CN115373974A

CN115373974A - Test data construction method and device and electronic equipment

Info

Publication number: CN115373974A
Application number: CN202110558872.XA
Authority: CN
Inventors: 李变; 陈佃晓
Original assignee: NetsUnion Clearing Corp
Current assignee: NetsUnion Clearing Corp
Priority date: 2021-05-21
Filing date: 2021-05-21
Publication date: 2022-11-22

Abstract

The embodiment of the specification discloses a test data construction method and device and electronic equipment. The test data construction method comprises the following steps: acquiring attribute characteristic information of service data in a production environment, wherein the attribute characteristic information comprises characteristic values corresponding to the service data in a plurality of attribute characteristics respectively; performing clustering training on characteristic values corresponding to the single attribute characteristics respectively based on the service data to obtain categories corresponding to the service data respectively under a plurality of attribute characteristics; inputting attribute feature information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, wherein the scene recognition model is obtained by training based on the attribute feature information of the sample service data and a corresponding scene label, and the scene label is used for indicating the service scene to which the sample service data belongs; and constructing test data corresponding to the service scene to which the service data belongs on the basis of the service data and the classes respectively corresponding to the service data under the attribute characteristics.

Description

Test data construction method and device and electronic equipment

Technical Field

The present disclosure relates to the field of testing technologies, and in particular, to a method and an apparatus for constructing test data, and an electronic device.

Background

In the development and online process of software products, the products are usually tested by using test data, so as to ensure that quality problems do not occur after the products are online.

In the current testing process, the testing is usually divided and respectively performed according to the business modules, specifically, for each business module, a tester constructs the testing data required by the current business module based on the concerned fields, and for other fields which are not concerned, a random filling mode is adopted. However, the test data constructed in this way is not comprehensive enough, cannot meet the test requirements of the test scene, affects the accuracy and reliability of the test result, and is easy to cause the test data to be disordered, so that various problems occur in the test environment due to dirty data, the problems are time-consuming and labor-consuming to troubleshoot, and finally the stability and the test efficiency of the test environment are affected.

Therefore, a testing scheme capable of improving the testing efficiency and the accuracy and reliability of the testing result is needed.

Disclosure of Invention

Embodiments of the present disclosure provide a test data construction method and apparatus, and an electronic device, which can improve test efficiency and accuracy and reliability of a test result.

In order to achieve the above purpose, the embodiments of the present specification adopt the following technical solutions:

in a first aspect, a test data construction method is provided, including:

acquiring attribute feature information of service data in a production environment, wherein the attribute feature information comprises feature values corresponding to the service data in a plurality of attribute features respectively;

performing clustering training on the basis of characteristic values of the service data corresponding to the single attribute characteristics respectively to obtain categories of the service data corresponding to the attribute characteristics respectively;

inputting attribute feature information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, wherein the scene recognition model is obtained by training based on the attribute feature information of sample service data and a scene label corresponding to the sample service data, and the scene label is used for indicating the service scene to which the sample service data belongs;

and constructing test data corresponding to the service scene to which the service data belongs on the basis of the service data and the classes respectively corresponding to the service data under the attribute characteristics.

In a second aspect, there is provided a test data construction apparatus comprising:

the system comprises a first acquisition unit, a second acquisition unit and a processing unit, wherein the first acquisition unit is used for acquiring attribute characteristic information of service data in a production environment, and the attribute characteristic information comprises characteristic values corresponding to a plurality of attribute characteristics of the service data respectively;

the single-feature training unit is used for performing clustering training on feature values of the service data corresponding to the single attribute features respectively to obtain categories of the service data corresponding to the attribute features respectively;

the scene recognition unit is used for inputting the attribute characteristic information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, wherein the scene recognition model is obtained by training based on the attribute characteristic information of the sample service data and a scene label corresponding to the sample service data, and the scene label is used for indicating the service scene to which the sample service data belongs;

and the construction unit is used for constructing test data corresponding to the service scene to which the service data belongs on the basis of the service data and the classes respectively corresponding to the service data under the attribute characteristics.

