CN117573543A - Test data generation method and device, electronic equipment and storage medium - Google Patents

Abstract

The invention discloses a method and a device for generating test data, electronic equipment and a storage medium, wherein in response to a generation instruction of the test data, a rule configuration file in the generation instruction is used for acquiring a test data generation rule, and corresponding keyword groups are generated according to keyword parameter lists and corresponding weight lists of different data types respectively based on the test data generation rule; and inputting the keyword group into a trained preset language model to obtain target test data corresponding to the keyword group. According to the method for generating the test data, keyword parameters of different data types are weighted through writing the test data generation rule, the test data is constructed, the generated test data more accords with the distribution characteristic of production data, the diversity of the test data is met, and meanwhile the randomness of the test data is guaranteed; and generating target test data according to the key word groups based on the trained preset language model, so as to meet the requirement of a large amount of test data in the test process.

Description

Test data generation method and device, electronic equipment and storage medium

Technical field

The present disclosure relates to the field of data processing technology, and in particular, to a test data generation method and device, electronic equipment and storage media.

Background technique

In software testing, test data is the basis of testing and has a vital impact on the accuracy and comprehensiveness of test results. Testers need to design and generate test data based on demand scenarios, and can use test data generation tools to quickly generate large amounts of test data.

During the testing process, it is often necessary to construct a large amount of test data, and these test data need to meet various types, parameters, and scenarios. Relevant test data generation technology uses test parameters to characterize the conditions for generating test data. A part of the required test data has been stored in the preset database, and the test data under corresponding conditions is retrieved from the database according to the set parameters.

However, the method of constructing and generating test data based on set parameters cannot support the random distribution of multiple parameters and multiple types of data, and cannot simulate the data situation of the production environment.

Contents of the invention

The present disclosure provides a test data generation method, device, electronic device, and storage medium. Its main purpose is to solve the problem that the method of constructing and generating test data based on set parameters cannot support the random distribution of multiple parameters and multiple types of data, and cannot simulate the data situation of the production environment.

According to a first aspect of the present disclosure, a method for generating test data is provided, which includes:

In response to the test data generation instruction, test data generation rules are obtained based on the rule configuration file in the generation instruction. The test data generation rules include a target number of test data and a parameter weight list, and the parameter weight list includes different data categories. Keyword parameter list and corresponding weight list;

Based on the test data generation rules, generate corresponding keyword groups according to keyword parameter lists and corresponding weight lists of different data categories;

Input the keyword group into the trained preset language model to obtain target test data corresponding to the keyword group.

Optionally, before responding to the test data generation instruction and obtaining the test data generation rules based on the rule configuration file in the generation instruction, the method includes:

In response to the configuration instruction for the rule configuration file, obtain the rule configuration file carried by the configuration instruction;

Based on the rule configuration file, write test data generation rules according to the configuration template of the preset test data generation rules;

Verify the test data generation rules;

After it is determined that the test data generation rule passes the verification, the test data generation rule is written into a preset database.

Optionally, before generating corresponding keyword groups according to keyword parameter lists and corresponding weight lists of different data categories based on the test data generation rules, the method further includes:

Obtain the parameter weight list, where a keyword parameter corresponds to a weight value and a first subscript, and there is a mapping relationship between the first subscript and the keyword parameter; the first subscript is the parameter The subscript corresponding to the storage address where the keyword parameter is stored in the weight list;

Initialize the weight values in the weight list to obtain the corresponding prefix sum list. The prefix sum list is an ordered increasing list according to the sum of the weight values and is used to represent the boundary of the weight value, where, The first subscript corresponds to the second subscript; each boundary value in the prefix sum list corresponds to a third subscript, and the third subscript corresponds to the second subscript;

A mapping relationship between the first subscript and the third subscript is established based on the second subscript.

Optionally, based on the test data generation rules, generating corresponding keyword groups according to keyword parameter lists and corresponding weight lists of different data categories includes:

Call the weight random selection algorithm to generate a random number according to the weight value, where the random number is any number within the range from 1 to the maximum value of the boundary value in the prefix sum list;

Call the binary search algorithm to respectively search for the target boundary value of the random number corresponding to the prefix sum list;

Obtain the third subscript corresponding to the target boundary value respectively;

According to the mapping relationship between the first subscript and the third subscript, respectively search for the first subscript corresponding to the third subscript;

According to the first subscript, search for the corresponding keyword parameter from the keyword parameter list;

The keyword group is combined according to the found keyword parameters.

Optionally, searching for the corresponding keyword parameter from the keyword parameter list according to the first subscript includes:

When it is detected that the number of keyword parameters reaches the target number, stop calling the random selection algorithm to generate the random numbers.

Optionally, the training method of the preset language model includes:

Obtain training text data of different data categories from a preset database;

Filter the text data for training according to preset filtering rules to obtain training keywords of the target data category, where the preset filtering rules include removing text data of the preset data category based on regular expression rules;

Keyword phrases for training of the target data category are input into the preset language model for training, and a trained preset language model is obtained.

