Test processing method and device

Abstract

The embodiment of the invention provides a test processing method and device.The test processing method comprises the steps that a to-be-classified data set for simulation environment configuration is obtained from a production environment, and meanwhile data features of all to-be-classified data are obtained, standard data features of all the to-be-classified data are obtained by conducting standardization processing on data features of all the to-be-classified data, classification processing is conducted on the basis of the standard data features through a pre-trained classification model, classification labels of all the to-be-classified data are obtained, and all the to-be-classified data are classified into corresponding data categories according to the classification labels. And performing configuration processing on the simulation environment by means of the classified target data under each data category so as to perform test processing on the test data acquired in the production environment in the configured simulation environment.

Description

Technical Field

[0001] This document relates to the field of data processing technology, and in particular to a test processing method and device. Background Art

[0002] With the continuous development of Internet technology, the applications running in the production environment have become more and more complex, and the amount of data processed by the applications has become increasingly large, which may lead to failures in the applications running in the production environment, making the demand for fault prevention and control in the applications increasingly urgent. In the process of fault prevention and control of applications running in the production environment, building a simulation environment is one of the effective means. Through the constructed simulation environment, testing can be carried out in the simulation environment and then fault detection can be performed. In this process, how to test more effectively in the simulation environment has gradually become the focus of the tester. Summary of the invention

[0003] One or more embodiments of the present specification provide a test processing method, comprising: obtaining a data set to be classified for simulation environment configuration from a production environment, and obtaining data features of each data to be classified in the data set to be classified. Standardizing the data features to obtain standard data features of each data to be classified. Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels of each data to be classified. Classifying each data to be classified into a corresponding data category according to the classification label, and performing configuration processing of the simulation environment based on the target data under the data category, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

[0004] One or more embodiments of the present specification provide a test processing device, including: a data acquisition module, configured to acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified. A standardization module, configured to perform standardization processing on the data features, and acquire standard data features of each data to be classified. A classification processing module, configured to input the standard data features into a pre-trained classification model for classification processing, and acquire classification labels of each data to be classified. A configuration processing module, configured to classify each data to be classified into a corresponding data category according to the classification label, and perform configuration processing of the simulation environment based on the target data under the data category, so as to perform test processing of the test data acquired in the production environment in the configured simulation environment.

[0005] One or more embodiments of the present specification provide a test processing device, including: a processor; and a memory configured to store computer executable instructions, wherein when the computer executable instructions are executed, the processor: obtains a data set to be classified for simulation environment configuration from a production environment, and obtains data features of each data to be classified in the data set to be classified. Standardize the data features to obtain standard data features of each data to be classified. Input the standard data features into a pre-trained classification model for classification processing to obtain classification labels of each data to be classified. Classify each data to be classified into a corresponding data category according to the classification label, and perform simulation environment configuration processing based on the target data under the data category, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

[0006] One or more embodiments of the present specification provide a computer-readable storage medium for storing computer-executable instructions, which implement the following steps when executed: obtaining a data set to be classified for simulation environment configuration from a production environment, and obtaining data features of each data to be classified in the data set to be classified. Standardizing the data features to obtain standard data features of each data to be classified. Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels of each data to be classified. Classifying each data to be classified into a corresponding data category according to the classification label, and performing configuration processing of the simulation environment based on the target data under the data category, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate one or more embodiments of the present specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present specification. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor.

[0008] Figure 1 A schematic diagram of an implementation environment of a test processing method provided for one or more embodiments of this specification;

[0009] Figure 2 A process flow chart of a test processing method provided for one or more embodiments of this specification;

[0010] Figure 3 A process flow chart of a test processing method applied to a test scenario provided in one or more embodiments of this specification;

[0011] Figure 4 A schematic diagram of a test processing device embodiment provided for one or more embodiments of this specification;

[0012] Figure 5 A schematic diagram of the structure of a test processing device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0014] The test processing method provided in one or more embodiments of this specification can be applied to the implementation environment of the simulation environment configuration, referring to Figure 1 , the implementation environment at least includes:

[0015] A processing system 101 performs operations such as data acquisition and data classification. In addition, the implementation environment may also include a production environment 102 and a simulation environment 103.

[0016] The processing system 101 may run on a server, and the server may be a single server, or a server cluster consisting of a plurality of servers, or one or more cloud servers in a cloud computing platform.

[0017] The production environment 102 and the simulation environment 103 can be configured on different servers or on the same server. The server configured for the production environment 102 and the simulation environment 103 can be a single server, a server cluster consisting of several servers, or one or more cloud servers in a cloud computing platform.

[0018] In this implementation environment, the processing system 101 obtains a data set to be classified for simulation environment configuration from the production environment 102, and obtains data features of each data to be classified, obtains standard data features of each data to be classified by standardizing the data features of each data to be classified, classifies the standard data features through a pre-trained classification model to obtain classification labels of each data to be classified, classifies each data to be classified into a corresponding data category according to the classification label of each data to be classified, and configures the simulation environment 103 with the help of the target data under the data category, so as to perform test processing of the test data obtained in the production environment 102 in the configured simulation environment 103.