In a third aspect, an electronic device is provided, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

In a fourth aspect, a computer-readable storage medium is provided that stores one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to:

acquiring attribute feature information of service data in a production environment, wherein the attribute feature information comprises feature values corresponding to a plurality of attribute features of the service data respectively;

performing clustering training on the basis of feature values of the service data corresponding to the single attribute feature respectively to obtain categories of the service data respectively corresponding to the attribute features;

inputting attribute feature information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, wherein the scene recognition model is obtained by training based on the attribute feature information of the sample service data and a scene label corresponding to the sample service data, and the scene label is used for indicating the service scene to which the sample service data belongs;

According to the scheme of the embodiment of the specification, the business data in the real production environment is combed and aggregated based on the attribute feature information through modes such as artificial intelligence and machine learning, the business scene to which the business data belong and the categories corresponding to the multiple attribute features are obtained respectively, and further the automatic construction of the test data corresponding to the business scene to which the business data belong is realized according to the business scene and the categories corresponding to the single attribute features. In addition, the whole construction process does not need manual intervention, and the testing efficiency can be improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the principles of the specification and not to limit the specification in a limiting sense. In the drawings:

FIG. 1 is a schematic flow chart diagram of a test data construction method provided in one embodiment of the present description;

FIG. 2 is a flow chart illustrating a method for constructing test data according to another embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a service testing method according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a test data construction apparatus according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a service testing apparatus according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present specification.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in this description shall fall within the scope of protection of this document.

As described above, in the current testing process, the testing is usually divided and respectively performed according to the business modules, specifically, for each business module, the tester constructs the testing data required by the current business module based on the concerned fields, and randomly fills in other fields that are not concerned. For example, the payment system mainly includes business modules such as a transaction module, a settlement module, and an account checking module, and when testing, test data required by the transaction module is constructed based on transaction-related fields, test data required by the settlement module is constructed based on the account-related fields, and test data required by the account checking module is constructed based on the account checking-related fields. However, the test data constructed in this way is not comprehensive enough, data really needed by a test scene is easily missed, accuracy and reliability of a test result are affected, the test data is also easily disordered, various problems occur in a test environment due to dirty data, the problems are time-consuming and labor-consuming to troubleshoot, and finally stability and test efficiency of the test environment are affected.

Therefore, embodiments of the present specification aim to provide a service data construction scheme, which identifies a service scenario to which service data belongs and classifies the service data under a single attribute feature based on attribute feature information of the service data in a real production environment, and further implements an automatic construction of test data corresponding to the service scenario to which the service data belongs according to the service scenario to which the service data belongs and a category corresponding to the single attribute feature.

It should be understood that the test data constructing method provided in the embodiments of the present specification may be executed by an electronic device or software installed in the electronic device, and specifically may be executed by a terminal device or a server device.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

Referring to fig. 1, a flow chart of a test data constructing method provided for an embodiment of the present disclosure may include:

s102, obtaining attribute characteristic information of the service data in the production environment, wherein the attribute characteristic information comprises characteristic values corresponding to the service data in a plurality of attribute characteristics respectively.

The attribute features may be a plurality of different features representing attributes of the service data, and may be specifically set according to service requirements. Optionally, the plurality of attribute characteristics may include types of service participants and supported service types. For example, taking a transaction-type service as an example, a service participant is a transaction participant, which may specifically include but is not limited to a transaction initiator, a payer and a payee, the type of the service participant may include a third party institution, a bank, and the like, and the type of the service may include a quick payment, a gateway payment, and the like. Table 1 shows an example of attribute characteristic information of one type of service data.

TABLE 1

Service data	Service initiator	Payment party	Cashier's party	Transaction types supported
					Service data 1	Third party organization	Third party organization	Third party organization	Quick transaction
Service data 2	Bank	Third party organization	Bank	Gateway transactions

The service data may comprise service-related data, wherein the service data differs from service to service. For example, in the above transaction-based service, the service data may include the transaction amount and transaction price of the commodity, the inventory amount, the warehouse where the commodity is located, the transaction amount and transaction amount between transaction participants, and the like.