Optionally, after inputting the keyword group into the preset language model and obtaining target test data corresponding to the keyword group, the method includes:

Read the timing sending information in the test data generation rules and determine whether to send the target test data in a timely manner;

In the case where it is determined to send the target test data regularly, when the timing sending conditions are met, the target test data are respectively sent to the target database of the corresponding data category according to the data category of the keyword parameter;

If it is monitored that the number of sent target test data is consistent with the received number of the target database, then the transmission of the target test data is completed;

If it is monitored that the number of target test data received by the target database is inconsistent with the target number, it is determined that the target data transmission is abnormal.

According to a second aspect of the present disclosure, a device for generating test data is provided, including:

An acquisition unit configured to respond to a test data generation instruction and acquire test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include a target quantity of test data and a parameter weight list. The parameter weights The list includes a list of keyword parameters of different data categories and a corresponding weight list;

A generation unit, configured to generate corresponding keyword groups based on the test data generation rules according to the keyword parameter lists of different data categories and the corresponding weight lists;

An input unit is used to input the keyword group into a trained preset language model to obtain target test data corresponding to the keyword group.

Optionally, the device further includes a writing unit, which includes:

The first acquisition module is configured to obtain the test data generation rules based on the rule configuration file in the generation instruction in response to the generation instruction of the test data, and in response to the configuration instruction of the rule configuration file, obtain the The rule configuration file carried by the configuration instruction;

A writing module, configured to write test data generation rules according to the configuration template of the preset test data generation rules based on the rule configuration file;

A verification module, used to verify the test data generation rules;

A writing module, configured to write the test data generation rules into a preset database after determining that the test data generation rules pass the verification.

Optionally, the device further includes an establishment unit, which includes:

The second acquisition module is used to obtain the parameter weight list before generating corresponding keyword groups based on the keyword parameter lists and corresponding weight lists of different data categories based on the test data generation rules, wherein a The keyword parameter corresponds to a weight value and a first subscript, and there is a mapping relationship between the first subscript and the keyword parameter; the first subscript is the value in the parameter weight list where the keyword parameter is stored. The subscript corresponding to the storage address;

An initialization module, used to initialize the weight values in the weight list to obtain the corresponding prefix sum list. The prefix sum list is an ordered increasing list according to the size of the sum of the weight values, and is used to represent the weight value. Boundary, wherein the first subscript corresponds to the second subscript; each boundary value in the prefix sum list corresponds to a third subscript, and the third subscript corresponds to the second subscript. mark correspondence;

An establishing module, configured to establish a mapping relationship between the first subscript and the third subscript based on the second subscript.

Optionally, the generating unit includes:

A generation module, configured to call a weight random selection algorithm and generate a random number according to the weight value, where the random number is any number within the range of 1 to the maximum value of the boundary value in the prefix sum list;

The first search module is used to call the binary search algorithm to respectively search for the target boundary value of the random number corresponding to the prefix sum list;

The third acquisition module is used to respectively acquire the third subscript corresponding to the target boundary value;

A second search module, configured to respectively search for the first subscript corresponding to the third subscript according to the mapping relationship between the first subscript and the third subscript;

A third search module, configured to search for the corresponding keyword parameter from the keyword parameter list according to the first subscript;

A combination module, configured to combine the found keyword parameters into the keyword group.

Optionally, the combination module is also used to:

When it is detected that the number of keyword parameters reaches the target number, stop calling the random selection algorithm to generate the random number.

Optionally, the device also includes a training unit, the training unit includes:

The fourth acquisition module is used to acquire training text data of different data categories from the preset database;

A filtering module configured to filter the text data for training according to preset filtering rules to obtain training keywords of the target data category. The preset filtering rules include removing text data of the preset data category based on regular expression rules. ;

The training module is used to input the training keywords of the target data category into the preset language model for training, and obtain the trained preset language model.

Optionally, the device further includes a monitoring unit, which includes:

A judgment module, used to input the keyword group into the preset language model and obtain the target test data corresponding to the keyword group, read the scheduled transmission information in the test data generation rule, and determine whether to send the target in time. Test Data;

A sending module, configured to send the target test data to the target database of the corresponding data category according to the data category of the keyword parameter when the timing sending condition is met when it is determined to send the target test data regularly;

The first monitoring module is configured to complete the transmission of the target test data if it monitors that the number of sent target test data is consistent with the received number of the target database;

The second monitoring module is configured to determine that the target data transmission is abnormal if it detects that the number of target test data received by the target database is inconsistent with the target number.

According to a third aspect of the present disclosure, an electronic device is provided, including:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can perform the method described in the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method described in the first aspect.

According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described in the foregoing first aspect.

The test data generation method and device, electronic equipment and storage medium provided by the present disclosure respond to the test data generation instruction and obtain the test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include test The target number of data and the parameter weight list, the parameter weight list includes the keyword parameter list of different data categories and the corresponding weight list; based on the test data generation rules, respectively according to the keyword parameter list of different data categories and the corresponding weight list The weight list generates a corresponding keyword group; the keyword group is input into the trained preset language model to obtain target test data corresponding to the keyword group. The test data generation method provided by this disclosure weights keyword parameters of different data categories by writing test data generation rules, and implements the construction of test data according to the test data generation rules, so that the generated test data is more consistent with the distribution of production data. It not only meets the diversity of test data but also ensures the randomness of test data; and generates target test data based on keyword groups based on the trained preset language model to meet the need for a large amount of test data during the test process.

