an operating state and a non-operating state; 2. user state: logged on and logged off states; 3. and (4) payment: balance payment, card payment and revocation payment; 4. and (3) payment state: payment success and payment failure. The actions should include: 1. selecting a payment mode; 2. confirm payment and revoke payment. And the tester needs to compile the actions and the states into a PIM model by adopting c # language, then generates a test model by utilizing an Nmode tool, and finally automatically generates a test case by combining test data provided by the tester.
The solution described above improves to some extent some of the problems of the prior art, but it also has at least the following drawbacks: the test case automatic generation technology based on the model-driven test method not only needs a certain code compiling capability of a tester, but also needs the tester to abstract a test scene and a test state in more detail, and if the abstraction is not accurate enough and comprehensive enough, the generated test cases are incomplete, and meanwhile, the automatically generated test cases are generated based on the model, so that the problems that the real conditions of products used by users cannot be simulated and the like are solved.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method and an apparatus for generating a mass UI test based on buried point data, so as to solve the problems in the prior art that software testers are required to have strong abstract capability and code writing capability, and the simulation degree is poor when a user operates a simulation, not only consider the user operation steps under normal conditions, but also incorporate the user operation pause time into the test case.
The invention provides a massive UI test generation method based on buried point data, which comprises the following steps: modeling buried point data, and collecting user data; checking the result reversely to obtain a test case model irrelevant to the platform; and generating a UI test case, and outputting data obtained in buried point data modeling and result reverse inspection as the test case.
In the method, a user behavior probability histogram and a residence time probability histogram are obtained in buried point data modeling.
In the above method, the user behavior probability histogram and the dwell time probability histogram are for uniform user behavior.
In the method, the collected user data in the buried point data modeling comprises capturing, processing, sending and implementing processes aiming at the behavior information of a specific user, corresponding result information brought by the behavior of the user and the time information of the occurrence of the behavior of the user.
In the method, in the result reverse check, the behavior information of the user is a, and the behavior matrix selected by the user in the next step is a ═ a1,a2,a3…,anAnd the corresponding user behavior probability histogram data is X ═ X1,x2,x3…,xn](∑n i＝1xi1), dwell time result matrix T ═ T1,t2,t3…,tnAnd the corresponding dwell time probability histogram M ═ M1,m2,m3…,mn](∑n i＝1mi＝1)。
In the above method, the test case model in the result reversal check includes Anext ═ a [ rand (n) ], where rand (n) coincides with the probability histogram X, and Tnext ═ T [ rand (n) ], where rand (n) coincides with the probability histogram M.
In the method, in the UI test case generation, result information generated by the user under a specific behavior and precondition information of the behavior are judged, and specific behavior information of the user under a special condition and the result information generated by the specific behavior information are obtained.
In the above method, the stay time information of the specific user behavior needs to be referred to while the test case in the UI test case generation is output. So as to simulate the user operation more realistically.
In the above method, the histogram data is referred to while the test case in the UI test case generation is output. To prioritize the test cases.
The invention also provides a massive UI test generation device based on the buried point data, which comprises the following components: the buried point data modeling unit is used for acquiring user data; a result reverse checking unit for obtaining a platform-independent test case model; and the UI test case generation unit is used for outputting data obtained in buried point data modeling and result reverse inspection as a test case.
In the device, a user behavior probability histogram and a residence time probability histogram are obtained in the buried point data modeling unit.
In the above apparatus, the user behavior probability histogram and the stay time probability histogram are for uniform user behavior.
In the device, the collected user data in the buried point data modeling unit comprises capturing, processing, sending and implementing processes aiming at the behavior information of a specific user, corresponding result information brought by the behavior of the user and the time information of the occurrence of the behavior of the user.
In the above apparatus, in the result reverse check unit, the behavior information of the user is a, and the behavior matrix that the user can select next is a ═ a1,a2,a3…,anAnd the corresponding user behavior probability histogram data is X ═ X1,x2,x3…,xn](∑n i＝1xi1), dwell time result matrix T ═ T1,t2,t3…,tnAnd the corresponding dwell time probability histogram M ═ M1,m2,m3…,mn](∑n i＝1mi＝1)。
In the above-described apparatus, the test case model in the result reverse check unit includes Anext ═ a [ rand (n) ], where rand (n) coincides with the probability histogram X, and Tnext ═ T [ rand (n) ], where rand (n) coincides with the probability histogram M.