In specific implementation, the business data may be obtained from a database (hereinafter referred to as "production database") of the online production environment, and the production database stores the business data generated in real time during the business processing. In an optional implementation manner, considering that service data generated in the service processing process is changed in real time, service data in a production environment in a predetermined historical time period before the current time can be acquired, and the acquired service data has better real-time performance, so that test data constructed subsequently can better meet the test requirement of the current real service scene.

And S104, performing clustering training respectively based on the feature values of the service data corresponding to the single attribute features to obtain the categories of the service data respectively corresponding to the plurality of attribute features.

By using a machine learning method, the service data with the same characteristic value corresponding to a single attribute feature are subjected to cluster training, so that the corresponding categories of the service data under the single attribute feature can be determined. In other words, the feature values of the acquired service data corresponding to the single attribute features are clustered, and the service data are finely classified according to the single attribute features, so that the specific categories of the service data corresponding to the single attribute features are determined, and the process from rough classification to fine classification of the service data is completed. Specifically, in an optional implementation, the step S104 may include:

step 1, aggregating the service data respectively based on the characteristic values of the service data corresponding to the single attribute characteristics to obtain the service data of the single attribute characteristics corresponding to different characteristic values.

For example, taking the attribute feature of the transaction initiator shown in table 1 as an example, by aggregating the service data of the transaction initiator as the third-party organization, the service data of the transaction initiator as the third-party organization can be obtained; by aggregating the business data of which the transaction initiator is a bank, the business data of which the transaction initiator is the bank can be obtained.

And 2, clustering the service data corresponding to the single attribute characteristic in different characteristic values based on a preset clustering algorithm to obtain the corresponding category of the service data in the single attribute characteristic.

And aiming at each characteristic value of a single attribute characteristic, clustering the service data corresponding to the characteristic value by adopting a preset clustering algorithm to obtain a plurality of characteristic value cluster clusters, wherein each characteristic value cluster is a category. For example, still taking the attribute feature of the transaction initiator shown in table 1 as an example, the service data corresponding to the feature value of the third-party organization is clustered to obtain a cluster including multiple categories such as the million-transaction-volume level and the million-transaction-volume level, and further, based on the cluster to which each service data belongs, the category corresponding to each service data under the attribute feature of the transaction initiator is determined.

The preset clustering algorithm may include, for example, a combination of one or more of the following algorithms: a K-means algorithm, a K-medoid algorithm, a Principal Component Analysis (PCA) algorithm, and the like, which may be specifically set according to business needs, and this is not specifically limited in this embodiment of the present specification.

The following describes the process of performing cluster training on the attribute feature of the transaction initiator in detail by taking the K-means algorithm as an example. First, the same service data of the transaction initiator are aggregated to obtain the number of service data included in each transaction party, and it is assumed that the mechanism and the number of service data included in the mechanism shown in table 2 below are obtained.

TABLE 2

Third party organization	Amount of included service data
		Mechanism A	5
Mechanism B	20
		Mechanism C	11
Mechanism D	5
		Mechanism E	9
Mechanism F	19
		Mechanism G	30
Mechanism H	3
		Mechanism I	15

And then, randomly dividing the mechanisms A to I into K groups, and calculating the average value of the number of the service data of each group, wherein K can be set according to actual needs. Assuming K =3, the initial grouping is as follows:

group 1: the system comprises a mechanism A, a mechanism B and a mechanism C, wherein the average value of the number of the service data of the group is 12;

group 2: a mechanism D, a mechanism E and a mechanism F, wherein the average value of the number of the service data of the group is 11;

group 3: the system comprises a mechanism G, a mechanism H and a mechanism I, wherein the average value of the number of the service data of the group is 16.

Further, for each organization, the organization is reassigned to a group of the average value of the number of the service data which is closest to the number of the service data contained therein, and the average value of the number of the service data of each group is recalculated. Taking the above initial grouping as an example, the organization a (containing 5 pieces of service data) may be reassigned to the 2 nd group (average value of service data amount is 11), and the like, and a new grouping is obtained as follows:

group 1: the mean value of the quantity of the service data of the group is 10;

group 2: the mean value of the quantity of the service data of the group is 4.33;

group 3: the average value of the number of the service data of the group is 21.

