Test element information generation method, device, equipment and storage medium

Abstract

The present disclosure provides a test element information generation method, device, equipment and storage medium, which can be applied to the field of big data. The method comprises the following steps: obtaining message information in a target transaction period from a gateway communication area; for the i th transaction scene information, querying n database parameter information corresponding to the transaction field name information from the database according to the transaction field name information; determining the information of the first characteristic parameter from the n database parameters according to the frequency information of the database parameter; performing cluster analysis on the information of the m first characteristic parameters to generate the information of the k second characteristic parameters; for the j th second characteristic parameter information, generating the test element information by querying the transaction field information corresponding to the second characteristic parameter information from the database according to the second characteristic parameter information.

Description

Technical Field

[0001] This disclosure relates to the field of big data, and in particular to a method, apparatus, equipment, medium, and program product for generating test element information. Background Technology

[0002] Currently, the testing work mainly relies on the experience of testers to sort out and prepare test data such as account, card type, and product type required for transactions, and then simulate business scenarios manually or automatically to discover software system defects.

[0003] In the process of realizing the inventive concept of this disclosure, the inventors discovered that the related technologies have problems such as incomplete test data coverage, which leads to inaccurate test results, and the repeated preparation of test data for similar business scenarios, which leads to a decrease in test efficiency. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a method, apparatus, device, medium and program product for generating test element information.

[0005] According to a first aspect of this disclosure, a method for generating test element information is provided, comprising: obtaining message information within a target transaction period from a gateway communication area, wherein the message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2; for the i-th transaction scenario information, retrieving information of n database parameters corresponding to the transaction field name information from a database based on the transaction field name information, wherein the database parameter information includes the frequency information of each database parameter appearing in the message information; where n is a positive integer; and retrieving information of the database parameters based on the transaction field name information. The frequency information is used to determine the information of the first feature parameter from the above n database parameters. The information of the first feature parameter represents the feature parameter information whose relevance to the information of the i-th transaction scenario meets the preset condition, 1≤i≤n; the information of the m first feature parameters is clustered to generate the information of the k second feature parameters, 1≤k≤m; for the information of the j-th second feature parameter, the test element information is generated by querying the transaction field information corresponding to the information of the second feature parameter from the above database, 1≤j≤k, where i, j, and k are all positive integers.

[0006] According to embodiments of this disclosure, the information for determining the first feature parameter from the n database parameters based on the frequency information of the database parameters includes: for each database parameter, calculating the correlation between each database parameter and the i-th transaction scenario information using the chi-square test based on the frequency information of the database parameters; sorting the n database parameters according to the correlation of the n database parameters to obtain sorting result information; and determining the first feature parameter from the n database parameters based on the sorting result information.

[0007] According to an embodiment of this disclosure, the information for determining the first feature parameter from the n database parameters based on the sorting result information includes: determining the sorting position information of each database parameter based on the sorting result information; and determining the database parameter as the first feature parameter when the sorting position information satisfies the preset conditions.

[0008] According to an embodiment of this disclosure, clustering analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, including: constructing an i-th correlation matrix based on the correlation between the first feature parameters and the information of the i-th transaction scenario; performing clustering analysis on the m correlation matrices to obtain k classes of transaction scenarios, wherein each class of transaction scenario includes information of multiple first feature parameters; and generating information of the second feature parameters based on the information of the multiple first feature parameters.

[0009] According to an embodiment of this disclosure, the above-mentioned clustering analysis of the above-mentioned m relevance matrices to obtain k classes of transaction scenarios includes: taking the above-mentioned m relevance matrices, using the transaction scenario information corresponding to the above-mentioned relevance matrices as labels, inputting them into a clustering algorithm model, and outputting clustering results; and determining the classes of the above-mentioned k transaction scenarios based on the above-mentioned clustering results.

[0010] According to an embodiment of this disclosure, for the i-th transaction scenario information, based on the aforementioned transaction field name information, information on n database parameters corresponding to the aforementioned transaction field name information is retrieved from the database, including: for the i-th transaction scenario information, based on the aforementioned transaction field name information, information on all database parameters corresponding to the aforementioned transaction field name information is retrieved from the database; based on the type information of the aforementioned database parameters, all the aforementioned database parameters are classified to obtain a classification result; based on the aforementioned classification result, frequency information of each of the aforementioned database parameters appearing in the aforementioned message information is generated.

[0011] According to an embodiment of this disclosure, the above-mentioned generation of test element information for the j-th second feature parameter, based on the information of the second feature parameter, by querying the database to obtain transaction field information corresponding to the information of the second feature parameter, includes: obtaining target transaction field information based on the information of the j-th second feature parameter, by querying the database to obtain transaction field information corresponding to the information of the second feature parameter; and generating the test element information according to a preset rule based on the target transaction field information.

[0012] The second aspect of this disclosure provides a test element generation apparatus, comprising: an acquisition module, configured to acquire message information of a gateway communication area during a target transaction period, wherein the message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2; a query module, configured to, for the i-th transaction scenario information, query information of n database parameters corresponding to the transaction field name information from a database based on the transaction field name information, wherein the database parameter information includes frequency information of each database parameter appearing in the message information; where n is a positive integer; and a determination module, configured to determine based on the number of... Based on the frequency information of the database parameters, the information of the first feature parameter is determined from the above n database parameters, wherein the information of the first feature parameter represents the feature parameter information whose relevance to the above i-th transaction scenario information meets the preset condition, 1≤i≤n; the first generation module is used to perform cluster analysis on the information of m first feature parameters to generate the information of k second feature parameters, 1≤k≤m; the second generation module is used to generate the above test element information for the information of the j-th second feature parameter by querying the above database to obtain the transaction field information corresponding to the information of the above second feature parameter, 1≤j≤k.

[0013] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the method described above.

[0014] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods described above.

[0015] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0016] According to the test element generation method, apparatus, device, medium, and program product provided in this disclosure, message information within the target transaction period is obtained from the gateway communication area. Then, based on the transaction field name information corresponding to the transaction scenario information in the message information, database parameter information corresponding to the transaction field name information is retrieved from the database to achieve the correspondence between the transaction field name information and the database parameters. Next, based on the frequency information of the database parameters, information on a first feature parameter whose relevance to the transaction scenario information meets preset conditions is determined from the database parameters. Then, cluster analysis is performed on the information of the first feature parameter to generate information on a second feature parameter that further corresponds to the transaction scenario information. Finally, based on the information of the second feature parameter, the corresponding transaction field information is retrieved from the database. This achieves the correspondence between transaction field information, transaction field name information, transaction scenario information, and database parameters. Finally, the required test element information can be generated based on the retrieved transaction field information, thereby realizing the sorting and preparation of test data. By mapping transaction field information, transaction field name information, and database parameters, and then generating test element information based on the queried transaction field information, the business scenario can be fully covered through the sorted test element information. Therefore, the problems of incomplete test data coverage and reduced test efficiency are at least partially solved, achieving the technical effect of comprehensive test data coverage, improving the accuracy of test results, and increasing test efficiency. Attached Figure Description

[0017] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 The illustration schematically depicts application scenarios of the test element generation method, apparatus, device, medium, and program product according to embodiments of the present disclosure;

[0019] Figure 2 A flowchart illustrating a test element generation method according to an embodiment of the present disclosure is shown schematically.

[0020] Figure 3a This illustration schematically shows a message information diagram of a test element generation method according to an embodiment of the present disclosure;

[0021] Figure 3b This illustration schematically shows a parsed message information of a test element generation method according to an embodiment of the present disclosure;

[0022] Figure 4 A schematic diagram illustrating the database parameters of a test element generation method according to an embodiment of the present disclosure is shown.

[0023] Figure 5A schematic diagram illustrating information about the second feature parameter of the test element generation method according to an embodiment of the present disclosure is shown.

[0024] Figure 6 This schematic diagram illustrates the sorting position information by score in the test element generation method according to an embodiment of the present disclosure;

[0025] Figure 7 The diagram illustrates a class-specific relevance matrix of a test element generation method according to an embodiment of the present disclosure.

[0026] Figure 8 A schematic diagram of a class for a test element generation method according to an embodiment of the present disclosure is shown;

[0027] Figure 9 This illustration schematically shows a test element information diagram of a test element generation method according to an embodiment of the present disclosure;

[0028] Figure 10 A schematic diagram illustrating the structure of a test element generation apparatus according to an embodiment of the present disclosure is shown; and

[0029] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing a test element generation method according to an embodiment of the present disclosure. Detailed Implementation

[0030] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0032] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0033] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0034] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure, and application of user personal information comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and there is no violation of public order and good morals.

[0035] In the technical solution disclosed herein, the acquisition, collection, storage, use, processing, transmission, provision, disclosure, and application of data all comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals.

[0036] In business operations, due to the wide variety of transaction types and the large number of communication fields for each type of transaction, the amount of data recorded in transaction records is substantial. Furthermore, not every communication field will affect transaction testing. Therefore, in existing technologies, testers rely on experience to organize and prepare test data, which can lead to incomplete test data coverage and inaccurate test results. Additionally, the repeated preparation of test data for similar transaction scenarios can reduce testing efficiency.

[0037] In view of this, embodiments of the present disclosure provide a test element generation method, including: obtaining message information within a target transaction period from a gateway communication area, wherein the message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2;

[0038] For the i-th transaction scenario information, based on the transaction field name information, retrieve the information of n database parameters corresponding to the transaction field name information from the database. The information of the database parameters includes the frequency information of each database parameter in the message information; n is a positive integer.

[0039] Based on the frequency information of the database parameters, the information of the first feature parameter is determined from n database parameters. The information of the first feature parameter represents the feature parameter information whose relevance to the information of the i-th transaction scenario meets the preset conditions, 1≤i≤n.

[0040] Cluster analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, where 1≤k≤m;

[0041] For the information of the j-th second feature parameter, the test element information is generated by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, where 1≤j≤k.

[0042] Figure 1 The diagram illustrates an application scenario of the test element information generation method according to an embodiment of the present disclosure.

[0043] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0044] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0045] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0046] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0047] It should be noted that the test element information generation method provided in this embodiment can generally be executed by server 105. Correspondingly, the test element information generation device provided in this embodiment can generally be located in server 105. The test element information generation method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the test element information generation device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0048] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0049] The following will be based on Figure 1 The described scene, through Figures 2-6 The method for generating test element information according to the disclosed embodiments is described in detail.

[0050] Figure 2 A flowchart illustrating a method for generating test element information according to an embodiment of the present disclosure is shown.

[0051] like Figure 2 As shown, the test element information generation method of this embodiment 200 includes operations S210 to S250.

[0052] In operation S210, message information within the target transaction period is obtained from the gateway communication area. The message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2.

[0053] According to embodiments of this disclosure, for example, the acquired message information may be production transaction data collected using a transaction recording and playback tool. The target transaction period may be a high-frequency transaction period. Transaction field name information may include name information such as region code information, branch number information, teller number information, and transaction code information. Transaction scenario information corresponds to the transaction field name information. Transaction scenario information may include information such as transaction code.

[0054] According to embodiments of this disclosure, for example, after obtaining message information, the message information can be parsed to obtain the transaction field name information and the corresponding numerical information in the message information.

[0055] Figure 3aThe illustration shows a message information diagram of a test element generation method according to an embodiment of the present disclosure.

[0056] like Figure 3a As shown, the message information may include transaction field name information and the corresponding numerical information of the transaction field name information.

[0057] Figure 3b The illustration shows a schematic diagram of the parsed message information of the test element generation method according to an embodiment of the present disclosure.

[0058] like Figure 3b As shown, the parsed message information includes transaction field name information such as region code, branch number, teller number, and transaction code. The numerical information can include the values ​​corresponding to the transaction field name information.

[0059] According to embodiments of this disclosure, for example, sensitive information included in the message information can be desensitized by masking. Sensitive information may include name information, password information, and email address, among other things.

[0060] According to embodiments of this disclosure, for example, different numerical information corresponding to the card type field in the transaction field can represent different types of cards, which may include: domestic cards, overseas cards, UnionPay cards, foreign cards, etc. Therefore, based on the numerical information, the required card type can be quickly obtained.

[0061] In operation S220, for the i-th transaction scenario information, based on the transaction field name information, the information of n database parameters corresponding to the transaction field name information is retrieved from the database. The information of the database parameters includes the frequency information of each database parameter in the message information; n is a positive integer.

[0062] According to embodiments of this disclosure, for example, database parameters may include table names and field names. Therefore, the information of database parameters may include table name information and field name information. The database parameter may be T1_C1, where T1 may be a table name and C1 may be a field name.

[0063] According to embodiments of this disclosure, for example, since the transactions recorded are from high-frequency periods, the data volume is large. Therefore, the processing of message information and the process of retrieving database parameters from the database according to the transaction scenario information are time-consuming. Therefore, Python multithreading programming can be used to divide this process into 256 threads for parallel processing, thereby compressing the time consumed by this process and saving time.

[0064] In operation S230, based on the frequency information of the database parameters, the information of the first feature parameter is determined from n database parameters. The information of the first feature parameter represents the feature parameter information whose correlation with the information of the i-th transaction scenario meets the preset conditions, 1≤i≤n.

[0065] According to embodiments of this disclosure, for example, frequency information can be the frequency of occurrence of a database parameter in transactions included in a transaction code. If the frequency meets a preset condition, the transaction code can be considered sufficiently relevant to the database parameter, and thus the database parameter can be used as the first characteristic parameter of the transaction code.

[0066] According to embodiments of this disclosure, for example, the database parameters can be T1_C1, T2_C2, and T3_C3, the transaction code can be M0001, and in the transaction corresponding to M0001, the frequency information of T1_C1 can be 5, the frequency information of T2_C2 can be 8, and the frequency information of T3_C3 can be 1. When the preset condition for the frequency information is 5, T1_C1 and T2_C2 can be used as the first feature parameters.

[0067] Figure 4 A schematic diagram of the database parameters of the test element generation method according to an embodiment of the present disclosure is shown.

[0068] like Figure 4 As shown, database parameters can include table name information and field name information. Figure 4 The numerical information in the data can be the numerical information corresponding to the database parameters.

[0069] According to embodiments of this disclosure, for example, the information of the first feature parameter may include a transaction code, a database parameter whose relevance to the transaction code meets a preset condition, and frequency information corresponding to the database parameter.

[0070] In operation S240, cluster analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, where 1≤k≤m.

[0071] According to embodiments of this disclosure, for example, information containing a second feature parameter can be generated by performing cluster analysis on the transaction codes contained in the information of the first feature parameter.

[0072] According to embodiments of this disclosure, for example, cluster analysis can be performed using the Affinity Propagation nearest neighbor algorithm, which is a graph-based clustering algorithm in machine learning theory.

[0073] According to embodiments of this disclosure, for example, the information of the second feature parameter may include database parameters, transaction codes, and numerical information. The numerical information may be the numerical information corresponding to the transaction code and the first feature parameter.

[0074] According to embodiments of this disclosure, for example, the transaction codes after cluster analysis may include M0001 and M0002, and the first feature parameters may include T1_C1 and T2_C2. Furthermore, the numerical information corresponding to M0001 and T1_C1 may be 10, and the numerical information corresponding to T2_C2 may be 5. Similarly, the numerical information corresponding to M0002 and T1_C1 may be 5, and the numerical information corresponding to T2_C2 may be 15.

[0075] Figure 5 The diagram illustrates information about the second feature parameter of the test element generation method according to an embodiment of the present disclosure.

[0076] like Figure 5 As shown, transaction codes can include M0001, M0002, M0003, and M0004, and database parameters can include T1_C1, T2_C2, and T3_C3. M0001 can correspond to a value of 10 with T1_C1, 5 with T2_C2, and 2 with T3_C3; M0002 can correspond to a value of 5 with T1_C1, 15 with T2_C2, and 1 with T3_C3; M0003 can correspond to a value of 3 with T1_C1, 1 with T2_C2, and 13 with T3_C3; M0004 can correspond to a value of 1 with T1_C1, 6 with T2_C2, and 2 with T3_C3.

[0077] In operation S250, for the information of the j-th second feature parameter, the test element information is generated by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, where 1≤j≤k.

[0078] According to embodiments of this disclosure, for example, test element information may include information such as card type and account type.

[0079] According to embodiments of this disclosure, message information within the target transaction period is obtained from the gateway communication area. Then, based on the transaction field name information corresponding to the transaction scenario information in the message information, database parameter information corresponding to the transaction field name information is retrieved from the database to achieve the correspondence between the transaction field name information and the database parameters. Then, based on the frequency information of the database parameters, information of a first feature parameter whose relevance to the transaction scenario information meets preset conditions is determined from the database parameters. Then, cluster analysis is performed on the information of the first feature parameter to generate information of a second feature parameter that further corresponds to the transaction scenario information. Then, based on the information of the second feature parameter, the transaction field information corresponding to the information of the second feature parameter is retrieved from the database. This achieves the correspondence between the transaction field information, the transaction field name information, the transaction scenario information, and the database parameters. Finally, the required test element information can be generated based on the retrieved transaction field information, thereby realizing the sorting and preparation of test data. By mapping transaction field information, transaction field name information, and database parameters, and then generating test element information based on the queried transaction field information, the business scenario can be fully covered through the sorted test element information. Therefore, the problems of incomplete test data coverage and reduced test efficiency are at least partially solved, achieving the technical effect of comprehensive test data coverage, improving the accuracy of test results, and increasing test efficiency.

[0080] According to embodiments of this disclosure, determining information for a first feature parameter from n database parameters based on frequency information of database parameters includes:

[0081] For each database parameter, based on the frequency information of the database parameter, the chi-square test is used to calculate the correlation between each database parameter and the i-th transaction scenario information;

[0082] Based on the relevance of n database parameters, sort the n database parameters to obtain the sorting result information;

[0083] Based on the sorting results, determine the information of the first feature parameter from n database parameters.

[0084] According to embodiments of this disclosure, for example, the same transaction scenario may correspond to different database parameters. After determining the relevance between the database parameters and the transaction scenario information, the database parameters can be sorted according to their relevance to obtain sorting result information.

[0085] According to embodiments of this disclosure, for example, the relevance can be scored using the chi-square test based on frequency information. In the transaction corresponding to M1233, the score of T3_C1 can be 152.757759, the score of T2_C5 can be 19.393861, the score of T2_C8 can be 17.240424, and the score of T1_C1 can be 3.121773. Then, the database parameters can be sorted from high to low based on the scores, with T3_C1 ranking first, T2_C5 ranking second, T2_C8 ranking third, and T1_C1 ranking fourth.

[0086] Figure 6 The diagram illustrates a sorting position information by score in a test element generation method according to an embodiment of the present disclosure.

[0087] like Figure 6 As shown, the sorting position information can be the sorting position information of the database parameters in the transaction corresponding to M1233. The score of T3_C1 can be 152.757759, the score of T2_C5 can be 19.393861, the score of T2_C8 can be 17.240424, and the score of T1_C1 can be 3.121773. Then, the database parameters can be sorted from high to low according to the scores. T3_C1 can be ranked first, T2_C5 can be ranked second, T2_C8 can be ranked third, and T1_C1 can be ranked fourth.

[0088] According to embodiments of this disclosure, for each database parameter, based on the frequency information of the database parameter, the chi-square test is used to calculate the correlation between each database parameter and the i-th transaction scenario information. Then, based on the correlation between the n database parameters and the i-th transaction scenario information, the n database parameters corresponding to the transaction scenario information can be sorted to obtain sorting result information. Based on the sorting result information, the information of the first feature parameter is determined from the n database parameters. Since the database parameters are sorted according to their correlation with the transaction scenario information, the database parameters whose correlation with the i-th transaction scenario information meets the requirements can be determined. Therefore, the database parameters whose correlation meets the preset conditions can be used as the first feature parameter. By determining the correlation between the transaction scenario information and the database parameters, the feature parameter whose correlation with the transaction scenario information meets the preset conditions can be determined, which can improve the coverage of test data, the accuracy of test results, and test efficiency.

[0089] According to embodiments of this disclosure, determining the information of the first feature parameter from n database parameters based on the sorting result information includes:

[0090] Based on the sorting results, determine the sorting position information for each database parameter;

[0091] If the sorting position information meets the preset conditions, the database parameter is determined as the first feature parameter.

[0092] According to embodiments of this disclosure, for example, n can be 2. In the same transaction scenario, two database parameters corresponding to the transaction scenario can be selected in descending order of relevance, and the two selected database parameters can be used as the first feature parameters.

[0093] According to an embodiment of this disclosure, for example, T2_C2 can be ranked first, T1_C1 can be ranked second, and T3_C3 can be ranked third. When the preset condition for the sorting position information is set to the first two positions, T2_C2 and T1_C1 are determined as the first feature parameters.

[0094] According to embodiments of this disclosure, by determining the sorting position information of each database parameter based on the sorting result information, the order of relevance between the database parameters and the transaction scenario information, as well as the position of the database parameters in the sorting, can be determined. If the sorting position information meets preset conditions, the database parameters whose relevance to the transaction scenario information meets the preset conditions can be determined as the first feature parameters. This realizes the correspondence between the transaction scenario information and the feature parameters, which can improve the coverage of test data, the accuracy of test results, and the test efficiency.

[0095] According to embodiments of this disclosure, cluster analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, including:

[0096] Based on the information of the first feature parameter, construct the i-th correlation matrix according to the correlation between the first feature parameter and the information of the i-th transaction scenario;

[0097] Cluster analysis is performed on m relevance matrices to obtain k classes of transaction scenarios, where each class of transaction scenario includes information on multiple first feature parameters;

[0098] Information about the second feature parameter is generated based on information from multiple first feature parameters.

[0099] According to embodiments of this disclosure, for example, the chi-square test score of the first feature parameter corresponding to the i-th transaction scenario can be used as a matrix element to construct the i-th relevance matrix, which is then substituted into the Affinity-Propagation clustering algorithm for clustering calculation. Since the first feature parameter can represent the i-th transaction scenario, the clustering result can be a class of k transaction scenarios.

[0100] Figure 7The diagram illustrates a class-specific relevance matrix of a test element generation method according to an embodiment of the present disclosure.

[0101] like Figure 7 As shown, the relevance matrix can include transaction codes, database parameters, and numerical information. The transaction codes corresponding to category 1 can include M1101, M1222, and M0031, and the database parameters can include T1_C1, T2_C3, and T3_C2. The numerical information corresponding to M1101 and T1_C1 can be 1, the numerical information corresponding to T2_C3 can be 1, and the numerical information corresponding to T3_C2 can be 20; the numerical information corresponding to M1222 and T1_C1 can be 1, the numerical information corresponding to T2_C3 can be 1, and the numerical information corresponding to T3_C2 can be 1; the numerical information corresponding to M0031 and T1_C1 can be 100, the numerical information corresponding to T2_C3 can be 1, and the numerical information corresponding to T3_C2 can be 1.

[0102] According to embodiments of this disclosure, for example, after determining the relevance matrix corresponding to a class, a first feature parameter in that class can be used as a second feature parameter.

[0103] According to embodiments of this disclosure, for the information of the first feature parameter, based on the correlation between the first feature parameter and the information of the i-th transaction scenario, an i-th correlation matrix is ​​constructed. The m correlation matrices are clustered to obtain the classes of k transaction scenarios related to the transaction scenario information. Then, the class to which the first feature parameter corresponding to the transaction scenario information belongs can be determined through the transaction scenario information. Furthermore, based on the information of multiple first feature parameters, the information of the second feature parameter belonging to the class of the transaction scenario is generated, realizing the correspondence between the class of the transaction scenario and the second feature parameter, thereby realizing a further correspondence between the transaction scenario information and the feature parameter, which can improve the coverage of test data, the accuracy of test results, and the test efficiency.

[0104] According to embodiments of this disclosure, cluster analysis is performed on m relevance matrices to obtain k classes of transaction scenarios, including:

[0105] Take m relevance matrices, use the transaction scenario information corresponding to the relevance matrices as labels, input them into the clustering algorithm model, and output the clustering results;

[0106] Based on the clustering results, determine the classes of k transaction scenarios.

[0107] According to embodiments of this disclosure, for example, the transaction code, which serves as a label, can be input into a clustering algorithm model, and the class to which the transaction code belongs can be determined based on the clustering results obtained through the clustering algorithm model.

[0108] Figure 8 A schematic diagram of a class of test element generation method according to an embodiment of the present disclosure is shown.

[0109] like Figure 8 As shown, after cluster analysis, M0001, M0005, and M0006 can belong to class 1, M0002 and M0004 can belong to class 2, and M0003 and M0007 can belong to class 3.

[0110] According to embodiments of this disclosure, by inputting m relevance matrices, with the transaction scenario information corresponding to the relevance matrices as labels, into a clustering algorithm model, the clustering results of the relevance matrices can be output. Based on the clustering results, k classes of transaction scenarios are determined. The second feature parameter can then be determined through the class of the transaction scenario, thus realizing the correspondence between the class of the transaction scenario and the second feature parameter, and further realizing the correspondence between transaction scenario information and feature parameters. This can improve the coverage of test data, the accuracy of test results, and the test efficiency.

[0111] According to embodiments of this disclosure, for the i-th transaction scenario information, based on the transaction field name information, information on n database parameters corresponding to the transaction field name information is retrieved from the database, including:

[0112] For the i-th transaction scenario information, based on the transaction field name information, query the database to obtain information on all database parameters corresponding to the transaction field name information;

[0113] Based on the type information of the database parameters, all database parameters are classified to obtain the classification results;

[0114] Based on the classification results, the frequency information of each database parameter appearing in the message information is generated.

[0115] According to embodiments of this disclosure, for example, information on all database parameters corresponding to a transaction code can be retrieved from the database based on the transaction code.

[0116] According to embodiments of this disclosure, for example, database parameters can be classified according to their corresponding transaction codes. For example, if the transaction code is M0001, the database parameters corresponding to M0001 are T1_C1 and T2_C2, and the numerical information corresponding to T1_C1 and T2_C2 also corresponds to M0001.

[0117] According to embodiments of this disclosure, for example, after determining the classification result, the frequency of database parameters appearing in the corresponding transaction code can be determined based on the classification result, and then frequency information can be generated based on the frequency.

[0118] According to embodiments of this disclosure, for the i-th transaction scenario information, based on the transaction field name information, information on all database parameters corresponding to the transaction field name information is retrieved from the database. Then, based on the type information of the database parameters, all database parameters are classified to obtain classification results. Based on the classification results, frequency information of each database parameter in the message information is generated. Since the frequency information of database parameters in the transaction code is determined by classifying the database parameters, the frequency information of the database parameters corresponding to the transaction code is determined. Then, the database parameters that meet the preset conditions of relevance to the transaction code can be determined by the frequency information, which can improve the coverage of test data, the accuracy of test results, and the test efficiency.

[0119] According to embodiments of this disclosure, for the information of the j-th second feature parameter, test element information is generated by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, including:

[0120] For the information of the j-th second feature parameter, the target transaction field information is obtained by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter.

[0121] Based on the target transaction field information, test element information is generated according to preset rules.

[0122] According to embodiments of this disclosure, for example, the corresponding transaction field information can be obtained from the database based on the database parameters included in the second feature parameter, and then the transaction field information can be determined as the target transaction field information.

[0123] According to embodiments of this disclosure, for example, after determining the target transaction field information, the target transaction field information can be processed according to preset rules. This can be done by obtaining business elements, such as card type, account type, etc., based on the target transaction field information, and using the obtained business elements as element information of test data.

[0124] Figure 9 The diagram illustrates test element information of a test element generation method according to an embodiment of the present disclosure.

[0125] like Figure 9 As shown, the test element information may include the external identifier of the medium, the medium type, the account status, and the type of medium authentication. The medium type can be physical, and physical mediums may include information such as card number, account number, and first-generation U-shield. The account status can be normal. The type of medium authentication may include U-shield, IC card frame count, electronic password generator, and dynamic password generator, etc.

[0126] According to embodiments of this disclosure, for the information of the j-th second feature parameter, based on the information of the second feature parameter after cluster analysis, the transaction field information corresponding to the information of the second feature parameter is obtained by querying the database. Then, the transaction field information can be determined as the target transaction field information. Then, based on the target transaction field information, test element information is generated according to preset rules. This realizes the determination of the target transaction field information and the generation of test element information based on the target transaction field information obtained by querying the information of the second feature parameter. Since the transaction scenario information is mapped to the second feature parameter, the test data can be fully covered, improving the accuracy of the test results and the test efficiency.

[0127] Based on the above-described method for generating test element information, this disclosure also provides a device for generating test element information. The following will be combined with... Figure 10 The device is described in detail.

[0128] Figure 10 A schematic block diagram of a test element information generation apparatus according to an embodiment of the present disclosure is shown.

[0129] like Figure 10 As shown, the test element information generation device 1000 of this embodiment includes an acquisition module 1010, a query module 1020, a determination module 1030, a first generation module 1040, and a second generation module 1050.

[0130] The acquisition module 1010 is used to acquire message information of the gateway communication area within the target transaction period. The message information includes m transaction scenario information items and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2. In one embodiment, the acquisition module 1010 can be used to execute the operation S210 described above, which will not be repeated here.

[0131] The query module 1020 is used to retrieve information on n database parameters corresponding to the transaction field name information from the database for the i-th transaction scenario information, based on the transaction field name information. The database parameter information includes the frequency information of each database parameter appearing in the message information; n is a positive integer. In one embodiment, the query module 1020 can be used to perform the operation S220 described above, which will not be repeated here.

[0132] The determining module 1030 is used to determine the information of a first feature parameter from n database parameters based on the frequency information of the database parameters. The information of the first feature parameter represents the feature parameter information whose relevance to the information of the i-th transaction scenario meets a preset condition, where 1 ≤ i ≤ n. In one embodiment, the determining module 1030 can be used to perform the operation S230 described above, which will not be repeated here.

[0133] The first generation module 1040 is used to perform cluster analysis on the information of m first feature parameters to generate information of k second feature parameters, where 1≤k≤m. In one embodiment, the first generation module 1040 can be used to perform the operation S240 described above, which will not be repeated here.

[0134] The second generation module 1050 is used to generate test element information based on the information of the j-th second feature parameter, by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, where 1≤j≤k. In one embodiment, the second generation module 1050 can be used to perform the operation S250 described above, which will not be repeated here.

[0135] According to embodiments of this disclosure, the determining module includes a calculation unit, a sorting unit, and a first determining unit. The calculation unit is used to calculate the relevance between each database parameter and the i-th transaction scenario information based on the frequency information of the database parameters using a chi-square test. The sorting unit is used to sort the n database parameters according to their relevance to obtain sorting result information. The first determining unit is used to determine the information of a first feature parameter from the n database parameters based on the sorting result information.

[0136] According to embodiments of this disclosure, the first determining unit includes a first determining subunit and a second determining subunit. The first determining subunit is used to determine the sorting position information of each database parameter based on the sorting result information; the second determining subunit is used to determine the database parameter as a first feature parameter if the sorting position information meets preset conditions.

[0137] According to embodiments of this disclosure, the first generation module includes a construction unit, a first acquisition unit, and a first generation unit. The construction unit is used to construct an i-th relevance matrix based on the information of the first feature parameters and the relevance between the first feature parameters and the information of the i-th transaction scenario. The first acquisition unit is used to perform cluster analysis on the m relevance matrices to obtain k classes of transaction scenarios, wherein each class of a transaction scenario includes information of multiple first feature parameters. The first generation unit is used to generate information of second feature parameters based on the information of the multiple first feature parameters.

[0138] According to embodiments of this disclosure, the first acquisition unit includes an output unit and a third determination subunit. The output unit is used to input m relevance matrices, with the transaction scenario information corresponding to the relevance matrices as labels, into a clustering algorithm model and output clustering results. The third determination subunit is used to determine the classes of k transaction scenarios based on the clustering results.

[0139] According to embodiments of this disclosure, the query module includes a query unit, a second acquisition unit, and a second generation unit. The query unit is used to query the database for the i-th transaction scenario information, based on the transaction field name information, to obtain information on all database parameters corresponding to the transaction field name information. The second acquisition unit is used to classify all database parameters according to their type information to obtain a classification result. The second generation unit is used to generate frequency information for each database parameter appearing in the message information based on the classification result.

[0140] According to embodiments of this disclosure, the second generation module includes a third acquisition unit and a third generation unit. The third acquisition unit is used to obtain target transaction field information based on the information of the j-th second feature parameter by querying a database to obtain transaction field information corresponding to the information of the second feature parameter. The third generation unit is used to generate test element information according to the target transaction field information and a preset rule.

[0141] According to embodiments of this disclosure, any multiple modules among the acquisition module 1010, query module 1020, determination module 1030, first generation module 1040, and second generation module 1050 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the acquisition module 1010, query module 1020, determination module 1030, first generation module 1040, and second generation module 1050 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 1010, query module 1020, determination module 1030, first generation module 1040 and second generation module 1050 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0142] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing a test element generation method according to an embodiment of the present disclosure.

[0143] like Figure 11As shown, an electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage portion 1108 into a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0144] RAM 1103 stores various programs and data required for the operation of electronic device 1100. Processor 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Processor 1101 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 1102 and / or RAM 1103. It should be noted that the programs may also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0145] According to embodiments of this disclosure, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to a bus 1104. The electronic device 1100 may also include one or more of the following components connected to the I / O interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1110 as needed so that computer programs read from it can be installed into the storage section 1108 as needed.

[0146] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0147] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 1102 and / or RAM 1103 and / or one or more memories other than ROM 1102 and RAM 1103 described above.

[0148] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the item recommendation method provided in the embodiments of this disclosure.

[0149] When the computer program is executed by the processor 1101, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0150] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1109, and / or installed from the removable medium 1111. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0151] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by processor 1101, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0152] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0153] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0154] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0155] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for generating test element information, comprising: Obtain message information within the target transaction period from the gateway communication area, wherein the message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2; For the i-th transaction scenario information, based on the transaction field name information, information on n database parameters corresponding to the transaction field name information is retrieved from the database. The information on the database parameters includes the frequency information of each database parameter appearing in the message information; n is a positive integer. Based on the frequency information of the database parameters, the information of the first feature parameter is determined from the n database parameters, wherein the information of the first feature parameter represents the feature parameter information whose relevance to the i-th transaction scenario information meets the preset condition, 1≤i≤n; Cluster analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, where 1≤k≤m; For the information of the j-th second feature parameter, the test element information is generated by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, where 1≤j≤k.

2. The method according to claim 1, wherein, The step of determining the information of the first feature parameter from the n database parameters based on the frequency information of the database parameters includes: For each of the database parameters, the correlation between each database parameter and the i-th transaction scenario information is calculated using the chi-square test based on the frequency information of the database parameter. Based on the relevance of n database parameters, the n database parameters are sorted to obtain sorting result information; Based on the sorting result information, the information of the first feature parameter is determined from the n database parameters.

3. The method according to claim 2, wherein, The step of determining the first feature parameter from the n database parameters based on the sorting result information includes: Based on the sorting result information, determine the sorting position information of each database parameter; If the sorting position information meets the preset conditions, the database parameter is determined as the first feature parameter.

4. The method according to claim 1, wherein, Cluster analysis is performed on the information of m first feature parameters to generate information of k second feature parameters, including: Based on the information of the first feature parameter, a first correlation matrix is ​​constructed according to the correlation between the first feature parameter and the information of the i-th transaction scenario; Cluster analysis is performed on the m relevance matrices to obtain k classes of transaction scenarios, wherein each class of transaction scenario includes information on multiple first feature parameters; Information about the second feature parameters is generated based on the information of the plurality of first feature parameters.

5. The method according to claim 4, wherein, The step of performing cluster analysis on the m relevance matrices to obtain k classes of transaction scenarios includes: The m relevance matrices are used as labels, with the transaction scenario information corresponding to the relevance matrices as labels, and the clustering algorithm model is input to output the clustering results. Based on the clustering results, the classes of the k transaction scenarios are determined.

6. The method according to claim 1, wherein, For the i-th transaction scenario information, based on the transaction field name information, information on n database parameters corresponding to the transaction field name information is retrieved from the database, including: For the i-th transaction scenario information, based on the transaction field name information, query the database to obtain information on all database parameters corresponding to the transaction field name information; Based on the type information of the database parameters, all database parameters are classified to obtain classification results; Based on the classification results, frequency information of each of the database parameters appearing in the message information is generated.

7. The method according to claim 1, wherein, The step of generating the test element information based on the information of the j-th second feature parameter, by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, includes: For the information of the j-th second feature parameter, the target transaction field information is obtained by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter. Based on the target transaction field information, the test element information is generated according to preset rules.

8. A test element generation device, comprising: The acquisition module is used to acquire message information of the gateway communication area during the target transaction period, wherein the message information includes m transaction scenario information and transaction field name information corresponding to the transaction scenario information, where m is a positive integer greater than 2; The query module is used to query the database for the i-th transaction scenario information based on the transaction field name information to obtain information on n database parameters corresponding to the transaction field name information. The information on the database parameters includes the frequency information of each database parameter appearing in the message information; n is a positive integer. The determination module is used to determine the information of the first feature parameter from the n database parameters based on the frequency information of the database parameters, wherein the information of the first feature parameter represents the feature parameter information whose relevance to the i-th transaction scenario information meets the preset condition, 1≤i≤n; The first generation module is used to perform cluster analysis on the information of m first feature parameters to generate information of k second feature parameters, where 1≤k≤m; The second generation module is used to generate the test element information based on the information of the j-th second feature parameter by querying the database to obtain the transaction field information corresponding to the information of the second feature parameter, where 1≤j≤k.

9. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images