[0019] One or more embodiments of a test processing method provided in this specification are as follows:

[0020] Reference Figure 2 The test processing method provided in this embodiment specifically includes steps S202 to S208.

[0021] Step S202, obtaining a data set to be classified for simulation environment configuration from a production environment, and obtaining data features of each data to be classified in the data set to be classified.

[0022] The production environment described in this embodiment refers to a real environment, that is, an environment that officially provides services to the outside world, and the target application can be run in the production environment; the simulation environment refers to a simulation environment parallel to the production environment, and the simulation environment is used to test the application to be tested, that is, the application to be tested can be run in the simulation environment, the target application and the application to be tested can be the same, or the target application and the application to be tested can be applications that provide the same service but belong to different versions.

[0023] The data set to be classified refers to a set consisting of one or more data to be classified; each data to be classified in the data set to be classified refers to the data obtained from the production environment and relied on for testing, and each data to be classified can be the data required to run the target application in the production environment. Specifically, each data to be classified can be configuration data, status data, and flow data, such as system configuration data required to run the payment application in the production environment, status data such as payment amount changes, and flow data from payment request to payment request response; the data set to be classified can be a set of tables to be classified, that is, a set consisting of one or more tables to be classified, such as Table 1, Table 2, Table 3, etc.

[0024] The data features of each data to be classified refer to features that characterize the data attributes of each data to be classified, that is, they can be attribute features of each data to be classified, or table attribute features of each table to be classified, for example, the data features of each data to be classified can be table name, read-write ratio, table size, read-write magnitude, etc. The data features of each data to be classified can be data feature groups of each data to be classified, that is, each data to be classified in each data to be classified can have a corresponding data feature group, and each data feature group of each data to be classified refers to a combination of one or more data features.

[0025] In actual applications, before testing the application to be tested in a simulation environment, it is often necessary to align the data of the simulation environment with the production environment, that is, to prepare the necessary data required for testing the application to be tested. In view of this, the present embodiment prepares the necessary data required for testing the application to be tested in an automated manner. Specifically, the data set to be classified for configuring the simulation environment is obtained from the production environment, which can be obtained from the storage unit of the production environment. The storage unit here can be a database unit and / or a cache unit. The database unit can be DB (DateBase), and the cache unit can be Cache (cache memory); the data set to be classified can be a set of tables to be classified; it can also be obtained from the production environment within a preset time period to configure the simulation environment. Specifically, the data set to be classified for configuring the simulation environment can be obtained from the production environment according to the preset time period corresponding to the test data in the production environment; the test data here can be request data, that is, traffic data in the production environment, and the traffic data is used for traffic playback.

[0026] In actual application scenarios, the classification data set is often manually classified. For example, according to manual experience, if the table name is xx1, the data to be classified is configuration data, and if the table name is xx2, the data to be classified is status data. However, manual experience is limited after all. For example, it is not feasible to manually determine the data category by whether there are sub-libraries and sub-tables. Therefore, in order to improve the efficiency and accuracy of data classification, after obtaining the data set to be classified for simulation environment configuration from the production environment, the data features of each data to be classified in the data set to be classified can be obtained; in an optional implementation manner provided in this embodiment, the data features of each data to be classified are obtained in the following manner:

[0027] Obtain static data features of each data to be classified from the management platform of the target application running in the production environment, collect dynamic data features of each data to be classified through the aspect program in the target application, and / or determine target data features of each data to be classified based on the dynamic data features and / or the static data features.

[0028] Among them, the target application refers to an application running in a production environment; the management platform of the target application can be a configuration management platform of the target application. The static data features of each data to be classified refer to the static data features of each data to be classified. Static data features do not need to be collected when the target application is running in a production environment, and can be directly crawled from the management platform of the target application. For example, static data features can be features such as whether there are sub-libraries and sub-tables; the dynamic data features of each data to be classified refer to the dynamic data features of each data to be classified, such as dynamic data features can be features such as input and output parameters of interface calls, table column names, etc.; the target data features refer to data features determined by statistics, such as read-write ratio, read-write magnitude, etc.

[0029] Specifically, the static data features of each data to be classified (each table to be classified) can be crawled from the management platform of the target application, and the dynamic data features of each data to be classified can be collected through the aspect program in the target application running in the production environment, and the target data features of each data to be classified can be determined based on the static data features of each data to be classified and / or the dynamic data features of each data to be classified; in this way, the static data features and dynamic data features of each data to be classified can be obtained respectively by combining the crawling technology and the aspect program technology, and the richness and comprehensiveness of the data features of the data to be classified can be improved by collecting the data features of each data to be classified in different dimensions and different states, and on this basis, the target data features of each data to be classified can be determined with the help of static data features and / or dynamic data features to further improve the richness and comprehensiveness of the data features of the data to be classified.