Repeating the regrouping step until the variation of the average value of the service data quantity of each group is within a preset range, thereby obtaining a final grouping result. And finally, determining the category of each group based on the number of the service data contained in each group and the preset corresponding relation between the number of the service data and the category, wherein the category of each group is the category to which the service data in each group belongs under the attribute characteristic of a transaction initiator.

It should be noted that, in the above example, only the feature value of the third party organization at the initiation of the transaction is subjected to clustering training by using the K-means algorithm, and for other attribute features and feature values, similar methods may be used to perform clustering training to obtain corresponding categories of the service data under other attribute features.

It can be understood that, through the above-mentioned clustering training mode, firstly, the business data is aggregated according to the characteristic value of the business data corresponding to the single attribute characteristic, and then, the business data corresponding to the single attribute characteristic at different characteristic values is clustered, so that the automatic carding of the attribute characteristic information of the business data can be realized by using machine learning, and the corresponding category of the obtained business data under the single attribute characteristic can more intuitively and accurately reflect the business characteristics of the real production environment.

Of course, in the test data construction method provided in the embodiment of the present specification, any other suitable manner may also be adopted to perform cluster training on feature values of the service data corresponding to a single attribute feature, and this is not specifically limited in the embodiment of the present specification.

And S106, inputting the attribute characteristic information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs.

The scene recognition model is obtained by training based on attribute feature information of the sample service data and scene labels corresponding to the sample service data. And the scene label corresponding to the sample service data is used for indicating the service scene to which the sample service data belongs.

The business process executed by the business system comprises at least one business scene, and the contained business scene is different according to different business processes. For example, a payment business process may include creating transactions and business scenarios such as payment, settlement, and billing. Table 3 shows an example of attribute feature information of service data and a service scenario to which the same belongs.

TABLE 3

And S108, constructing test data corresponding to the service scene to which the service data belongs based on the service data and the classes respectively corresponding to the service data under the plurality of attribute characteristics.

In order to ensure the orderliness of the constructed test data and avoid various problems of the test environment due to dirty data, thereby ensuring the stability and the test efficiency of the test environment, the service data can be aggregated according to the categories of the service data respectively corresponding to a plurality of attribute characteristics, so as to obtain typical test data in the service scene to which the service data belongs.

Specifically, in an alternative embodiment, the step S108 may include:

step 1, based on the categories of the service data respectively corresponding to the multiple attribute characteristics, aggregating the service data corresponding to the same category combination to obtain the service data corresponding to different category combinations.

The category combination refers to a combination of categories corresponding to a plurality of attribute features. And aggregating the acquired service data according to the category combination to obtain the service data corresponding to different category combinations.

Taking the above-mentioned multiple attribute characteristics including the transaction initiator, payer, payee, and supported transaction types as an example, table 4 shows an illustration of a different category combination.

TABLE 4

And 2, constructing test data corresponding to the same category combination based on the service data corresponding to the same category combination.

Specifically, the service data corresponding to the same category combination may be used as the test data corresponding to the same category combination.

And 3, determining the test data corresponding to the constructed different types of combinations as the test data corresponding to the service scene to which the service data belongs.

For each service scenario, the corresponding test data of the same service scenario can be combined in different categories to determine the test data corresponding to the service scenario. For example, taking the category combination shown in table 4 as an example, the service data corresponding to the service scenario one is aggregated according to the category combination, and the test data corresponding to the service scenario one is obtained as shown in table 5 below.

TABLE 5

By the method, the test data under the same service scene can be finely classified in different types of combinations, the alignment of the test data can be realized, various problems of the test environment due to dirty data are avoided, manual troubleshooting is reduced, the stability and the test efficiency of the test environment are guaranteed, the comprehensiveness of the test data under the same service scene can be improved, and the accuracy and the reliability of test results are improved. Of course, in the test data construction method provided in the embodiment of the present specification, any other appropriate technical means commonly used in the art may also be adopted to implement the construction of the test data, and this is not specifically limited in the embodiment of the present specification.

