An interface isolation controlled automated testing method and system

Abstract

This invention relates to an automated testing method and system for interface isolation control. It configures status tags for mock interfaces and uses these tags to set sets for testing. Defects are categorized according to a preset decision model, and external dependency masking is selectively implemented. Interface isolation control helps automatically identify the reasons for script failures, effectively reducing the frequency of manual troubleshooting of automated test script failures. This improves the stability of automated testing while ensuring the interaction frequency between the tested system and real services. Alarms are only triggered when real defects are detected, thereby increasing the proportion of valid defects discovered by the automated testing system.

Description

Technical Field

[0001] This invention relates to the fields of network testing, automated testing, and Mock data processing, and in particular to an automated testing method and system for interface isolation control. Background Technology

[0002] With the development of the internet, automated testing has gradually replaced manual testing for some of the more arduous tasks. However, existing automated tests are susceptible to the influence of external dependencies. In actual testing, due to the numerous backend services the tested system depends on, long call chains, and factors such as the network environment, the success rate of automated testing is highly unstable. According to relevant statistics, approximately 80% of automated test failures are due to network or dependent service issues, rather than valid defects. Manually troubleshooting automated test script failures would waste a significant amount of time. To isolate external dependencies from automated testing, a common method is to use mocking techniques to simulate external dependent services or interfaces. Open-source tools such as MockServer and Moco can simulate third-party services, thereby eliminating the impact of the external environment on automated testing. Alternatively, a separate, independent runtime environment can be built for automated testing to completely isolate the automation from the influence of external dependent services or network conditions.

[0003] However, setting up a separate test environment is too costly and requires real-time maintenance of updates and iterations for various system services, undoubtedly increasing the workload for development and testing. Existing mock technologies often isolate external dependencies by simulating external third-party services. Such solutions lead to the system under test only interacting with the simulated third-party service during automated testing, failing to detect system defects caused by changes in the third-party service. Alternatively, they control the validity of mock data with a switch; if the switch is off, the caller can interact with the real service. Such solutions require the caller to actively configure the validity of the mock data, which is not intelligent enough and cannot be directly applied to automated testing. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes an automated testing method and system with interface isolation control. By using interface isolation control, the system automatically identifies the reasons for script execution failures, thereby effectively reducing the frequency of manual troubleshooting of automated test script failures. This improves the stability of automated testing while ensuring the interaction frequency between the tested system and the real service. Alarms are only issued when real defects are detected, thus increasing the proportion of valid defects discovered by the automated testing system.

[0005] To achieve the above objectives, the technical solution adopted by the present invention includes:

[0006] An automated testing method for interface isolation control, characterized by comprising:

[0007] S1. Configure status labels for the Mock interface, the status labels including normal response interface and abnormal response interface;

[0008] S2. Scan the status labels of the Mock interfaces of the system under test, and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface.

[0009] S3. Use a preset decision model to analyze the test interface set and determine the abnormal result classification of the Mock interface. The abnormal result classification includes Class I abnormality, Class II abnormality and unknown abnormality. Class I abnormality corresponds to the effective defect of the tested system, Class II abnormality corresponds to the third-party service defect of the non-tested system, and unknown abnormality corresponds to other defects other than Class I abnormality and Class II abnormality.

[0010] S4. When the abnormal result of the Mock interface is classified as a type of abnormality, output the corresponding abnormal information and generate a matching alarm.

[0011] S5. When the exception result of the Mock interface is classified as a type II exception, isolate the external dependency corresponding to the Mock interface and re-execute step S3.

[0012] S6. When the exception result of the Mock interface is classified as an unknown exception, stop the test execution and output the unknown exception information.

[0013] Furthermore, the preset decision model includes an encapsulated AC automaton model.

[0014] Furthermore, the encapsulated AC automaton model is created by the following method:

[0015] A1. Configure exception identifiers, including environment exception identifiers, program logic exception identifiers, and other exception identifiers;

[0016] A2. Configure corresponding identifier data based on the anomaly identifier to form an anomaly detection model;

[0017] A3. Construct corresponding trie trees based on the different anomaly identifiers of the anomaly detection model;

[0018] A4. Construct mismatch pointers for each node in the trie;

[0019] A5. Configure the decision set for the output results of each trie.

[0020] Furthermore, the decision set for configuring the output results of each trie includes:

[0021] Set the values ​​to be taken from the output of the trie, where the range of the output values ​​is 1 or 0;

[0022] Configure the combination decision of the trie output results among the various exception identifiers.

[0023] Furthermore, the combination decision of the trie output results among the various anomaly identifiers includes:

[0024] When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly.

[0025] When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class I anomaly.

[0026] When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly.

[0027] When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class II anomaly.

[0028] When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as other anomalies.

[0029] When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class II anomaly.

[0030] When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class I anomaly.

[0031] When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly.

[0032] Furthermore, the encapsulated AC automaton model also includes:

[0033] Configure independent execution threads for each trie.

[0034] This invention also relates to an automated testing system for interface isolation control, characterized in that it comprises:

[0035] The status label configuration module is used to configure and modify the status labels used for the Mock interface;

[0036] The test set generation module is used to scan the Mock interface status labels of the system under test and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface.

[0037] The anomaly classification and judgment module is used to analyze the test interface set using a preset decision model and determine the anomaly result classification of the Mock interface;

[0038] The execution feedback module is used to perform corresponding operations and provide matching information based on the abnormal results of the Mock interface.

[0039] The present invention also relates to a computer-readable storage medium, characterized in that the storage medium stores a computer program, which, when executed by a processor, implements the above-described method.

[0040] The present invention also relates to an electronic device, characterized in that it includes a processor and a memory;

[0041] The memory is used to store state tags and preset decision models;

[0042] The processor is used to execute the above method by invoking a status label and a preset decision model.

[0043] The present invention also relates to a computer program product, including a computer program and / or instructions, characterized in that the computer program and / or instructions, when executed by a processor, implement the steps of the above-described method.

[0044] The beneficial effects of this invention are as follows:

[0045] The automated testing method and system employing interface isolation control as described in this invention avoids the drawbacks of high time complexity and long processing times associated with traditional internet anomaly detection methods. By using interface isolation control, it automatically identifies the causes of script failures, effectively reducing the frequency of manual troubleshooting. This improves the stability of automated testing while ensuring the interaction frequency between the tested system and real services. Alarms are only triggered when real defects are detected, thereby increasing the proportion of valid defects found by the automated testing system and improving anomaly detection accuracy. Furthermore, this invention is applicable to different interface automated testing frameworks and supports real-time log analysis and decision-making based on failed test cases during automated execution. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the automated testing method for interface isolation control of the present invention.

[0047] Figure 2 This is a schematic diagram of the automated testing system structure for interface isolation control according to the present invention. Detailed Implementation

[0048] To better understand the content of this invention, a detailed description will be provided in conjunction with the accompanying drawings and embodiments.

[0049] The first aspect of this invention relates to a process flow as follows: Figure 1 The automated testing method for interface isolation control shown includes:

[0050] S1. Configure status labels for the Mock interface, including normal response interface and abnormal response interface, to distinguish different types of Mock interfaces.

[0051] Preferably, for ease of management, mock interfaces with abnormal responses can be stored in the expectations file, while mock interfaces with normal responses should be stored in the expectationsSuccess.json file.

[0052] By simulating normal responses from third-party interfaces, external dependencies of the automation system can be isolated. Therefore, only mock interfaces with normal responses need to have their validity modified in real time. Mock interfaces with abnormal responses are for anomaly testing. Due to the existence of the MockServer matching mechanism, it can be targeted only to a single caller and can be configured to remain effective without dynamic control.

[0053] Secondly, all third-party interfaces created through MockServer need to specify their system tag for easy batch modification.

[0054] S2. Scan the status labels of the Mock interfaces of the system under test, and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface.

[0055] Preferably, an updateMockAPi interface can be added on top of the Mock framework, which supports batch modification of the validity of the Mock interface through specified system tags for use by external callers.

[0056] S3. Use a preset decision model to analyze the test interface set and determine the abnormal result classification of the Mock interface. The abnormal result classification includes Class I abnormality, Class II abnormality and unknown abnormality. Class I abnormality corresponds to the valid defect of the tested system, Class II abnormality corresponds to the defect of third-party service of non-tested system, and unknown abnormality corresponds to other defects other than Class I abnormality and Class II abnormality.

[0057] Specifically, the preset decision model includes an encapsulated Aho-Corasick automaton model, which is preferably created using the following method:

[0058] A1. Configure exception identifiers, including environmental exception identifiers, program logic exception identifiers, and other exception identifiers.

[0059] A2. Configure corresponding identifier data based on the anomaly identifiers to form an anomaly detection model. Among them, the environment anomaly identifiers contain keywords for common anomaly information caused by service deployment and network problems; the program logic anomaly identifiers store common Java runtime anomalies; and other anomaly identifiers (personalized anomaly identifiers) store high-frequency error log information that occurs when the tested system calls external systems.

[0060] A3. Construct corresponding tries based on the different anomaly identifiers of the anomaly detection model. Tries can utilize the common prefixes of strings to minimize unnecessary character comparisons, while saving storage space and thus improving query efficiency. Ideally, three tries can be generated, requiring only one initialization.

[0061] A4. Construct mismatch pointers for each node in the trie. If the target string fails to match at a certain node, the mismatch pointer will be redirected to another related branch to avoid redundant searching from the beginning.

[0062] A5. Configure the decision set of each dictionary output result. First, set the value of the dictionary output result. The range of the dictionary output result value is 1 or 0. Then, configure the combination decision of the dictionary output results among each exception identifier.

[0063] Preferably, for the classification of exception results from the Mock interface, the optional combined decision includes: when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 1, and the output of other exception identifier dictionaries is 1, the exception result is classified as a Class I exception; when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 1, and the output of other exception identifier dictionaries is 0, the exception result is classified as a Class I exception; when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 0, and the output of other exception identifier dictionaries is 1, the exception result is classified as a Class I exception; when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 0, and the output of other exception identifier dictionaries is 1, the exception result is classified as a Class I exception; when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 0, and the output of other exception identifier dictionaries is 1, the exception result is classified as a Class I exception; when the output of the environment exception identifier dictionary is 1, the output of the program logic exception identifier dictionary is 0, and the output of other exception identifier dictionaries is 1, the exception result is classified as a Class I exception. When the value is 0, the abnormal result is classified as a Category II abnormality; when the output values ​​of the environmental abnormality identifier dictionary, the program logic abnormality identifier dictionary, and the other abnormality identifier dictionary are all 0, the abnormal result is classified as an other abnormality; when the output values ​​of the environmental abnormality identifier dictionary, the program logic abnormality identifier dictionary, and the other abnormality identifier dictionary are all 1, the abnormal result is classified as a Category II abnormality; when the output values ​​of the environmental abnormality identifier dictionary, the program logic abnormality identifier dictionary, and the other abnormality identifier dictionary are all 0, the abnormal result is classified as a Category I abnormality; when the output values ​​of the environmental abnormality identifier dictionary, the program logic abnormality identifier dictionary, and the other abnormality identifier dictionary are all 0, the abnormal result is classified as a Category I abnormality.

[0064] Preferably, to further improve retrieval speed, independent execution threads can be configured for each trie, for example, three threads can be started to match different tries simultaneously. Secondly, the matching results of different threads need to be processed asynchronously.

[0065] S4. When the abnormal result of the Mock interface is classified as a single type of abnormality, the corresponding abnormal information is output and a matching alarm is generated. Outputting the specified abnormal information and issuing an alarm at this time indicates that a valid defect in the system under test has been discovered, requiring the sending of an alarm message and notification to relevant personnel for further investigation.