[0030] For example, crawl the static data features of each table to be classified from the management platform of the target application to see if there are sub-libraries and sub-tables. Collect the dynamic data features of the input and output parameters of each table to be classified through the aspect program in the target application running in the production environment. Determine the target data features such as the read-write ratio and read-write level of each table to be classified based on the input and output parameters of each table to be classified.

[0031] In addition, the data features of each data to be classified can also be obtained in the following ways: acquiring the static data features of each data to be classified from the management platform of the target application, collecting the dynamic data features of each data to be classified through the aspect program in the target application running in the production environment, or determining the target data features of each data to be classified based on the dynamic data features and / or the static data features.

[0032] It should be noted that, in actual applications, the method for obtaining the data features of each data to be classified may be to obtain any one or more of the three, static data features, dynamic data features, and target data features, as needed, and this embodiment does not make any specific limitations here.

[0033] Step S204: standardize the data features to obtain standard data features of the data to be classified.

[0034] The above-mentioned method obtains the data set to be classified for simulation environment configuration from the production environment, and obtains the data features of each data to be classified in the data set to be classified. In this step, since the value range of the data feature of each data to be classified may be different, the difference between each data feature may be large. Therefore, in order to make the data features of each data to be classified under the same standard, the data features of each data to be classified can be standardized to obtain the standard data features of each data to be classified.

[0035] In practical applications, after obtaining the data features of each data to be classified in the data set to be classified, since the data features of each data to be classified may be quite different, the data features of each data to be classified may also be non-standard. In view of this, in order to improve the adaptability of the data features of each data to be classified and the classification model, in an optional implementation manner provided by this embodiment, in the process of standardizing the data features to obtain the standard data features of each data to be classified, the data features of each data to be classified in the data to be classified may be standardized to obtain the standard data features of each data to be classified. Specifically, the following operations may be performed:

[0036] Performing noise reduction processing on the data features to obtain noise reduction features of each data to be classified;

[0037] Numerically processing the target denoising features among the denoising features of each of the data to be classified to obtain an intermediate feature;

[0038] The intermediate features of each of the data to be classified are converted into standard data features of each of the data to be classified.

[0039] The target denoising feature among the denoising features of each data to be classified refers to the target denoising feature among the denoising features of each data to be classified that is not a numerical feature. The numerical feature refers to a feature in which the data feature is a numerical value.

[0040] It should be noted that in the process of standardizing data features to obtain standard data features of each data to be classified, one or more of the three processes of noise reduction, digitization, and conversion can also be used for standardization processing. The execution order of one or more of the three processes is not specifically limited here.

[0041] It should be noted that the above step S204 can be replaced by performing feature preprocessing on the data features of each data to be classified to obtain the standard data features of each data to be classified, and forming a new implementation method with other processing steps provided in this embodiment;

[0042] Among them, feature preprocessing of the data features of each data to be classified includes: performing feature denoising on the data features of each data to be classified, and obtaining the denoising features of each data to be classified as the standard data features of each data to be classified; or, performing numerical processing on the preset data features in the data features of each data to be classified, and using the obtained target data features and the data features of the remaining data to be classified as the standard data features of each data to be classified; or, performing dimensionality up-processing on the data features of each data to be classified, and obtaining the dimensionality up-processing features of each data to be classified as the standard data features of each data to be classified; or, performing dimensionality reduction processing on the data features of each data to be classified, and obtaining the dimensionality reduction features of each data to be classified as the standard data features of each data to be classified; or, converting the data features of each data to be classified into the standard data features of each data to be classified.

[0043] It should be added that the above-mentioned five preprocessing methods involved in the process of preprocessing the data features of each data to be classified can be combined with one or more of them for feature preprocessing. This embodiment does not make any specific limitations on this, and this embodiment does not make any specific limitations on the execution order of one or more combined methods.

[0044] It should also be added that the data features of each data to be classified can be a data feature group of each data to be classified. In this case, in the process of standardizing the data features to obtain the standard data features of each data to be classified, each data feature in the data feature group of each data to be classified can be standardized to obtain the standard data feature group of each data to be classified.

[0045] Step S206: input the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified.

[0046] The classification label of each data to be classified in this embodiment refers to a data category label that characterizes the classification of each data to be classified. For example, if the classification label is 0, it means that the data to be classified is classified into the corresponding first data category. The classification label is 1, which means that the data to be classified is classified into the corresponding second data category. The first data category can be a configuration data category, and the second data category can be a status data category.