By adopting the test data construction method provided by the embodiment of the specification, the business data in the real production environment is combed and aggregated based on the attribute characteristic information through modes of artificial intelligence, machine learning and the like, the business scene to which the business data belongs and the categories respectively corresponding to a plurality of attribute characteristics are respectively obtained, and the automatic construction of the test data corresponding to the business scene to which the business data belongs is further realized according to the business scene and the categories corresponding to the single attribute characteristics. In addition, the whole construction process does not need manual intervention, and the testing efficiency can be improved.

Further, in another embodiment of the present specification, in order to more conveniently obtain test data in a subsequent test process and further improve the comprehensiveness of the test data, as shown in fig. 2, after the test data corresponding to the service scenario to which the service data belongs is constructed through the above S108, the test data construction method provided in any of the above embodiments further includes a step of aggregating the test data of the local stock and the newly constructed test data to generate a test database, and thus, in a subsequent test, required test data may be obtained from the test database.

Specifically, after S108, the test data constructing method provided in the embodiment of the present specification further includes: aggregating the constructed test data and the locally stored test data according to the service scenes to obtain test data sets corresponding to different service scenes; determining feature values respectively corresponding to the service data under the plurality of attribute features as scene features of a service scene to which the service data belongs; and generating a test database based on the test data sets and the scene characteristics respectively corresponding to different service scenes.

It can be understood that, by the test data constructing method provided in this embodiment, the generated test database stores test data sets corresponding to different service scenarios, so that dynamic maintenance of the test data corresponding to different service scenarios can be achieved, and in a subsequent test process, part or all of the test data in the service scenario can be acquired from the test database based on the scenario characteristics of the service scenario to be tested, so as to further improve the test efficiency and the accuracy and reliability of the test result.

Further, in another embodiment of the present specification, as shown in fig. 2, after the generating the test database, the test data constructing method provided by any one of the above embodiments further includes: and acquiring target test data matched with the test scene based on the scene characteristics of the test scene provided by the tester. Specifically, the test data construction method provided in any of the above embodiments further includes: receiving a data acquisition request sent by a tester, wherein the data acquisition request is used for requesting to acquire test data and carries scene characteristics of a test scene; accordingly, in response to the data acquisition request, target test data matched with the test scene is acquired from the test database based on the scene characteristics of the test scene, and the target test data is fed back to the tester.

More specifically, the scene characteristics of the test scene may be matched with the scene characteristics of each service scene in the test database, the service scene related to the test scene may be determined based on the matching result, and the test data in the test data set corresponding to the related service scene may be determined as the target test data matched with the test scene.

For example, taking the test data shown in table 5 above as an example, assume that the scenario characteristic of the test scenario is "transaction initiator: a third party authority; the payer: a third party authority; the payee: a third party authority; the transaction type: quick transaction ", it may be determined that the target test data matching the test scenario includes test data 1 and test data 2.

It can be understood that by the test data construction method provided by this embodiment, a tester can acquire test data matched with a test scenario only by providing scenario features of the test scenario, so that automation and targeted pushing of the test data are realized, the test data are ensured to meet requirements of the test scenario, and accuracy and reliability of test results are further improved.

Further, the test data construction method provided in any of the above embodiments further includes a training method for the scene recognition model, and it should be noted that the training method for the scene recognition model is performed in advance according to the sample service data collected from the production environment and/or the test environment and the corresponding scene label thereof, for example, before S106, and in the process of constructing the test data, it is not necessary to train the scene recognition model each time, or the scene recognition model may be periodically updated based on the sample service data collected from the production environment and/or the test environment center and the corresponding scene label thereof.

Specifically, the training method for the scene recognition model comprises the following steps: and obtaining attribute characteristic information of the sample service data and a scene label corresponding to the sample service data, and training by taking the attribute characteristic information of the sample service data as input and taking the scene label corresponding to the sample service data as output to obtain a scene recognition model. In practical applications, the sample service data may be historical service data obtained from a production environment.

The method has the advantages that the scene recognition model is trained in the above mode, so that the trained scene recognition model can quickly and accurately recognize the service scene to which the service data belongs based on the attribute characteristic information of the service data, and powerful support can be provided for the subsequent construction of test data.