In the device, the UI test case generating unit judges the result information generated by the user under the specific behavior and the precondition information of the behavior to obtain the specific behavior information of the user under the special condition and the result information generated by the specific behavior information.
In the above apparatus, the stay time information of the specific user behavior needs to be referred to while the test case in the UI test case generating unit is output. So as to simulate the user operation more realistically.
In the above apparatus, the histogram data is referred to while the test case in the UI test case generating unit is output. To prioritize the test cases.
Compared with the prior art, the massive UI test generation method and device based on the buried point data have the advantages that: 1. the method and the device have the advantages that the problem that the PIT depends on the PIM excessively is solved, the PIT can simulate the use behavior of a user to a high degree, the PIT in the method and the device thereof is completely learned from buried point data, testers are not needed to abstract the PIM, and the problem that the PIT is incomplete due to the PIM is solved; 2. the test data of the method and the device thereof consists of the behaviors of real users, and under the condition that the considered user operation stay time possibly brings influence, the user operation stay time is taken into the test model, so that the generated test case can simulate the user operation more truly and effectively; 3. in addition, the method and the device thereof can also determine the priority order of the test cases according to the user behavior result data.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 provides a flow chart of a method in an embodiment of the invention.
FIG. 2 provides an example user behavior histogram for buried point data modeling in a method in an embodiment of the invention.
FIG. 3 provides an example residence time histogram for buried point data modeling in a method in an embodiment of the invention.
FIG. 4 provides a data acquisition flow diagram for the behavior array and the dwell time array in the buried point data modeling in a method in an embodiment of the invention.
Fig. 5 provides a schematic diagram of the operation of the apparatus in an embodiment of the invention.
FIG. 6 provides a flow diagram of a test case generation process in the prior art.
Detailed Description
The present invention will be described in further detail below by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, the method for generating a massive UI test based on buried point data includes: modeling buried point data, and collecting user data; checking the result reversely to obtain a test case model irrelevant to the platform; and generating a UI test case, and outputting data obtained in buried point data modeling and result reverse inspection as the test case.
Specifically, in the buried point data modeling, a user behavior probability histogram and a residence time probability histogram are obtained; the user behavior probability histogram and the stay time probability histogram are directed at uniform user behaviors; the collected user data in the buried point data modeling includes capturing, processing, sending and implementing processes aiming at behavior information of a specific user, corresponding result information brought by the behavior of the user and time information of the occurrence of the behavior of the user.
One example of the buried point data modeling is shown in fig. 2 and 3, assuming that the a behavior is followed by B, C, D, E four behaviors and the occurrence times are 5, 6, 7 and 8 times, respectively, and assuming that the a behavior is sequentially 3s, 5s, 6s and 7s from the next time, the times are 3, 6, 5 and 3 times, respectively.
Further, in the result reverse check, the behavior information of the user is a, and the behavior matrix selected by the user in the next step is a ═ a1,a2,a3…,anAnd the corresponding user behavior probability histogram data is X ═ X1,x2,x3…,xn](∑n i＝1xi1), dwell time result matrix T ═ T1,t2,t3…,tnAnd the corresponding dwell time probability histogram M ═ M1,m2,m3…,mn](∑n i＝1mi1); the test case model in the result reverse check here includes Anext ═ A [ rand (n)]Wherein rand (n) matches the probability histogram X, Tnext ═ T [ rand (n)]Wherein rand (n) is in accordance with the probability histogram M. However, in real-world operation, the resulting matrix is found to be smaller than the matrix a that we learned from buried point data. Let behavior a be the last step behavior of behavior b, and R ═ R1,r2…,rnIt can be easily understood that not all r can lead to the generation of the action b. To solve this problem, we get preconditions for different behaviors by back-checking the results.
Further, in the UI test case generation, result information generated by the user under a specific behavior and precondition information of the behavior are judged to obtain specific behavior information of the user under a special condition and result information generated by the specific behavior information; when the test case in the UI test case generation is output, the stay time information of specific user behaviors needs to be referred to so as to more truly simulate the user operation; here, the histogram data is referred to when the test cases in the UI test case generation are output, so as to prioritize the test cases.