[0047] In practical applications, manual methods are often used to classify the data to be classified based on standard data features. However, this method is not efficient and the accuracy of the classification process may also be low. In order to improve the efficiency and accuracy of the classification process for each data to be classified, a pre-trained classification model may be introduced. The classification model is used to classify the data to be classified based on the standard data features. In an optional implementation manner provided in this embodiment, the classification model is classified in the following manner:

[0048] Determining a classification index for classifying each of the data to be classified based on each standard data feature in the standard data feature group of each of the data to be classified and a predetermined classification weight;

[0049] Determine the classification label of each data to be classified according to the classification index.

[0050] The classification index may be a classification index or a classification value of each data to be classified obtained by calculation based on each standard data feature and classification weight.

[0051] On this basis, in actual application scenarios, the combination of at least two standard data features in the standard data feature group of each data to be classified may also determine how to classify each data to be classified, that is, the role played by each standard data feature and the combination feature in the subsequent data classification process may be different, that is, the degree of influence of each standard data feature and the combination feature on data classification may be different. In view of this, in order to quantify the degree of influence of each standard data feature and the combination feature on data classification and improve the accuracy of data classification; in an optional implementation manner provided by this embodiment, in the process of determining the classification index for classifying the data to be classified based on each standard data feature in the standard data feature group of each data to be classified and a predetermined classification weight, the following operations are performed:

[0052] Combining at least two of the standard data features to obtain a combined feature;

[0053] The classification index is calculated according to the standard data features and the corresponding classification weights and the combined features and the corresponding classification weights.

[0054] The combination feature may be one or more; the standard data features in the combination feature may be at least two standard data features. The classification weight corresponding to each standard data feature may represent the role played by each standard data feature in the process of data classification. The classification weight corresponding to the combination feature may represent the degree of influence of the combination feature on data classification.

[0055] Specifically, at least two of the standard data features can be combined to obtain a combined feature, and the classification value for classifying each data to be classified can be calculated based on the classification weights corresponding to each standard data feature and the feature categories of each standard data feature, and the classification weights corresponding to the combined feature and the feature categories of the combined feature.

[0056] In order to facilitate data classification for each data to be classified, each classification label may be provided, and the classification label of each data to be classified may be obtained by matching the classification index of each data to be classified with each classification label; in an optional implementation manner provided by this embodiment, in the process of determining the classification label of each data to be classified according to the classification index, the following operations are performed:

[0057] The classification index of each data to be classified is matched with each classification label to obtain the classification label of each data to be classified.

[0058] Specifically, the classification index of each data to be classified may be matched with the classification index interval of each classification label to obtain the classification label of each data to be classified.

[0059] In actual application scenarios, the data features of different data in the production environment have different degrees of influence on data classification. For example, these data features such as read-write ratio, read-write level, and table name may play different roles in data classification during data classification. In view of this, in order to quantify the degree of influence of data features on data classification and realize automatic data classification based on various data features, the classification weights of different data features can be obtained by machine learning, that is, by training the model to be trained, adjusting the classification weights during the model training process to obtain a classification model, which carries the classification weights corresponding to different data features and the classification weights corresponding to the combined features; in an optional implementation manner provided in this embodiment, the classification model is obtained by training in the following manner:

[0060] Acquire data features of data in a production environment, and determine feature categories of the data features of the data;

[0061] A model to be trained is constructed based on the feature category, and model training is performed on the model to be trained according to the data features of the data to obtain the classification model.

[0062] The feature category of the data feature of the data refers to the category of the data feature, for example, if the data feature is read-write ratio, the feature category is read-write ratio category, and if the data feature is table name, the feature category is table name category. The data feature of the data may have multiple feature categories.

[0063] On this basis, in an optional implementation manner provided in this embodiment, in the process of constructing the model to be trained based on the feature category, the following operations are performed:

[0064] Performing conversion processing on each feature category and / or a combination of feature categories obtained by combining the feature categories to obtain a first conversion parameter corresponding to each feature category and / or a second conversion parameter corresponding to the combination of feature categories;

[0065] The classification weight corresponding to the first conversion parameter and / or the classification weight corresponding to the second conversion parameter is initialized, and the model to be trained is constructed according to the initialized classification weight and the first conversion parameter and / or the second conversion parameter.

[0066] Specifically, each feature category and the feature category combination obtained by combining each feature category can be converted separately to obtain the first conversion parameter corresponding to each feature category and the second conversion parameter corresponding to the feature category combination, the classification weight corresponding to the first conversion parameter and the classification weight corresponding to the second conversion parameter are initialized, and the model to be trained is constructed according to the initialized classification weight and the first conversion parameter and the second conversion parameter; or, each feature category or the feature category combination obtained by combining each feature category can be converted separately to obtain the first conversion parameter corresponding to each feature category or the second conversion parameter corresponding to the feature category combination, the classification weight corresponding to the first conversion parameter or the classification weight corresponding to the second conversion parameter is initialized, and the model to be trained is constructed according to the initialized classification weight and the first conversion parameter or the second conversion parameter.