Further, in order to avoid the leakage of the sensitive information in the service data and realize the reliable protection of the sensitive information, the sample service data and the attribute feature information thereof used for training the scene recognition model may be desensitized, that is, the scene recognition model is obtained by training based on the attribute feature information of the sample service data after desensitization and the corresponding scene label thereof. Accordingly, as shown in fig. 2, after the attribute feature information of the service data is acquired in S102, desensitization processing is performed on the service data and the attribute feature information thereof. The desensitization strategy adopted for desensitization processing of the sample service data and the attribute characteristic information thereof can be the same as that adopted for desensitization processing of the service data and the attribute characteristic information thereof.

It should be noted that the data construction method provided in the embodiments of the present specification may be performed in advance before the test, or may be performed periodically based on the newly acquired service data and the attribute feature information thereof from the production environment, so as to further improve the accuracy and reliability of the subsequent test result.

Correspondingly to the above method, an embodiment of the present specification further provides a service testing method, which can perform a service test based on the test data constructed by the method shown in fig. 1. It should be understood that the service testing method provided in the embodiments of the present specification may be executed by an electronic device or software installed in the electronic device, and specifically may be executed by a terminal device or a server device.

Referring to fig. 3, a flow chart of a service testing method provided for an embodiment of the present specification may include:

s302, obtaining attribute feature information of the service data in the production environment, wherein the attribute feature information comprises feature values corresponding to the service data in a plurality of attribute features respectively.

S304, clustering training is respectively carried out on the basis of the feature values of the service data corresponding to the single attribute feature, so as to obtain the categories of the service data respectively corresponding to the multiple attribute features.

S306, inputting the attribute characteristic information of the service data into a pre-established scene recognition model to obtain the service scene to which the service data belongs.

The scene recognition model is obtained by training based on attribute feature information of the sample business data and scene labels corresponding to the sample business data. And the scene label corresponding to the sample service data is used for indicating the service scene to which the sample service data belongs.

S308, based on the service data and the classes respectively corresponding to the service data under the attribute characteristics, constructing test data corresponding to the service scene to which the service data belongs.

And S310, performing service test based on the constructed test data.

Specifically, the test related to the service scenario may be performed according to the test data corresponding to the service scenario to be tested.

It should be noted that the specific implementation manner of the steps S302 to S308 is similar to that of the steps S102 to S108 in the embodiment shown in fig. 1, and specific reference may be made to the related description of the embodiment shown in fig. 1, which is not repeated herein.

Obviously, the service test method provided in the embodiments of the present specification can perform a service test based on test data corresponding to different service scenarios, so as to improve accuracy and reliability of a test result. Then, based on the service test result, relevant service processing tests can be executed, such as improving the performance of the service system.

In addition, corresponding to the test data constructing method shown in fig. 1, the embodiment of the present specification further provides a test data constructing apparatus. Fig. 4 is a schematic structural diagram of a test data constructing apparatus 400 provided in an embodiment of the present specification, including:

a first obtaining unit 410, configured to obtain attribute feature information of service data in a production environment, where the attribute feature information includes feature values corresponding to a plurality of attribute features of the service data, respectively;

a single-feature training unit 420, which performs cluster training based on feature values of the service data corresponding to a single attribute feature, respectively, to obtain categories of the service data corresponding to the multiple attribute features, respectively;

a scene recognition unit 430, configured to input attribute feature information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, where the scene recognition model is obtained by training based on attribute feature information of sample service data and a scene label corresponding to the sample service data, and the scene label is used to indicate the service scene to which the sample service data belongs;

a constructing unit 440, configured to construct test data corresponding to the service scenario to which the service data belongs, based on the service data and the categories of the service data respectively corresponding to the plurality of attribute features.

The test data construction device provided in the embodiment of the present specification, through artificial intelligence, machine learning, and other manners, sorts and aggregates the service data in the real production environment based on the attribute feature information, respectively obtains the service scenario to which the service data belongs and the categories respectively corresponding to the plurality of attribute features, and further implements automatic construction of the test data corresponding to the service scenario to which the service data belongs according to the service scenario and the categories corresponding to the single attribute feature. In addition, the whole construction process does not need manual intervention, and the testing efficiency can be improved.