The working principle is as follows: in buried point data modeling, buried point data histogram statistics first create a next step action array ArrayList for each action<String>A, a residence time array ArrayList<float>B, and two ArrayLists<Integer>C，ArrayList<Integer>D is used for counting histogram data, traversing all buried point data, and acquiring the next action a of the action when the action is met1Inspection of a1Whether an array a exists or not, and if so, acquiring the array a1The subscript index is a if not present1Putting the data into an array A, recording the current subscript index, and simultaneously, correspondingly arranging a histogram array Cindex＝Cindex+1, next, the next action a is acquired2Repeating the above steps to finally obtain a histogram D of the residence time, wherein the specific flow is shown in FIG. 4; in the result reverse check, firstly, a HashMap of a hash table is created<String,String>E, reading buried point data and obtaining the current behavior a1Current behavior state r and user next-step behavior a2Putting the current state and the next action of the user into a Hash array, wherein key is the action; in the UI test case generation, combining the buried point data with the histogram data of the specific behaviors obtained in the buried point data modeling and the precondition of the specific behaviors obtained in the result reverse inspection, circularly traversing each specific behavior, marking the preconditions and the generated result thereof as well as the possible time length information, and then performing text transfer on the behaviors.
As shown in fig. 5, the apparatus for generating a massive UI test based on buried point data includes: the buried point data modeling unit is used for acquiring user data; a result reverse checking unit for obtaining a platform-independent test case model; and the UI test case generation unit is used for outputting data obtained in buried point data modeling and result reverse inspection as a test case.
Specifically, in the buried point data modeling unit, a user behavior probability histogram and a residence time probability histogram are obtained; the user behavior probability histogram and the stay time probability histogram are directed at uniform user behaviors; the collected user data in the buried point data modeling unit comprises capturing, processing, sending and implementing processes aiming at the behavior information of a specific user, corresponding result information brought by the behavior of the user and the time information of the occurrence of the behavior of the user.
Further, in the result reverse checking unit, the behavior information of the user is a, and the behavior matrix that the user can select next is a ═ a1,a2,a3…,anAnd the corresponding user behavior probability histogram data is X ═ X1,x2,x3…,xn](∑n i＝1xi1), dwell time result matrix T ═ T1,t2,t3…,tnAnd the corresponding dwell time probability histogram M ═ M1,m2,m3…,mn](∑n i＝1mi1); the test case model in the result reverse checking unit here includes Anext ═ A [ rand (n)]Wherein rand (n) matches the probability histogram X, Tnext ═ T [ rand (n)]Wherein rand (n) is in accordance with the probability histogram M.
Further, in the UI test case generating unit, result information generated by the user under a specific behavior and precondition information of the behavior are determined to obtain specific behavior information of the user under a special condition and result information generated by the specific behavior information; when the test case in the UI test case generation unit is output, the stay time information of specific user behaviors needs to be referred to so as to simulate the user operation more truly; here, the test cases in the UI test case generating unit need to be output while referring to the histogram data to prioritize the test cases.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (14)
1. A massive UI test generation method based on buried point data is characterized by comprising the following steps: the method comprises the following steps:
modeling buried point data, and collecting user data; in buried point data modeling, buried pointsThe data histogram statistics first create a next step behavior array ArrayList for each behavior<String>A, a residence time array ArrayList<float>B, and two ArrayLists<Integer>C，ArrayList<Integer>D is used for counting histogram data, traversing all buried point data, and acquiring the next action a of the action when the action is met1Inspection of a1If the array A exists, acquiring a1The subscript index is a if not present1Putting the data into an array A, recording the current subscript index, and simultaneously, correspondingly arranging a histogram array Cindex＝Cindex+1, next, the next action a is acquired2Repeating the steps to finally obtain a histogram D of the stay time;
checking the result reversely to obtain a test case model irrelevant to the platform; in the result reverse check, firstly, a HashMap of a hash table is created<String,String>E, reading buried point data and obtaining the current behavior a1Current behavior state r and user next-step behavior a2Putting the current state and the next action of the user into a Hash array, wherein key is the action;
and generating a UI test case, namely combining the buried point data with the histogram data of the specific behaviors obtained in the buried point data modeling and the preconditions of the specific behaviors obtained in the result reverse inspection, circularly traversing each specific behavior, marking the preconditions and the generated results of the behavior and the possible time length information, and then performing text transfer on the behaviors to obtain the test case.
2. The massive UI test generation method based on buried point data as claimed in claim 1, characterized in that in the buried point data modeling, a user behavior probability histogram and a dwell time probability histogram are obtained.
3. The massive UI test generation method based on buried point data as claimed in claim 2, characterized in that the user behavior probability histogram and the stay time probability histogram are for unified user behavior.
4. The method for generating mass UI tests based on buried point data as claimed in claim 1, wherein the collecting user data in the buried point data modeling comprises capturing, processing, sending and implementing processes aiming at the behavior information of a specific user, corresponding result information brought by the user's behavior and the time information of the user's behavior.
5. The massive UI test generation method based on the buried point data as claimed in claim 1, characterized in that in the UI test case generation, the result information generated by the user under the specific behavior and the precondition information of the behavior are judged to obtain the specific behavior information of the user under the special condition and the result information generated by the user.
6. The massive UI test generation method based on buried point data as claimed in claim 1, characterized in that the stay time information of specific user behavior is referred to while the test case in the UI test case generation is outputted.
7. The massive UI test generation method based on buried point data as claimed in claim 2, characterized in that histogram data is referred to while test cases in the UI test case generation are output.
8. A massive UI test generation device based on buried point data is characterized in that: the method comprises the following steps:
the buried point data modeling unit is used for acquiring user data; in buried point data modeling, buried point data histogram statistics first create a next step action array ArrayList for each action<String>A, a residence time array ArrayList<float>B, and two ArrayLists<Integer>C，ArrayList<Integer>D is used for counting histogram data, traversing all buried point data, and acquiring the next action a of the action when the action is met1Inspection of a1Whether or not toExists in the array A, if exists, obtains a1The subscript index is a if not present1Putting the data into an array A, recording the current subscript index, and simultaneously, correspondingly arranging a histogram array Cindex＝Cindex+1, next, the next action a is acquired2Repeating the steps to finally obtain a histogram D of the stay time;
a result reverse checking unit for obtaining a platform-independent test case model; in the result reverse check, firstly, a HashMap of a hash table is created<String,String>E, reading buried point data and obtaining the current behavior a1Current behavior state r and user next-step behavior a2Putting the current state and the next action of the user into a Hash array, wherein key is the action;
and the UI test case generating unit is used for combining the buried point data, the histogram data of the specific behaviors obtained in the buried point data modeling and the precondition of the specific behaviors obtained in the result reverse inspection, circularly traversing each specific behavior, marking the preconditions and the generated result of the behavior and the possible time length information of the behavior, and then performing text transfer on the behavior to obtain the test case.
9. The device for generating mass UI tests based on buried point data according to claim 8, wherein in the buried point data modeling unit, a user behavior probability histogram and a dwell time probability histogram are obtained.
10. The device for generating mass UI tests based on buried point data of claim 9, wherein the user behavior probability histogram and the dwell time probability histogram are for uniform user behavior.
11. The device for generating mass UI tests based on buried point data as claimed in claim 8, wherein the collected user data in the buried point data modeling unit comprises capturing, processing, sending and implementing processes aiming at the behavior information of a specific user, corresponding result information brought by the user's behavior and the time information of the user's behavior.
12. The device for generating mass UI tests based on buried point data of claim 8, wherein in the UI test case generating unit, the result information generated by the user under the specific behavior and the precondition information of the behavior are judged to obtain the specific behavior information of the user under the special condition and the result information generated by the specific behavior information.
13. The mass UI test generation device based on buried point data of claim 8, wherein the stay time information of a specific user behavior is referred to while the test case in the UI test case generation unit is outputted.
14. The mass UI test generation device according to claim 9, wherein the histogram data is referred to while the test cases in the UI test case generation unit are outputted.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106649120A (en) * | 2016-12-28 | 2017-05-10 | 中国银联股份有限公司 | Data acquisition method, and data analysis method and system |
| CN107688530A (en) * | 2017-04-06 | 2018-02-13 | 平安科技（深圳）有限公司 | Method for testing software and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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