[0067] For example, each feature category includes table name, read-write ratio, read-write magnitude, and table column name. Each feature category is converted to obtain conversion parameters corresponding to each feature category, which are x1, x2, x3, and x4. Since each feature category has a classification weight, which represents the degree of influence of each feature category on data classification, the classification weight corresponding to each conversion parameter is a, b, c, and d. The classification weight corresponding to each conversion parameter is initialized at the beginning, and the model to be trained is constructed according to the initialized classification weight and the corresponding conversion parameters, such as a*x1+b*x2+c*x3+d*x4. The model to be trained here is only schematic, and the model to be trained can be constructed differently according to the actual application.

[0068] Step S208, classifying the data to be classified into corresponding data categories according to the classification labels, and performing configuration processing of the simulation environment based on the target data under the data categories, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

[0069] The above-mentioned classification model is pre-trained to perform classification processing based on standard data features to obtain classification labels for each data to be classified. In this step, each data to be classified is classified into a corresponding data category according to the classification label, and the simulation environment is configured based on the target data under the data category, so that the test data obtained in the production environment can be tested in the configured simulation environment, that is, the application to be tested is tested in the configured simulation environment based on the test data obtained in the production environment, that is, the traffic data obtained in the production environment is replayed in the configured simulation environment to achieve test processing for the application to be tested.

[0070] The test data obtained in the production environment described in this embodiment may be request data obtained from the production environment, that is, traffic data obtained from the production environment.

[0071] In specific implementation, each data to be classified can be classified into a data category corresponding to the classification label of each data to be classified according to the classification label, and the simulation environment can be configured and processed based on the target data under each data category.

[0072] In practical applications, since the amount of pipeline data is large, if the pipeline data in the simulation environment is to be aligned, the cost of configuring the simulation environment may be high. Therefore, in order to reduce the configuration cost of the simulation environment configuration, the pipeline data in each data to be classified may be eliminated. In an optional implementation provided by this embodiment, on the basis of matching the classification index of each data to be classified with each classification label to obtain the classification label of each data to be classified, in the process of classifying each data to be classified into the corresponding data category according to the classification label, the following operations are performed:

[0073] According to the classification labels of the data to be classified, the remaining data to be classified whose classification labels are not empty are screened out from the data to be classified;

[0074] According to the classification labels of the remaining data to be classified, the remaining data to be classified is classified into data categories corresponding to the classification labels of the remaining data to be classified.

[0075] During the specific execution process, since there may be target data of multiple data categories under the data category, the configuration processing methods of the simulation environment for target data under different data categories may also be different. In view of this, in an optional implementation manner provided by this embodiment, in the process of configuring the simulation environment based on the target data under the data category, the following operations are performed:

[0076] Import the configuration data under the configuration data category into the configuration storage location corresponding to the simulation environment;

[0077] Import the status data under the status data category into the database in the simulation environment.

[0078] The configuration data under the configuration data category may be static or relatively static configuration data required for the application to be tested to run in the simulation environment, such as system parameters, rule settings, user permissions, API (Application Programming Interface) interface addresses, etc. The status data under the status data category may be dynamic status data of the application to be tested at different time points, such as user account balance, order status, product inventory, etc.

[0079] In actual applications, in the process of classification processing based on the standard data features of each data to be classified, the number of features of the standard data features of each data to be classified in each data to be classified may be different, and for large-scale services, i.e., target applications, the data volume of the standard data features of each data to be classified may be large. In this case, if the standard data features of each data to be classified are classified by all classification models, the processing pressure on the classification model is large. Therefore, in order to reduce the processing pressure of the classification model and to mobilize multiple resources for classification processing to improve the resource utilization of multiple resources; in an optional implementation mode provided by this embodiment, in the above step S204, the data features are standardized to obtain the standard data features of each data to be classified, and before the above step S206, the standard data features are input into the pre-trained classification model for classification processing to obtain the classification labels of each data to be classified, the following operations are also performed:

[0080] Detecting whether there is data to be classified whose number of standard data features is less than a preset number among the data to be classified;

[0081] If not, executing step S206, inputting the standard data features into a pre-trained classification model for classification processing, and obtaining classification labels for each data to be classified.

[0082] The number of standard data features refers to the number of features of standard data features.

[0083] On the basis of detecting the number of features of the standard data features of each data to be classified, in order to further reduce the processing pressure of the classification model; in an optional implementation manner provided by this embodiment, if the execution result after detecting whether there is data to be classified whose number of standard data features is less than the preset number in the data to be classified is that there is, the following operations are performed:

[0084] Detecting whether the feature category of the standard data feature of the data to be classified is a preset feature category;

[0085] If yes, input the standard data features of the data to be classified into the classification rules for classification processing, and classify the data to be classified into the corresponding data category according to the output classification label of the data to be classified;

[0086] The simulation environment is configured based on the data in each data category, so as to perform test processing on the test data in the configured simulation environment.

[0087] Further, in an optional implementation manner provided by this embodiment, if the result of the execution of the detection of whether the feature category of the standard data feature of the data to be classified is a preset feature category is no, since the classification rule is usually constructed based on specific standard data features, the following operations may be performed:

[0088] Sending the standard data features of the data to be classified to a classifier to determine a classification label of the data to be classified based on the standard data features of the data to be classified;

[0089] According to the classification labels of the data to be classified returned by the classifier, the data to be classified is classified into corresponding data categories, and the simulation environment is configured and processed based on the data under each data category, so as to perform test processing on the test data in the configured simulation environment.

[0090] Through the three classification processing methods of classification model, classification rules and classification personnel mentioned above, we can fully utilize the classification processing resources of multiple parties and improve the classification processing efficiency.

[0091] It should be added that in the process of obtaining classification labels for each data to be classified based on standard data features, any one or more of the three methods of classification model, classification rules and classification personnel provided above can be used to achieve flexibility and convenience of classification processing.

[0092] It should be supplemented that each optional implementation method in steps S202 to S208 in the test processing method provided in this embodiment can be executed independently as needed, or can be combined and referenced with each other. At the same time, each specific execution step in each optional implementation method can also be executed independently, and this embodiment does not make specific limitations.

[0093] In summary, the one or more test processing methods provided in this embodiment firstly obtain a data set to be classified for simulation environment configuration from a production environment, and obtain static data features of each data to be classified from a management platform of a target application running in the production environment, collect dynamic data features of each data to be classified through a section program in the target application running in the production environment, determine target data features of each data to be classified based on dynamic data features and / or static data features, and use static data features, dynamic data features and target data features as data features of each data to be classified, thereby improving the richness and comprehensiveness of data features of each data to be classified through multiple ways of obtaining data features of each data to be classified;

[0094] Secondly, the data features of each data to be classified are standardized to obtain the standard data features of each data to be classified, so that the data features of each data to be classified can be under the same measurement standard, thereby improving the adaptability of the subsequent input as the classification model; then the standard data features of each data to be classified are input into the pre-trained classification model for classification processing to obtain the classification label of each data to be classified. The classification model here determines the classification label of each data to be classified based on each standard data feature in the standard data feature group of each data to be classified and the predetermined classification weight. In this way, by setting the classification weight for each standard data feature, it is possible to quantify the role of each standard data feature in the process of classifying the corresponding data to be classified, so that each data to be classified can be classified more conveniently, and the accuracy of the classification label of each data to be classified can be improved;

[0095] Finally, each data to be classified is classified into the corresponding data category according to its classification label, and the simulation environment is configured based on the target data under each data category, so that the test data obtained in the production environment can be tested in the configured simulation environment. In this way, the simulation environment can simulate the production environment and achieve data alignment between the simulation environment and the production environment, and then the production environment can be fault-controlled by testing in the configured simulation environment to ensure the stable operation of the target application in the production environment.

[0096] The following uses the application of a test processing method provided in this embodiment in a test scenario as an example to further illustrate the test processing method provided in this embodiment. Figure 3 , the test processing method applied to the test scenario specifically includes the following steps.

[0097] Step S302: obtaining a set of tables to be classified for simulation environment configuration from the production environment, and obtaining table features of each table to be classified in the set of tables to be classified.

[0098] Among them, obtaining the table characteristics of each table to be classified in each set of tables to be classified may include: obtaining static table characteristics of each table to be classified from a management platform of a target application running in a production environment, collecting dynamic table characteristics of each table to be classified through an aspect program in a target application running in a production environment, and / or determining target table characteristics of each table to be classified based on dynamic table characteristics and / or static table characteristics.

[0099] Step S304: standardize the table features of each table to be classified to obtain the standard table features of each table to be classified.

[0100] Step S306: input the standard table features of each table to be classified into a pre-trained classification model for classification processing to obtain a classification label of each table to be classified.

[0101] Among them, the classification model can be classified in the following ways:

[0102] Determine the classification index for classifying each table to be classified based on each standard data feature in the standard table feature group of each table to be classified and the predetermined classification weight;

[0103] Determine the classification label of each table to be classified according to the classification index.

[0104] Here, based on each standard table feature in the standard table feature group of each table to be classified and a predetermined classification weight, determining the classification index for classifying each table to be classified may include:

[0105] Combining at least two standard table features among the standard table features to obtain a combined feature;

[0106] According to the characteristics of each standard table and the corresponding classification weight as well as the combined characteristics and the corresponding classification weight, the classification index of each table to be classified is calculated.

[0107] Step S308 , filtering out the remaining tables to be classified whose classification labels are not empty from the tables to be classified according to the classification labels of the tables to be classified, and classifying the remaining tables to be classified into corresponding table categories according to the classification labels of the remaining tables to be classified.