Optionally, the constructing unit 440 is specifically configured to:

based on the classes respectively corresponding to the service data under the multiple attribute characteristics, aggregating the service data corresponding to the same class combination to obtain service data corresponding to different class combinations, wherein the class combinations refer to the combinations of the classes respectively corresponding to the multiple attribute characteristics;

constructing test data corresponding to the same category combination based on the service data corresponding to the same category combination;

and determining the test data corresponding to the constructed different types of combinations as the test data corresponding to the service scene to which the service data belongs.

Optionally, the apparatus 400 further comprises:

the aggregation unit aggregates the constructed test data and the locally stored test data according to the service scenario to which the service data belongs after the construction unit 440 constructs the test data corresponding to the service scenario to which the service data belongs, so as to obtain test data sets corresponding to different service scenarios;

a scene characteristic determining unit, configured to determine, as a scene characteristic of a service scene to which the service data belongs, a characteristic value corresponding to each of the service data under the plurality of attribute characteristics;

and the test database generating unit is used for generating a test database based on the test data sets and the scene characteristics respectively corresponding to the different service scenes.

Optionally, the apparatus 400 further comprises:

the receiving unit is used for receiving a data acquisition request sent by a tester after the test database generating unit generates a test database, wherein the data acquisition request is used for requesting to acquire test data, and the data acquisition request carries scene characteristics of a test scene;

the second acquisition unit is used for acquiring target test data matched with the test scene from the test database based on the scene characteristics of the test scene;

and the sending unit feeds the target test data back to the testing party.

Optionally, the single-feature training unit 420 is specifically configured to:

respectively aggregating the service data based on the characteristic values of the service data corresponding to the single attribute characteristics to obtain the service data corresponding to the single attribute characteristics at different characteristic values;

and clustering the service data corresponding to the single attribute characteristic in different characteristic values based on a preset clustering algorithm to obtain the corresponding category of the service data under the single attribute characteristic.

Optionally, the apparatus 400 further comprises:

a third obtaining unit, configured to obtain attribute feature information of sample service data and a scene tag corresponding to the sample service data before the scene recognition unit 430 inputs the attribute feature information of the service data into a pre-established scene recognition model;

and the training unit takes the attribute feature information as input and takes the scene label corresponding to the sample service data as output for training so as to obtain the scene recognition model.

Optionally, the scene recognition model is obtained by training based on attribute feature information of the desensitized sample service data and a scene label corresponding to the attribute feature information;

the apparatus 400 further comprises:

a desensitization processing unit, configured to perform desensitization processing on the service data and the attribute feature information thereof after the first obtaining unit 410 obtains the attribute feature information of the service data in the production environment.

Optionally, the plurality of attribute characteristics includes types of service participants and supported service types.

Obviously, the test data constructing apparatus according to the embodiment of the present specification may be an execution subject of the test data constructing method shown in fig. 1, and thus may be capable of implementing the function of the test data constructing method in fig. 1. Since the principle is the same, it is not described herein again.

In addition, corresponding to the service testing method shown in fig. 3, an embodiment of the present specification further provides a service testing apparatus. Fig. 5 is a schematic structural diagram of a service testing apparatus 500 provided in an embodiment of the present specification, including:

a first obtaining unit 510, configured to obtain attribute feature information of service data in a production environment, where the attribute feature information includes feature values corresponding to a plurality of attribute features of the service data, respectively;

a single-feature training unit 520, configured to perform cluster training based on feature values of the service data corresponding to a single attribute feature, respectively, so as to obtain categories of the service data corresponding to the multiple attribute features, respectively;

a scene recognition unit 530, configured to input attribute feature information of the service data into a pre-established scene recognition model to obtain a service scene to which the service data belongs, where the scene recognition model is obtained by training based on attribute feature information of sample service data and a scene label corresponding to the sample service data, and the scene label is used to indicate the service scene to which the sample service data belongs;

a constructing unit 540, configured to construct test data corresponding to the service scenario to which the service data belongs, based on the service data and the categories of the service data respectively corresponding to the plurality of attribute features.

And a test unit 550 for performing a service test based on the constructed test data.

The service testing device provided in the embodiments of the present description can perform service testing based on test data corresponding to different service scenarios, so as to improve accuracy and reliability of a test result. Thereafter, based on the service test result, a relevant service processing test may be performed, for example, to improve the performance of the service system.