It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the present application will become readily understood from the following description.

Description of the drawings

The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. in:

Figure 1 is a schematic flowchart of a method for generating test data provided by an embodiment of the present disclosure;

Figure 2 is a schematic flowchart of writing test data generation rules provided by an embodiment of the present disclosure;

Figure 3 is a schematic diagram of a rule configuration instruction page provided by an embodiment of the present disclosure;

Figure 4 is a schematic flowchart of generating keyword groups according to an embodiment of the present disclosure;

Figure 5 is a schematic structural diagram of a test data generation device provided by an embodiment of the present disclosure;

Figure 6 is a schematic structural diagram of another test data generation device provided by an embodiment of the present disclosure;

FIG. 7 is a schematic block diagram of an example electronic device 500 provided by an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the present disclosure are included to facilitate understanding and should be considered to be exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

The method, device, electronic device, and storage medium for generating test data according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

FIG. 1 is a schematic flowchart of a method for generating test data provided by an embodiment of the present disclosure.

As shown in Figure 1, the method consists of the following steps:

Step 101: In response to the test data generation instruction, obtain the test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include the target number of test data and a parameter weight list. The parameter weight list includes A list of keyword parameters for different data categories and a corresponding list of weights.

In the embodiment of this application, in order to construct test data of different data categories and generate test data more flexibly, different test data generation rules need to be written according to test requirements. The test data generation rules are written by testers according to test requirements, and are stored in the rule configuration file after writing. After receiving the test data generation instruction, the test data generation is obtained based on the rule configuration file in the generation instruction. rule. The test data generation rules include a target number of test data and a parameter weight list. The target number is the number of generated keyword groups. The parameter weight list includes a keyword parameter list of different data categories and a corresponding weight list.

In order to better understand the test data generation rules, embodiments of this application provide a test data generation rule as follows:

{"num":"5","crontab":"whether it is timed","attribute":{"emotion":["positive","neutral","negative"],"weight":[2,1 ,2]},"region":{"region":["Shanghai","Beijing","Nanjing"],"weight":[3,1,1]},"industry":{"industry": ["Sales","Finance","Computer"],"Weight":[1,2,2]}}.

Among them, the test data generation rules are input in the format of key-value pairs, ""num":"5"" means that the target number is 5, that is, the number of key phrases that need to be generated is 5; "" crontab":""Is timing"" used to set timing information,""attribute":{"emotion":["positive","neutral","negative"],"weight":[2,1,2 ]},"region":{"region":["Shanghai","Beijing","Nanjing"],"weight":[3,1,1]},"industry":{"industry":[" Sales","Finance","Computer"],"Weight":[1,2,2]}" is a list of parameter weights, which are keyword parameter lists and correspondences for different data categories representing emotions, regions, and industries. The weight list, each keyword parameter list corresponds to a weight list, the keyword parameter list of each data category contains different keyword parameters, and one keyword parameter corresponds to one weight.

Step 102: Based on the test data generation rules, generate corresponding keyword groups according to keyword parameter lists and corresponding weight lists of different data categories.

After obtaining the test data generation rules, in order to obtain target test data, it is also necessary to generate corresponding keyword groups based on keyword parameter lists of different data categories and corresponding weight lists. In order to better understand the keyword group, please continue to refer to the test data generation rules in step 101 above. Since the target number is 5, according to the test data generation rules, the keyword parameter list and corresponding weights based on different data categories are The list generates 5 keyword groups, such as ["Neutral+Beijing+Sales", "Positive+Shanghai+Finance", "Negative+Beijing+Computer", "Positive+Beijing+Finance", "Negative+Nanjing+Computer"] .

Step 103: Input the keyword group into the trained preset language model to obtain target test data corresponding to the keyword group.

In order to enrich the types of test data and meet the needs of a large amount of test data for testing, after obtaining the keyword group, the keyword group is input into the trained preset language model, and through the trained preset language model, we get The target test data corresponding to the keyword group. In order to better understand the target test data, the embodiment of this application provides samples of the target test data as shown in the following table:

Table 1

The test data generation method provided by the present disclosure responds to a test data generation instruction and obtains test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include a target number of test data and a parameter weight list. , the parameter weight list includes keyword parameter lists of different data categories and corresponding weight lists; based on the test data generation rules, corresponding keyword groups are generated according to keyword parameter lists of different data categories and corresponding weight lists; Input the keyword group into the trained preset language model to obtain target test data corresponding to the keyword group. The test data generation method provided by this disclosure weights keyword parameters of different data categories by writing test data generation rules, and implements the construction of test data according to the test data generation rules, so that the generated test data is more consistent with the distribution of production data. It not only meets the diversity of test data but also ensures the randomness of test data; and generates target test data based on keyword groups based on the trained preset language model to meet the need for a large amount of test data during the test process.

In the embodiment of the present application, in order to meet the diversity of generated test data and the generation of test data of different data types, before obtaining the test data generation rules based on the rule configuration file in the generation instruction, it is also necessary to according to the test requirements Write the test data generation rules as described.

In order to have a better understanding of writing the test data generation rules according to requirements, please refer to Figure 2, which is a schematic flow chart of writing test data generation rules provided by an embodiment of the present application.

As shown in Figure 2, the method consists of the following steps:

Step 201: In response to the configuration instruction for the rule configuration file, obtain the rule configuration file carried by the configuration instruction.

In order to better understand the method of obtaining the rule configuration file carried by the configuration instruction in response to the configuration instruction of the rule configuration file, please refer to Figure 3. Figure 3 is a rule provided by an embodiment of the present application. Schematic diagram of the configuration command page. As shown in Figure 3, the rule configuration instruction page includes four options: "Quantity", "Timing", "Rule Configuration File" and "Preset Template". The "quantity" is used to input the target quantity, the "timing" is used to set whether to send target test data in a timely manner, and the "rule configuration file" is used to write the test data generation rules. In some embodiments, The above rule configuration file is written in JSON format; the "preset template" is used to download the preset test data generation rule configuration template and after completing the writing of the test data generation rule, the rules written based on the template Configuration file is uploaded.

Step 202: Based on the rule configuration file, write test data generation rules according to the preset test data generation rule configuration template.

In the embodiment of this application, the configuration template of the preset test data generation rules is used to facilitate the writing of the test data generation rules. In order to better understand the configuration template of the preset test data generation rules, The configuration template of the preset test data generation rules is as follows:

{"num":"quantity","crontab":"whether to time","region":{"region":["region 1","region 2"],"weight":[weight 1, weight 2] },"industry":{"industry":["industry1","industry2"],"weight":[weight1,weight2]},"attribute":{"emotion":["emotion1" ,"emotion 2"],"weight":[weight 1,weight 2]},"type":{"type":["type 1","type 2"],"weight":[weight 1,weight 2]}.

After downloading the configuration template based on the rule configuration instruction page, the test data generation rules can be written through the configuration template. In addition to the content in the above configuration template, testers can also add keyword parameters of different data types in key-value format to the rule template according to their own testing needs to generate the specific data types they need. Test Data.

Step 203: Verify the test data generation rules.

After uploading the rule configuration file on the rule configuration instruction page, the system will verify the passed test data generation rules, for example, verify the legality of the keyword parameters, that is, whether the keyword parameters exist in the corresponding product category; Verify the accuracy of keyword parameters, that is, whether the content corresponding to the keyword parameters meets the input requirements of the background.

Step 204: After determining that the test data generation rule passes the verification, write the test data generation rule into a preset database.

If it is determined that the test data generation rule passes the verification, the test data generation rule is written into the preset database, and a prompt message of "keyword parameters written successfully" is displayed on the rule configuration instruction page. In some embodiments, the preset database may be a MySQL database.

Through the above method of writing test data generation rules, different categories of keyword parameters can be set according to the needs of testers; multiple different categories of test data can be weighted; and the keyword groups can be generated more flexibly.

As an implementation method of the above step 102, in order to generate the corresponding keyword groups according to the keyword parameter lists of different data categories and the corresponding weight lists, it is also necessary to construct the boundary values in the keyword parameters and prefix sum lists. The corresponding relationship, the specific implementation method includes: before generating the corresponding keyword group according to the keyword parameter list and the corresponding weight list of different data categories, obtaining the parameter weight list, in the parameter weight list, a key The word parameter corresponds to a weight value and a first subscript, and there is a mapping relationship between the first subscript and the keyword parameter; the first subscript is the storage of the keyword parameter in the parameter weight list. The subscript corresponding to the address; initialize the weight value in the weight list to obtain the corresponding prefix sum list. The prefix sum list is an orderly increasing list according to the size of the weight value and is used to represent the boundary of the weight value. , wherein the first subscript corresponds to the second subscript; each boundary value in the prefix sum list corresponds to a third subscript, and the third subscript corresponds to the second subscript Correspondence: establishing a mapping relationship between the first subscript and the third subscript based on the second subscript.

In order to better understand the above-mentioned prefix sum list and the boundary value of the prefix sum list, the embodiment of this application provides the following example: setting the parameter weight list to "region":{"region":["Beijing" ,"Shanghai", "Tianjin", "Shenzhen"], "weight": [1,2,3,4]}, the keyword list ["Beijing", "Shanghai", "Tianjin", "Shenzhen" ] The corresponding first subscripts are [0, 1, 2, 3] respectively, that is, the weight corresponding to the keyword parameter "Beijing" is 1, and the first subscript is 0; the weight list [1, 2, 3, 4] The corresponding second subscript corresponds to the first table below, and then the weight values in the weight list [1, 2, 3, 4] are initialized, that is, the weight list is cumulatively calculated in order, The corresponding prefix sum list [1, 3, 6, 10] is obtained. Therefore, the prefix sum list is an ordered increasing list according to the size of the sum of the weight values; and, each value in the prefix sum list is the right boundary value of the sum of the weight values, each right boundary value corresponds to a third subscript, and the third subscript corresponds to the second subscript. Therefore, based on the second subscript, A mapping relationship between the first subscript and the third subscript is established.

By establishing a mapping relationship between the first subscript and the third subscript, and simultaneously establishing a mapping relationship between the prefix sum list and the keyword parameter list, the call weight random selection can be called The algorithm generates random numbers and calls the binary search method to find the right boundary value corresponding to the random number in the prefix sum list, returns the third subscript corresponding to the right boundary value, and based on the prefix sum list and the keyword The mapping relationship between parameter lists finds the corresponding keyword parameters.

In order to have a better understanding of finding the corresponding keyword parameters based on the mapping relationship between the prefix sum list and the keyword parameter list, please refer to Figure 4, which is provided by the embodiment of the present application. A schematic diagram of a process for generating keyword phrases.

As shown in Figure 4, the method consists of the following steps:

Step 301: Call the weight random selection algorithm to generate a random number according to the weight value, where the random number is any number within the range of 1 to the maximum value of the boundary value in the prefix sum list.

In order to better understand the process of generating keyword phrases, please continue to refer to the test data generation rules provided in step 102 above. The test data generation rules are as follows:

{"num":"5","crontab":"whether it is timed","attribute":{"emotion":["positive","neutral","negative"],"weight":[2,1 ,2]},"region":{"region":["Shanghai","Beijing","Nanjing"],"weight":[3,1,1]},"industry":{"industry": ["Sales","Finance","Computer"],"Weight":[1,2,2]}}

The parameter weight list "region":{"region":["Shanghai","Beijing","Nanjing"],"weight":[3,1,1]} is based on the data category in the above test data generation rules. For example, the keyword parameter list is ["Shanghai", "Beijing", "Nanjing"], the corresponding first subscript is [0,1,2], and the corresponding weight list is [3,1,1] , then the prefix sum list obtained after initializing the weight list is [3,4,5], and the maximum value of the boundary value in the prefix sum list is 5. Therefore, the weight random selection algorithm is called, and according to the weight Value generates a random number, which is any number in the range of 1 to 5. The time complexity of each random selection is O(logn), and n is the sum of the weight values of the corresponding weight list.

Step 302: Call the binary search algorithm to respectively search for the target boundary value of the random number corresponding to the prefix sum list.

Generate random numbers in the range of 1 to 5 according to the method described in step 301 above. Taking the generated random number as 2 as an example, call the binary search algorithm to find the right boundary value of the random number 2 in the prefix sum list, so The right boundary value is 3.

Step 303: Obtain the third subscript corresponding to the target boundary value.

According to the above step 302, the third subscript corresponding to the right boundary value 3 of the random number 2 in the prefix sum list can be determined.

Step 304: According to the mapping relationship between the first subscript and the third subscript, respectively search for the first subscript corresponding to the third subscript.

According to the above step 303, it can be determined that the first subscript corresponding to the third subscript is 0.

Step 305: Search the corresponding keyword parameter from the keyword parameter list according to the first subscript.

According to the mapping relationship between keyword parameters and the first subscript, it can be found that the corresponding keyword parameter with a first subscript of 0 in the keyword parameter list is "Shanghai".

Step 306: Compose the keyword group according to the found keyword parameters.

Repeat the above steps 301 to 305 until the corresponding keyword parameters generated by the parameter weight list of each data category reach the target number. For example, when the target number is 5, then 5 parameter weight lists of each data category are generated. The corresponding first subscript, such as {"attribute":{"emotion":[1,0,2,0,2]},"region":{"region":[1,0,1,1,2 ]},"industry":{"industry":[0,1,2,1,2]}}, then the corresponding keyword parameters are {"attribute":{"emotion":[neutral, positive, negative ,positive,negative]},"region":{"region":[Beijing, Shanghai, Beijing, Beijing, Nanjing]},"industry":{"industry":[sales, finance, computer, finance, computer]} }; Compose the keyword group according to the found keyword parameters, such as ["Neutral+Beijing+Sales", "Positive+Shanghai+Finance", "Negative+Beijing+Computer", "Positive+Beijing+Finance", "Negative+Nanjing+Computer"], the target number is obtained, that is, 5 keyword groups.

In the above step 305, when it is detected that the number of keyword parameters reaches the target number, the random selection algorithm is stopped to be called to generate the random numbers.

By calling the weighted random selection algorithm above and randomly generating keyword parameters according to the weight values corresponding to the keyword parameters and combining them to obtain keyword groups, the random distribution of multiple parameters and types of test data can be achieved, simulating the production environment. data situation.

In order to obtain a trained preset language model, such as a trained GPT-2 model and other language models, the preset language model also needs to be trained. The training method includes the following steps:

Step 1. Obtain training text data of different data categories from the preset database. The training text data has been marked with the corresponding emotion type attribute, region, and industry.

Step 2. Filter the text data for training according to preset filtering rules to obtain the training keywords of the target data category. The preset filtering rules include removing text data of the preset data category based on regular expression rules. The preset filtering rules include removing noise data based on regular expression rules, such as (1) removing URL data, html format, etc. in the text, (2) removing pictures and emoji expression data in the text, (3) removing text in the text. spaces, newlines and other meaningless data, (4) remove text content that is too long or too short.

In addition, some sensitive data are filtered through blacklists, such as (1) removing swear words and sensitive words. (2) Remove data containing personally identifiable information.

Step 3. Input the training keywords of the target data category into the preset language model for training, and obtain the trained preset language model.

By training the preset language model, for example, training the GPT-2 model, and then inputting the obtained keyword phrase into the trained preset language model, the corresponding text data is obtained as the target test data, so that the target test The data content is richer and meets the needs of a large amount of test data for testing.

In order to better save the target test data, after obtaining the target test data corresponding to the keyword group, it is also necessary to read the scheduled transmission information in the test data generation rules to determine whether to send the target test data in a timely manner; In the case where it is determined to send the target test data regularly, when the timing sending conditions are met, the target test data will be sent to the target database of the corresponding data category according to the data category of the keyword parameter; if the If the number of target test data sent is consistent with the number of target test data received by the target database, the transmission of the target test data will be completed and the transmission success indicator will be returned; if it is monitored that the number of target test data received by the target database is consistent with the target number If they are inconsistent, it is determined that the target data transmission is abnormal, and a transmission failure identification is returned to prompt the tester.

To sum up, the embodiments of the present disclosure can achieve the following effects:

1. Write test data generation rules based on the rule configuration file, which can set different categories of keyword parameters according to the needs of testers; can achieve weighting of multiple different categories of test data; and can generate the keyword groups more flexibly.

2. By calling the weight random selection algorithm and randomly generating keyword parameters according to the weight values corresponding to the keyword parameters and combining them to obtain the keyword group, random distribution of multiple parameters and multiple types of test data can be achieved, simulating the production environment. data situation.

3. Obtain a trained preset language model by training the preset language model, input the obtained keyword phrase into the trained preset language model, and obtain the corresponding text data as the target test data, so that the target test The data content is richer and meets the needs of a large amount of test data for testing.

Corresponding to the above parameter calibration method of the automobile thermal management system, the present invention also proposes a parameter calibration device of the automobile thermal management system. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be described again in the present invention.

Corresponding to the above-mentioned test data generation method, the present invention also proposes a test data generation device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be described again in the present invention.

Figure 5 is a schematic structural diagram of a test data generation device provided by an embodiment of the present disclosure. As shown in Figure 5, it includes: an acquisition unit 41, a generation unit 42, and an input unit 43.

The acquisition unit 41 is configured to respond to a test data generation instruction and acquire test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include a target quantity of test data and a parameter weight list. The parameters The weight list includes a keyword parameter list of different data categories and a corresponding weight list;

The generation unit 42 is configured to generate corresponding keyword groups based on the test data generation rules according to the keyword parameter lists of different data categories and the corresponding weight lists;

The input unit 43 is used to input the keyword group into a trained preset language model to obtain target test data corresponding to the keyword group.

Further, in a possible implementation of this embodiment, as shown in Figure 6, before the acquisition unit 41, the device further includes a writing unit 44, and the writing unit 44 includes:

The first acquisition module 441 is configured to obtain the test data generation rules based on the rule configuration file in the generation instruction in response to the generation instruction of the test data, and obtain the test data generation rules in response to the configuration instruction of the rule configuration file. The rule configuration file carried by the above configuration instructions;

Writing module 442, configured to write test data generation rules according to the configuration template of the preset test data generation rules based on the rule configuration file;

Verification module 443, used to verify the test data generation rules;

The writing module 444 is configured to write the test data generation rules into a preset database after determining that the test data generation rules pass the verification.

Further, in a possible implementation of this embodiment, as shown in Figure 6, before the generating unit 42, the device further includes an establishing unit 45, and the establishing unit 45 includes:

The second acquisition module 451 is configured to obtain the parameter weight list before generating corresponding keyword groups based on the keyword parameter lists and corresponding weight lists of different data categories based on the test data generation rules, where, A keyword parameter corresponds to a weight value and a first subscript, and there is a mapping relationship between the first subscript and the keyword parameter; the first subscript is the keyword parameter stored in the parameter weight list. The subscript corresponding to the storage address;

Initialization module 452 is used to initialize the weight values in the weight list to obtain the corresponding prefix sum list. The prefix sum list is an ordered increasing list according to the size of the sum of the weight values and is used to represent the weight value. boundary, wherein the first subscript corresponds to the second subscript; each boundary value in the prefix sum list corresponds to a third subscript, and the third subscript corresponds to the second subscript. Subscript correspondence;

The establishment module 453 is configured to establish a mapping relationship between the first subscript and the third subscript based on the second subscript.

Further, in a possible implementation of this embodiment, as shown in Figure 6, the generating unit 42 includes:

The generation module 421 is used to call the weight random selection algorithm and generate a random number according to the weight value, where the random number is any number within the range of 1 to the maximum value of the boundary value in the prefix sum list;

The first search module 422 is used to call a binary search algorithm to respectively search for the target boundary value of the random number corresponding to the prefix sum list;

The third obtaining module 423 is used to obtain the third subscript corresponding to the target boundary value respectively;

The second search module 424 is configured to respectively search for the first subscript corresponding to the third subscript according to the mapping relationship between the first subscript and the third subscript;

The third search module 425 is configured to search for the corresponding keyword parameter from the keyword parameter list according to the first subscript;

The combination module 426 is used to combine the keyword groups according to the found keyword parameters.

Further, in a possible implementation of this embodiment, the combination module 426 is also used to:

When it is detected that the number of keyword parameters reaches the target number, stop calling the random selection algorithm to generate the random number.

Further, in a possible implementation of this embodiment, the device further includes a training unit 46. As shown in Figure 6, the training unit 46 includes:

The fourth acquisition module 461 is used to acquire training text data of different data categories from the preset database;

The filtering module 462 is configured to filter the text data for training according to preset filtering rules to obtain the training keywords of the target data category. The preset filtering rules include removing text of the preset data category based on regular expression rules. data;

The training module 463 is used to input the training keywords of the target data category into the preset language model for training, and obtain the trained preset language model.

Further, in a possible implementation of this embodiment, the device further includes a monitoring unit 47, and the monitoring unit 47 includes:

The judgment module 471 is used to input the keyword group into the preset language model and obtain the target test data corresponding to the keyword group, read the timed sending information in the test data generation rule, and judge whether to send the timed message. target test data;

Sending module 472, configured to send the target test data to the target database of the corresponding data category according to the data category of the keyword parameter when the timing sending conditions are met when it is determined to send the target test data at a scheduled time. ;

The first monitoring module 473 is configured to complete the transmission of the target test data if it is monitored that the number of sent target test data is consistent with the received number of the target database;

The second monitoring module 474 is configured to determine that the target data transmission is abnormal if it is detected that the number of target test data received by the target database is inconsistent with the target number.

The test data generation device provided by the present disclosure responds to the test data generation instruction and obtains the test data generation rules based on the rule configuration file in the generation instruction. The test data generation rules include the target number of test data and the parameter weight list. , the parameter weight list includes keyword parameter lists of different data categories and corresponding weight lists; based on the test data generation rules, corresponding keyword groups are generated according to keyword parameter lists of different data categories and corresponding weight lists; Input the keyword group into the trained preset language model to obtain target test data corresponding to the keyword group. The test data generation method provided by this disclosure weights keyword parameters of different data categories by writing test data generation rules, and implements the construction of test data according to the test data generation rules, so that the generated test data is more consistent with the distribution of production data. It not only meets the diversity of test data but also ensures the randomness of test data; and generates target test data based on keyword groups based on the trained preset language model to meet the need for a large amount of test data during the test process.

It should be noted that the foregoing explanation of the method embodiment also applies to the device of this embodiment. The principles are the same and are no longer limited in this embodiment.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

7 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in Figure 7, the device 500 includes a computing unit 501, which can be loaded into a RAM (Random Access Memory) according to a computer program stored in a ROM (Read-Only Memory) 502 or from a storage unit 508. The computer program in the memory) 503 to perform various appropriate actions and processes. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. Computing unit 501, ROM 502 and RAM 503 are connected to each other via bus 504. An I/O (Input/Output, input/output) interface 505 is also connected to the bus 504.

Multiple components in the device 500 are connected to the I/O interface 505, including: input unit 506, such as a keyboard, mouse, etc.; output unit 507, such as various types of displays, speakers, etc.; storage unit 508, such as a magnetic disk, optical disk, etc. ; and communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through computer networks such as the Internet and/or various telecommunications networks.

Computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include but are not limited to CPU (Central Processing Unit, Central Processing Unit), GPU (Graphic Processing Units, Graphics Processing Units), various dedicated AI (Artificial Intelligence, artificial intelligence) computing chips, various running The computing unit of the machine learning model algorithm, DSP (Digital Signal Processor, digital signal processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the test data generation method. For example, in some embodiments, the test data generating method may be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 508 . In some embodiments, part or all of the computer program may be loaded and/or installed onto device 500 via ROM 502 and/or communication unit 509 . When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the aforementioned test data generation method in any other suitable manner (eg, by means of firmware).

Various implementations of the systems and technologies described above in this article can be implemented in digital electronic circuit systems, integrated circuit systems, FPGA (Field Programmable Gate Array, field programmable gate array), ASIC (Application-Specific Integrated Circuit, application-specific integrated circuit) , ASSP (Application Specific Standard Product, dedicated standard product), SOC (System On Chip, system on chip), CPLD (Complex Programmable Logic Device, complex programmable logic device), computer hardware, firmware, software, and/or implemented in their combination. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions specified in the flowcharts and/or block diagrams/ The operation is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, laptop disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory, erasable programmable read-only memory) Or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory, portable compact disk read-only memory), optical storage device, magnetic storage device, or any suitable combination of the above.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, CRT (Cathode-Ray Tube, cathode ray tube) or LCD (Cathode-Ray Tube)) for displaying information to the user. Liquid Crystal Display (liquid crystal display) monitor); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., A user's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

Computer systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, also known as cloud computing server or cloud host. It is a host product in the cloud computing service system to solve the problem of traditional physical host and VPS service ("Virtual Private Server", or "VPS" for short) Among them, there are defects such as difficult management and weak business scalability. The server can also be a distributed system server or a server combined with a blockchain.

Among them, it should be noted that artificial intelligence is the study of using computers to simulate certain human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It has both hardware-level technology and software-level technology. Artificial intelligence hardware technology generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning/depth Learning, big data processing technology, knowledge graph technology and other major directions.

It should be understood that various forms of the process shown above may be used, with steps reordered, added or deleted. For example, each step described in the present disclosure can be executed in parallel, sequentially, or in a different order. As long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, there is no limitation here.

The above-mentioned specific embodiments do not constitute a limitation on the scope of the present disclosure. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this disclosure shall be included in the protection scope of this disclosure.

Claims (10)

1. A method of generating test data, comprising:

responding to a generation instruction of test data, and acquiring a test data generation rule based on a rule configuration file in the generation instruction, wherein the test data generation rule comprises target quantity of the test data and a parameter weight list, and the parameter weight list comprises keyword parameter lists of different data types and corresponding weight lists;

based on the test data generation rule, generating corresponding key word groups according to the key word parameter lists of different data types and the corresponding weight lists respectively;

and inputting the keyword group into a trained preset language model to obtain target test data corresponding to the keyword group.

2. The method of claim 1, wherein prior to the generating instruction responsive to the test data, obtaining the test data generation rule based on the rule profile in the generating instruction, the method comprises:

responding to a configuration instruction of the rule configuration file, and acquiring the rule configuration file carried by the configuration instruction;

writing test data generating rules according to a configuration template of a preset test data generating rule based on the rule configuration file;

Checking the test data generation rule;

and after the test data generation rule is confirmed to pass the verification, writing the test data generation rule into a preset database.

3. The method of claim 1, wherein before the generating the corresponding keyword groups based on the test data generating rule according to the keyword parameter list and the corresponding weight list of different data categories, respectively, the method further comprises:

acquiring the parameter weight list, wherein one keyword parameter corresponds to a weight value and a first subscript, and the first subscript and the keyword parameter have a mapping relation; the first subscript is a subscript corresponding to a storage address storing the keyword parameters in the parameter weight list;

initializing weight values in the weight list to obtain corresponding prefixes and a list, wherein the prefixes and the list are sequentially increased lists according to the added sizes of the weight values and are used for representing boundaries of the weight values, and the first subscript corresponds to the second subscript; each boundary value in the prefix and list corresponds to a third subscript, and the third subscript corresponds to the second subscript;

And establishing a mapping relation between the first subscript and the third subscript based on the second subscript.

4. The method of claim 3, wherein generating the corresponding keyword groups based on the test data generation rule according to the keyword parameter list and the corresponding weight list of the different data categories, respectively, comprises:

invoking a weight random selection algorithm, and generating a random number according to the weight value, wherein the random number is any number from 1 to the maximum value range of the prefix and the boundary value in the list;

invoking a binary search algorithm to search target boundary values of the random numbers corresponding to the prefixes and the list respectively;

respectively obtaining third subscripts corresponding to the target boundary values;

according to the mapping relation between the first subscript and the third subscript, searching the first subscript corresponding to the third subscript respectively;

searching corresponding keyword parameters from the keyword parameter list according to the first subscript;

and synthesizing the keyword group according to the searched keyword parameter group.

5. The method of claim 4, wherein the searching for the corresponding keyword parameter from the keyword parameter list based on the first subscript comprises:

And stopping calling a random selection algorithm to generate the random number when the number of the keyword parameters reaches the target number.

6. The method according to claim 2, wherein the training method of the preset language model comprises:

acquiring training text data of different data types from a preset database;

filtering the training text data according to a preset filtering rule to obtain training keyword groups of target data types, wherein the preset filtering rule comprises the step of removing text data of preset data types based on a regular expression rule;

and inputting the training keyword group of the target data category into a preset language model for training to obtain a trained preset language model.

7. The method according to claim 1, wherein after inputting the keyword group into a preset language model to obtain target test data corresponding to the keyword group, the method comprises:

reading timing sending information in the test data generation rule, and judging whether to send the target test data at a timing;

under the condition that the target test data are sent at fixed time, when the fixed-time sending condition is met, the target test data are respectively sent to a target database of the corresponding data category according to the data category of the keyword parameter;

If the sending number of the target test data is monitored to be consistent with the receiving number of the target database, completing the transmission of the target test data;

and if the quantity of the target test data received by the target database is not consistent with the target quantity, determining that the target data transmission is abnormal.

8. A test data generating apparatus, comprising:

the first acquisition unit is used for responding to a generation instruction of the test data, acquiring a test data generation rule based on a rule configuration file in the generation instruction, wherein the test data generation rule comprises target quantity of the test data and a parameter weight list, and the parameter weight list comprises keyword parameter lists of different data types and corresponding weight lists;

the generating unit is used for generating corresponding keyword groups according to the keyword parameter lists of different data categories and the corresponding weight lists respectively based on the test data generating rule;

and the input unit is used for inputting the keyword group into a trained preset language model to obtain target test data corresponding to the keyword group.

9. An electronic device, comprising:

At least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.