[0108] Step S310 , performing configuration processing of the simulation environment based on the target table under each table category, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

[0109] It should be noted that any one of steps S302 to S310 or any combination of multiple steps can be replaced by the corresponding technical means provided in the above steps S202 to S208 according to the needs of implementation and deployment, and will not be described one by one here.

[0110] An embodiment of a test processing device provided in this specification is as follows:

[0111] In the above-mentioned embodiment, a test processing method is provided, and correspondingly, a test processing device is also provided, which will be described below with reference to the accompanying drawings.

[0112] Reference Figure 4 , which shows a schematic diagram of an embodiment of a test processing device provided in this embodiment.

[0113] Since the device embodiment corresponds to the method embodiment, the description is relatively simple, and the relevant parts can refer to the corresponding description of the method embodiment provided above. The device embodiment described below is only illustrative.

[0114] This embodiment provides a test processing device, including:

[0115] The data acquisition module 402 is configured to acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified;

[0116] A standardization module 404 is configured to perform standardization processing on the data features to obtain standard data features of each data to be classified;

[0117] The classification processing module 406 is configured to input the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified;

[0118] The configuration processing module 408 is configured to classify the data to be classified into corresponding data categories according to the classification labels, and perform configuration processing of the simulation environment based on the target data under the data categories, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

[0119] An embodiment of a test processing device provided in this specification is as follows:

[0120] Corresponding to the test processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a test processing device, which is used to execute the test processing method provided above. Figure 5 A schematic diagram of the structure of a test processing device provided for one or more embodiments of this specification.

[0121] This embodiment provides a test processing device, including:

[0122] like Figure 5As shown, the test processing device may have relatively large differences due to different configurations or performances, and may include one or more processors 501 and memory 502, and the memory 502 may store one or more storage applications or data. Among them, the memory 502 may be a short-term storage or a persistent storage. The application stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the test processing device. Furthermore, the processor 501 may be configured to communicate with the memory 502 and execute a series of computer executable instructions in the memory 502 on the test processing device. The test processing device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, etc.

[0123] In a specific embodiment, the test processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer executable instructions for the test processing device, and the one or more programs configured to be executed by one or more processors include computer executable instructions for performing the following:

[0124] Acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified;

[0125] Standardizing the data features to obtain standard data features of the data to be classified;

[0126] Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified;

[0127] The data to be classified are classified into corresponding data categories according to the classification labels, and the simulation environment is configured based on the target data under the data category, so as to perform test processing on the test data obtained in the production environment in the configured simulation environment.

[0128] An embodiment of a computer-readable storage medium provided in this specification is as follows:

[0129] Corresponding to the test processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0130] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, and the computer-executable instructions implement the following steps when executed:

[0131] Acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified;

[0132] Standardizing the data features to obtain standard data features of the data to be classified;

[0133] Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified;

[0134] The data to be classified are classified into corresponding data categories according to the classification labels, and the simulation environment is configured based on the target data under the data category, so as to perform test processing on the test data obtained in the production environment in the configured simulation environment.

[0135] It should be noted that the embodiment of a computer-readable storage medium in this specification and the embodiment of a test processing method in this specification are based on the same inventive concept, so the specific implementation of this embodiment can refer to the implementation of the aforementioned corresponding method, and the repeated parts will not be repeated.

[0136] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are similar to the method embodiment, so the description is relatively simple. To read the relevant contents in the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the partial description of the method embodiment.

[0137] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0139] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0140] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, 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.

[0141] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0142] It should be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, system or computer program product. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable test processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable test processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable test processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable test processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide for implementing the process described in the flow. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0147] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0148] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0149] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of features includes not only those features, but also other features not explicitly listed, or also includes features inherent to such process, method, commodity or device. In the absence of further restrictions, features defined by the phrase "including a ..." do not exclude the existence of other identical features in the process, method, commodity or device including the features.

[0150] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0151] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0152] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A test processing method, comprising: Acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified; Standardizing the data features to obtain standard data features of the data to be classified; Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified; The data to be classified are classified into corresponding data categories according to the classification labels, and the simulation environment is configured based on the target data under the data category, so as to perform test processing on the test data obtained in the production environment in the configured simulation environment.

2. According to the test processing method of claim 1, the classification model is classified in the following manner: Determining a classification index for classifying each of the data to be classified based on each standard data feature in the standard data feature group of each of the data to be classified and a predetermined classification weight; Determine the classification label of each data to be classified according to the classification index.

3. The test processing method according to claim 2, wherein the determining the classification index for classifying the data to be classified based on each standard data feature in the standard data feature group of each data to be classified and a predetermined classification weight comprises: Combining at least two of the standard data features to obtain a combined feature; The classification index is calculated according to the standard data features and the corresponding classification weights and the combined features and the corresponding classification weights.

4. The test processing method according to claim 2, wherein determining the classification label of each data to be classified according to the classification index comprises: The classification index of each data to be classified is matched with each classification label to obtain the classification label of each data to be classified.

5. The test processing method according to claim 4, wherein the step of classifying the data to be classified into corresponding data categories according to the classification labels comprises: According to the classification labels of the data to be classified, the remaining data to be classified whose classification labels are not empty are screened out from the data to be classified; According to the classification labels of the remaining data to be classified, the remaining data to be classified is classified into data categories corresponding to the classification labels of the remaining data to be classified.

6. The test processing method according to claim 1, wherein the data features of each data to be classified are obtained by: Obtain static data features of each data to be classified from the management platform of the target application running in the production environment, collect dynamic data features of each data to be classified through the aspect program in the target application, and / or determine target data features of each data to be classified based on the dynamic data features and / or the static data features.

7. The test processing method according to claim 1, wherein the step of standardizing the data features to obtain the standard data features of each data to be classified comprises: Performing noise reduction processing on the data features to obtain noise reduction features of each data to be classified; Numerically processing the target denoising features among the denoising features of each of the data to be classified to obtain an intermediate feature; The intermediate features of each of the data to be classified are converted into standard data features of each of the data to be classified.

8. The test processing method according to claim 1, wherein the classification model is obtained by training in the following manner: Acquire data features of data in a production environment, and determine feature categories of the data features of the data; A model to be trained is constructed based on the feature category, and model training is performed on the model to be trained according to the data features of the data to obtain the classification model.

9. The test processing method according to claim 8, wherein the step of constructing a model to be trained based on the feature category comprises: Performing conversion processing on each feature category and / or a combination of feature categories obtained by combining the feature categories to obtain a first conversion parameter corresponding to each feature category and / or a second conversion parameter corresponding to the combination of feature categories; The classification weight corresponding to the first conversion parameter and / or the classification weight corresponding to the second conversion parameter is initialized, and the model to be trained is constructed according to the initialized classification weight and the first conversion parameter and / or the second conversion parameter.

10. The test processing method according to claim 1, wherein the configuration processing of the simulation environment based on the target data under the data category comprises: Import the configuration data under the configuration data category into the configuration storage location corresponding to the simulation environment; Import the status data under the status data category into the database in the simulation environment.

11. The test processing method according to claim 1, after the step of performing standard processing on the data features to obtain the standard data features of each data to be classified is performed, and before the step of inputting the standard data features into a pre-trained classification model for classification processing to obtain the classification labels of each data to be classified is performed, further comprising: Detecting whether there is data to be classified whose number of standard data features is less than a preset number among the data to be classified; If not present, the step of inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified is performed.

12. The test processing method according to claim 11, if the step of detecting whether there is data to be classified whose number of standard data features is less than a preset number in the data to be classified is executed, the following operation is performed: Detecting whether the feature category of the standard data feature of the data to be classified is a preset feature category; If yes, input the standard data features of the data to be classified into the classification rules for classification processing, and classify the data to be classified into the corresponding data category according to the output classification label of the data to be classified; The simulation environment is configured based on the data in each data category, so as to perform test processing on the test data in the configured simulation environment.

13. The test processing method according to claim 12, if the result of the operation of detecting whether the feature category of the standard data feature of the data to be classified is a preset feature category is no, performing the following operations: Sending the standard data features of the data to be classified to a classifier to determine a classification label of the data to be classified based on the standard data features of the data to be classified; According to the classification labels of the data to be classified returned by the classifier, the data to be classified is classified into corresponding data categories, and the simulation environment is configured and processed based on the data under each data category, so as to perform test processing on the test data in the configured simulation environment.

14. A test processing device, comprising: A data acquisition module is configured to acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified; A standardization module is configured to perform standardization processing on the data features to obtain standard data features of each data to be classified; A classification processing module is configured to input the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified; The configuration processing module is configured to classify the data to be classified into corresponding data categories according to the classification labels, and perform configuration processing of the simulation environment based on the target data under the data category, so as to perform test processing of the test data obtained in the production environment in the configured simulation environment.

15. A test processing device comprising: processor; and a memory configured to store computer executable instructions that, when executed, cause the processor to: Acquire a data set to be classified for simulation environment configuration from a production environment, and acquire data features of each data to be classified in the data set to be classified; Standardizing the data features to obtain standard data features of the data to be classified; Inputting the standard data features into a pre-trained classification model for classification processing to obtain classification labels for each data to be classified; The data to be classified are classified into corresponding data categories according to the classification labels, and the simulation environment is configured based on the target data under the data category, so as to perform test processing on the test data obtained in the production environment in the configured simulation environment.

16. A computer-readable storage medium for storing computer-executable instructions, wherein the computer-executable instructions implement the steps of the method of claim 1 when executed.

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