Obviously, the service testing apparatus according to the embodiment of the present disclosure may be used as the execution main body of the service testing method shown in fig. 3, and thus the function of the service testing method implemented in fig. 3 can be implemented. Since the principle is the same, it is not described herein again.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present specification. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other by an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the test data constructing device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

Or the processor reads the corresponding computer program from the nonvolatile memory into the memory and runs the computer program to form the service testing device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

constructing test data corresponding to the service scene to which the service data belongs based on the service data and the classes respectively corresponding to the service data under the attribute characteristics;

and carrying out service test based on the constructed test data.

The method executed by the test data constructing apparatus disclosed in the embodiment shown in fig. 1 of the present specification or the service testing method disclosed in the embodiment shown in fig. 3 of the present specification may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

It should be understood that the electronic device of the embodiment of the present specification may implement the functions of the test data constructing apparatus in the embodiment shown in fig. 1, or may implement the functions of the above-described service testing apparatus in the embodiment shown in fig. 3. Because the principle is the same, the embodiments of the present description are not described herein again. Since the principle is the same, the embodiments of the present description are not described herein again.

Of course, besides the software implementation, the electronic device in the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

Embodiments of the present specification also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 1, and in particular for performing the following operations:

Alternatively, the above instructions, when executed by a portable electronic device comprising a plurality of application programs, can cause the portable electronic device to perform the method of the embodiment shown in fig. 3, and is specifically configured to perform the following operations:

and carrying out service test based on the constructed test data.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present specification shall be included in the protection scope of the present specification.

The systems, apparatuses, modules or units described in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims

1. A method of test data construction, comprising:

2. The method of claim 1, wherein the constructing test data corresponding to the service scenario to which the service data belongs based on the classes respectively corresponding to the service data and the service data under the plurality of attribute characteristics comprises:

3. The method of claim 1, wherein after constructing test data corresponding to a service scenario to which the service data belongs, the method further comprises:

according to the service scenes, aggregating the constructed test data and the locally stored test data to obtain test data sets corresponding to different service scenes;

determining feature values respectively corresponding to the service data under the plurality of attribute features as scene features of a service scene to which the service data belongs;

and generating a test database based on the test data sets and the scene characteristics respectively corresponding to the different service scenes.

4. The method of claim 3, wherein after generating the test database, the method further comprises:

receiving a data acquisition request sent by a tester, wherein the data acquisition request is used for requesting to acquire test data and carries scene characteristics of a test scene;

acquiring target test data matched with the test scene from the test database based on the scene characteristics of the test scene;

and feeding the target test data back to the testing party.

5. The method of claim 1, wherein the performing cluster training based on feature values of the business data corresponding to a single attribute feature to obtain categories of the business data corresponding to the attribute features respectively comprises:

and based on a preset clustering algorithm, clustering the service data corresponding to the single attribute characteristic in different characteristic values to obtain the corresponding category of the service data under the single attribute characteristic.

6. The method of claim 1, wherein prior to inputting attribute feature information of the business data into a pre-established scene recognition model, the method further comprises:

acquiring attribute characteristic information of sample service data and a scene label corresponding to the sample service data;

and training by taking the attribute characteristic information as input and taking a scene label corresponding to the sample service data as output to obtain the scene recognition model.

7. The method of claim 1, wherein the scene recognition model is obtained by training based on attribute feature information of the desensitized sample service data and corresponding scene labels thereof;

after obtaining attribute feature information of the business data in the production environment, the method further comprises:

and desensitizing the service data and the attribute characteristic information thereof.

8. The method of any of claims 1 to 7, wherein the plurality of attribute characteristics includes a type of service participant and a type of service supported.

9. A test data construction apparatus comprising:

the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring attribute characteristic information of service data in a production environment, and the attribute characteristic information comprises characteristic values corresponding to a plurality of attribute characteristics of the service data;

the single-feature training unit is used for performing clustering training on the basis of feature values of the service data corresponding to the single attribute feature respectively so as to obtain categories of the service data respectively corresponding to the attribute features;

10. An electronic device, comprising:

a processor; and

11. A computer readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to: