CN107577604B - Test data generation method and device and computer readable storage medium - Google Patents
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Abstract
The invention discloses a method and a device for generating test data, and relates to the field of data processing. The method comprises the following steps: a curve dividing step, namely dividing the probability distribution curve of the test data into a plurality of parts, wherein the test data are divided into a plurality of data sets according to the data types, and the number of the parts corresponds to the number of the data sets; a probability determination step, namely calculating the coverage area of each part, and determining the generation probability of each data set according to the ratio of each coverage area; and a data generation step, namely selecting from the data set according to the generation probability to generate test data. The method and the device can improve the accuracy and the efficiency of the test.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for generating test data, and a computer-readable storage medium.
Background
In many fields such as automated testing and performance testing, test data needs to be prepared in advance, or generation codes of the test data are provided, and test data of different service types are automatically generated in the script running process so as to be called by the test script.
In the prior art, pre-configured test data is stored in a configuration file (such as an excel table, a csv file or an xml file); loading a configuration file when the automation script runs; and processing all the test data item by item according to the service logic and a preset sequence to obtain a test result.
Disclosure of Invention
The inventors of the present invention have found that the following problems exist in the above prior art: test data are not configured according to the occurrence probability of the service, and the test result is inaccurate due to the difference with a real scene; in the testing process, the testing efficiency is low because the processed testing data needs to be removed while the testing data is selected. The present inventors have devised a solution to at least one of the above-mentioned problems.
The invention aims to provide a technical scheme for generating test data.
According to an embodiment of the present invention, there is provided a test data generation method including: a curve dividing step, namely dividing a probability distribution curve of test data into a plurality of parts, wherein the test data are divided into a plurality of data sets according to data types, and the number of the parts corresponds to the number of the data sets; a probability determination step of calculating the coverage area of each part and determining the generation probability of each data set according to the ratio of each coverage area; and a data generation step, selecting from the data set according to the generation probability to generate test data.
Optionally, the data selection number in each data set is determined according to the generation probability, and test data is selected from the corresponding data set according to the data selection number.
Optionally, the probability distribution curve is a plurality of probability distribution curves.
Optionally, processing the curve dividing step, the probability determining step and the data generating step on a plurality of probability distribution curves respectively to generate a plurality of groups of test data, and storing the plurality of groups of test data into a plurality of data sets respectively; the data set was randomly selected for testing.
Optionally, assigning the same preset value to each data set as a respective tag value; a data set selecting step, wherein a data set with the label value as the preset value is randomly selected; a data set label updating step, wherein the data set is used for testing, and the label value of the data set is updated; and repeating the data set selecting step and the data set label updating step until the label values of all the data sets are not the preset value.
Optionally, assigning the same preset value as respective label values to all the test data in the data set; a data selection step, wherein test data with the label value as the preset value is randomly selected; a data label updating step, wherein the test data is used for testing, and the label value of the test data is updated; and repeating the data selection step and the data label updating step until the label value of the test data in the data set is not the preset value.
According to another embodiment of the present invention, there is provided a test data generation apparatus including: a curve dividing module: the device comprises a probability distribution curve generating unit, a probability distribution unit and a data processing unit, wherein the probability distribution curve for test data is divided into a plurality of parts, the test data is divided into a plurality of data sets according to data types, and the number of the parts corresponds to the number of the data sets; a probability determination module, configured to calculate coverage areas of the portions, and determine a generation probability of each data set according to a ratio of the coverage areas; and the data generation module is used for selecting from the data set according to the generation probability so as to generate test data.
Optionally, the data generation module determines a data selection number in each data set according to the generation probability, and selects test data from the corresponding data set according to the data selection number.
Optionally, the data storage module is configured to store the test data generated according to the different probability distribution curves into different data sets respectively; and the data selection module is used for randomly selecting the data set for testing.
Optionally, the data selecting module executes the following steps: assigning the same preset value to each data set as respective label value; a data set selecting step, wherein a data set with the label value as the preset value is randomly selected; a data set label updating step, wherein the data set is used for testing, and the label value of the data set is updated; and repeating the data set selecting step and the data set label updating step until the label values of all the data sets are not the preset value.
Optionally, assigning the same preset value as respective label values to all the test data in the data set; a data selection step, wherein test data with the label value as the preset value is randomly selected; a data label updating step, wherein the test data is used for testing, and the label value of the test data is updated; and repeating the data selection step and the data label updating step until the label value of the test data in the data set is not the predicted value.
According to still another embodiment of the present invention, there is provided a test data generation apparatus including: a memory and a processor coupled to the memory, the processor being configured to perform the method of generating test data in any of the above embodiments based on instructions stored in the memory device.
According to still another embodiment of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the test data generation method in any of the above-described embodiments.
One advantage of the invention is that the generation probability of each type of test data is determined according to the probability distribution curve of the test data, so that the generation of the test data accords with a real scene, and the obtained test result is more accurate; and a one-to-many mapping relation between the labels and the data is established, so that the automatic screening of the test data is realized, and the test efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
The invention will be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
FIG. 1 shows a flow diagram of one embodiment of a method of test data generation of the present invention.
FIG. 2a is a schematic diagram illustrating one embodiment of a method for generating test data according to the present invention.
Fig. 2b shows a schematic diagram of another embodiment of the test data generation method of the present invention.
Fig. 3a shows a schematic diagram of a further embodiment of the test data generation method of the present invention.
Fig. 3b shows a schematic diagram of a further embodiment of the test data generation method of the present invention.
FIG. 4 shows a flow diagram of another embodiment of a method of test data generation of the present invention.
FIG. 5 is a schematic diagram illustrating one embodiment of a method for generating test data in accordance with the present invention.
Fig. 6 is a block diagram showing an embodiment of a test data generation apparatus of the present invention.
Fig. 7 is a block diagram showing another embodiment of the test data generation apparatus of the present invention.
Fig. 8 is a block diagram showing still another embodiment of the test data generation apparatus of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
FIG. 1 shows a flow diagram of one embodiment of a method of test data generation of the present invention.
As shown in FIG. 1, in step 110, the probability distribution curve of the test data is divided into a plurality of portions. For example, the probability distribution curve may be generated based on data extracted on a line or a pre-stored data table, or may be generated randomly.
In one embodiment, the test data is divided into 4 classes by the type of order service, service types A, B, C and D, respectively. As shown in FIG. 2a, the probability distribution curve of the test data is curve f1(x) Curve f is divided according to the type of service1(x) The division into 4 parts, the length of the abscissa interval of each part is equal.
In step 120, the coverage area of each portion is calculated, and the generation probability of each data set is determined according to the ratio of each coverage area.
In one embodiment, the area covered by each part of curve can be calculated as S through a trapezoidal formulaA、SB、SCAnd SD. After rounding and maximum common approximation calculation, the ratio of the area of each part can be obtained and used as the generation probability of each type of test data, for example, the ratio of the area of each part to the curve f1(x) Correspondingly, the ratio of the generation probability of each type of data is 3: 2: 1: 1.
in step 130, test data is generated by selecting from the data set based on the generated probabilities. For example, the data selection number in each data set may be determined according to the generation probability, and the test data may be selected from the corresponding data set according to the data selection number.
In one embodiment, as shown in FIG. 3a, the database stores 4 types of test data lists { A | a }1,a2…aK}，{B|b1,b2…bL}，{C|c1,c2…cMAnd { D | D }1,d2…dN}. According to the generation probability of each type of data, the number of the test data taken out from the corresponding list can be determined. For example, 3, 2, 1, and 1 may be randomly extracted from the 4 lists, and the number of extraction may be 3: 2: 1: 1 ratio, such as 6, 4, 2, and 2. Resulting sum curve f1(x) The corresponding test data may be a data set T as shown in FIG. 3a1:[a2,a3,a6；b1,b7；c5；d8]。
In the embodiment, the generation probability of each type of test data is calculated according to the probability distribution curve of each type of test data, and the corresponding number of test data is randomly selected from the list of each type of test data for testing according to the generation probability, so that the generation of the test data conforms to the real service occurrence rule, and the accuracy of the test result is improved.
FIG. 4 shows a flow diagram of one embodiment of a method of test data generation of the present invention.
As shown in fig. 4, in step 1310, the same preset value is assigned to each data set as the respective tag value.
In one embodiment, each type of order service occurs in relation to time, e.g., during time period T1The occurrence probability of the type A service is larger, but in the time period T2The occurrence probability of the medium-type-D service is larger, namely the generation probability of the test data changes along with the time. For example, as shown in FIGS. 2a and 2b, the time period T1And T2The corresponding test data probability distribution curves are respectively f1(x) And f2(x) In that respect And f1(x) And f2(x) The generation probability ratios of the corresponding data of each type are respectively 3: 2: 1: 1 and 2: 1: 2: 3.
and randomly selecting a corresponding number of test data from the test data list according to the generation probability of different time periods, and respectively storing the test data into different data sets. For example, as shown in FIG. 3b, corresponding to time period T1And T2The test data sets of (a) are: t is1:[a2,a3,a6；b1,b7；c5；d8]And T2:[a1,a5；b6；c1,c3；d1,d2,d3]. May be a data set T1And T1Setting an initial tag value of 0, for example, the combined tag value may represent the test data to be tested as a test data set consisting of a data set of { T }1|0，T2L 0 }. Therefore, the test data can be generated in different time periods according to the service occurrence probability in different time periods, so that the generation process of the test data is closer to the real situationThe method is described. The basis for dividing the test data set may also be other conditions such as different locations where the service occurs, different users, etc.
In step 1320, a data set with a predetermined tag value is randomly selected.
In step 1330, the data set is used for testing, updating the tag values of the data set.
In step 1340, it is determined whether the label values of all the data sets are not preset values. If not, then repeat step 1320; if so, the test ends (step 1350).
In one embodiment, the data set T in the test data set is randomly selected1Testing is carried out, and after the testing is finished, the data set T is used1The tag value of (1) is updated, the data set T can be updated1Put at the end of the test data set, i.e. when the test data set is T2|0，T 11. At this time, a data set with a tag value of 0, such as the data set T, is randomly selected from the test data set2And updating the label value to 1 after testing and putting the label value at the end of the set, namely the test data set is { T }1|1，T 21. And after searching, if the label values of all the data sets in the set are not the initial value 0, ending the test.
If more test data samples are needed in actual situations, 1 test on each data set is not enough to meet the sample requirements, and repeated tests can be performed on the data sets in the set. For example, after the tag values of all data sets are updated to 1, randomly extracting the data set with the tag value of 1 for testing, adding 1 to the tag value after the testing is finished, putting the data set at the end of the set, and repeating the above steps until the tag value in the set is P, where P may be an integer greater than or equal to 1.
In another embodiment, a data set T is selected1After testing the data set, it may be a data set T1All test data [ a ] in2,a3,a6；b1,b7；c5；d8]Assigning the same preset value as the respective tag value, e.g., the preset value may be 0, the tag value is setPost-test data set T1As shown in FIG. 5, is [ a ]2|0,a3|0,a6|0；b1|0,b7|0；c5|0；d8|0]. Randomly selecting a test datum with a tag value of 0, e.g. a2For testing, after testing, a2Adds 1 to the tag value of (b), and adds b1Put at the end of the queue, at which time the data set T is shown in FIG. 51Is [ a ]3|0,a6|0；b1|0,b7|0；c5|0；d8|0,a2|1](ii) a Continuing to select the test data with the label value of 0 for testing and updating the label value until T1The tag values of the test data in (1) are all 0.
If a large number of test data samples are needed in actual situations, 1 test on each test data in the data set is not enough to meet the sample requirements, and the test data in the data set can be tested repeatedly. For example, after the tag values of all the test data in the data set are updated to 1, randomly extracting the test data with the tag value of 1 for testing, adding 1 to the tag value after the test is finished, placing the test data at the tail of the queue, and repeating the above steps until the tag value in the set is Q, wherein Q may be an integer greater than or equal to 1.
In the embodiment, the test data is divided into different data sets according to different conditions of service occurrence, such as time periods, places, users and the like, so that a three-dimensional corresponding relation of data sets-data-probability is formed, the generation probability conditions of data of different service types can be more accurately described from multiple angles, and the test accuracy is improved; labels are set for different data sets and different test data in the data sets to identify whether the data sets or the data are tested or not, the data sets and the data are randomly selected according to the test data distribution probability, and an automatic random data selection mechanism with nested data inside and outside the data sets is formed, so that the test data generation efficiency is improved.
Fig. 6 is a block diagram showing an embodiment of a test data generation apparatus of the present invention.
As shown in fig. 6, the apparatus includes: a curve dividing module 61, a probability determination module 62 and a data generation module 63.
The curve dividing module 61 divides the probability distribution curve of the test data into a plurality of parts, wherein the test data is divided into a plurality of data sets according to the data types, and the number of the divided parts corresponds to the number of the data sets.
The probability determination module 62 calculates the coverage area of each portion, and determines the generation probability of each data set according to the ratio of the coverage areas.
The data generation module 63 selects from the data sets according to the generation probability to generate test data. For example, the data generating module 63 may determine the number of data selections in each data set according to the generation probability, and select the test data from the corresponding data set according to the number of data selections.
In one embodiment, as shown in fig. 7, the apparatus further comprises: a data storage module 74 and a data extraction module 75.
The data storage module 74 stores the test data generated according to the different probability distribution curves into different data sets, respectively.
The data selection module 75 randomly selects a data set for testing. For example, the data selection module 75 may perform the following steps: assigning the same preset value to each data set as respective label value; a data set selecting step, wherein a data set with a label value as a preset value is randomly selected; a data set label updating step, wherein the data set is used for testing, and the label value of the data set is updated; repeating the data set selecting step and the data set label updating step until all the label values of the data sets are not preset values.
In another embodiment, the data selection module 75 may select the data set for testing, which may include the following steps: assigning the same preset value as respective label value to all the test data in the data set; a data selection step, wherein test data with a label value as a preset value is randomly selected; a data label updating step, wherein the test data is used for testing, and the label value of the test data is updated; repeating the data selecting step and the data label updating step until the label value of the test data in the data set is not a predicted value.
In the embodiment, the generation probability of each type of test data is calculated according to the probability distribution curve of each type of test data, and the corresponding number of test data is randomly selected from the list of each type of test data for testing according to the generation probability, so that the generation of the test data conforms to the real service occurrence rule, and the accuracy of the test result is improved.
Fig. 8 is a block diagram showing another embodiment of the test data generation apparatus of the present invention.
As shown in fig. 8, the apparatus 80 of this embodiment includes: a memory 81 and a processor 82 coupled to the memory 81, the processor 82 being configured to execute a method of generating test data according to any one of the embodiments of the present invention based on instructions stored in the memory 81.
The memory 81 may include, for example, a system memory, a fixed nonvolatile storage medium, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), a database, and other programs.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
So far, the test data generation method, apparatus, and computer-readable storage medium according to the present invention have been described in detail. Some details well known in the art have not been described in order to avoid obscuring the concepts of the present invention. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
Although some specific embodiments of the present invention have been described in detail by way of illustration, it should be understood by those skilled in the art that the above illustration is only for the purpose of illustration and is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (11)
1. A method of generating test data, comprising:
a curve dividing step, namely dividing a probability distribution curve of test data into a plurality of parts, wherein the test data are divided into a plurality of data sets according to data types, the number of the parts corresponds to the number of the data sets, and the length of an abscissa interval of each part is equal;
a probability determination step of calculating the coverage area of each part and determining the generation probability of each data set according to the ratio of each coverage area;
a data generation step, selecting from the data set according to the generation probability to generate test data;
wherein the data generating step comprises:
and determining the data selection number in each data set according to the generation probability, and selecting test data from the corresponding data set according to the data selection number.
2. The generation method of claim 1, wherein the probability distribution curve is a plurality of probability distribution curves.
3. The generation method of claim 2, further comprising:
processing the curve dividing step, the probability determining step and the data generating step on a plurality of probability distribution curves respectively to generate a plurality of groups of test data, and storing the plurality of groups of test data into a plurality of data sets respectively;
the data set was randomly selected for testing.
4. The generation method of claim 3, wherein the randomly chosen data set for testing comprises:
assigning the same preset value to each data set as respective label value;
a data set selecting step, wherein a data set with the label value as the preset value is randomly selected;
a data set label updating step, wherein the data set is used for testing, and the label value of the data set is updated;
and repeating the data set selecting step and the data set label updating step until the label values of all the data sets are not the preset value.
5. The generation method of claim 4, wherein said using the data set for testing comprises:
assigning the same preset value to all the test data in the data set as respective label values;
a data selection step, wherein test data with the label value as the preset value is randomly selected;
a data label updating step, wherein the test data is used for testing, and the label value of the test data is updated;
and repeating the data selection step and the data label updating step until the label value of the test data in the data set is not the preset value.
6. An apparatus for generating test data, comprising:
a curve dividing module: the device comprises a probability distribution curve generating unit, a data collecting unit, a data analyzing unit and a data analyzing unit, wherein the probability distribution curve of test data is divided into a plurality of parts, the test data is divided into a plurality of data sets according to data types, the number of the parts corresponds to the number of the data sets, and the length of abscissa intervals of the parts is equal;
a probability determination module, configured to calculate coverage areas of the portions, and determine a generation probability of each data set according to a ratio of the coverage areas;
the data generation module is used for selecting from the data set according to the generation probability so as to generate test data;
and the data generation module determines the data selection number in each data set according to the generation probability and selects test data from the corresponding data set according to the data selection number.
7. The generation apparatus of claim 6, further comprising:
the data storage module is used for respectively storing the test data generated according to different probability distribution curves into different data sets;
and the data selection module is used for randomly selecting the data set for testing.
8. The generation apparatus of claim 7, wherein the data extraction module performs the steps of:
assigning the same preset value to each data set as respective label value;
a data set selecting step, wherein a data set with the label value as the preset value is randomly selected;
a data set label updating step, wherein the data set is used for testing, and the label value of the data set is updated;
and repeating the data set selecting step and the data set label updating step until the label values of all the data sets are not the preset value.
9. The generation apparatus of claim 8, wherein the using the data set for testing comprises:
assigning the same preset value to all the test data in the data set as respective label values;
a data selection step, wherein test data with the label value as the preset value is randomly selected;
a data label updating step, wherein the test data is used for testing, and the label value of the test data is updated;
and repeating the data selection step and the data label updating step until the label value of the test data in the data set is not the preset value.
10. An apparatus for generating test data, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of generating test data of any of claims 1-5 based on instructions stored in the memory.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of generating test data according to any one of claims 1 to 5.
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