[0066] S5. When the exception result of the Mock interface is classified as a Category II exception, isolate the external dependencies corresponding to the Mock interface and re-execute step S3. Category II exceptions represent exceptions in the called third-party service or network timeouts. It is necessary to first notify the Mock service to modify the validity of the specified third-party mock interface, isolate the external dependencies, and then rerun the test script.

[0067] S6. When the exception result of the Mock interface is classified as an unknown exception, stop the test execution and output the unknown exception information. An unknown exception indicates that a problem has been found that is not in the system under test, such as a problem with the test script itself, and the execution of the automated test script needs to be stopped.

[0068] Preferably, the model used can be encapsulated using a suitable interface to achieve the function of obtaining real-time logs of automated scripts by specifying conditions, and to perform preliminary cleaning of abnormal logs generated by automated test scripts.

[0069] Another aspect of the present invention relates to an automated testing system for interface isolation control, the structure of which is as follows: Figure 2 As shown, it includes:

[0070] The status label configuration module is used to configure and modify the status labels used for the Mock interface;

[0071] The test set generation module is used to scan the Mock interface status labels of the system under test and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface.

[0072] The anomaly classification and judgment module is used to analyze the test interface set using a preset decision model and determine the anomaly result classification of the Mock interface;

[0073] The execution feedback module is used to perform corresponding operations and provide matching information based on the abnormal results of the Mock interface.

[0074] By using this system, the aforementioned computational processing methods can be executed and the corresponding technical effects can be achieved.

[0075] Embodiments of the present invention also provide a computer-readable storage medium capable of implementing all the steps of the methods in the above embodiments, wherein the computer-readable storage medium stores a computer program that, when executed by a processor, implements all the steps of the methods in the above embodiments.

[0076] Embodiments of the present invention also provide an electronic device for performing the above-described method. As an implementation device for the method, the electronic device has at least a processor and a memory. In particular, the memory stores data and related computer programs required for performing the method, such as status tags and preset decision models. The processor calls the data and programs in the memory to execute all the steps of the method and obtain the corresponding technical effect.

[0077] Preferably, the electronic device may include a bus architecture, which may include any number of interconnected buses and bridges. The bus will include various circuits linked together by one or more processors and memories. The bus may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter may be the same element, i.e., a transceiver, providing a unit for communicating with various other systems over a transmission medium. The processor is responsible for managing the bus and general processing, while the memory may be used to store data used by the processor during operation.

[0078] Additionally, the electronic device may further include components such as a communication module, an input unit, an audio processor, a display, and a power supply. The processor (or controller, operating control) used may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device; the memory may be one or more of a buffer, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices, which can store the aforementioned data information, and may also store programs for executing the information, and the processor can execute the program stored in the memory to achieve information storage or processing, etc.; the input unit is used to provide input to the processor, for example, it can be a button or touch input device; the power supply is used to provide power to the electronic device; the display is used to display images and text, for example, it can be an LCD display. The communication module is a transmitter / receiver that transmits and receives signals via an antenna. The communication module (transmitter / receiver) is coupled to the processor to provide input signals and receive output signals, which can be the same as in conventional mobile communication terminals. Based on different communication technologies, multiple communication modules can be incorporated into the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) is also coupled to a speaker and microphone via an audio processor to provide audio output through the speaker and receive audio input from the microphone, thereby enabling typical telecommunications functions. The audio processor can include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor is coupled to a central processing unit, enabling on-device recording via the microphone and on-device playback of stored sound via the speaker.

[0079] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0080] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0083] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An automated testing method for interface isolation control, characterized in that, include: S1. Configure status labels for the Mock interface, the status labels including normal response interface and abnormal response interface; S2. Scan the status labels of the Mock interfaces of the system under test, and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface. S3. Use a preset decision model to analyze the test interface set and determine the abnormal result classification of the Mock interface. The abnormal result classification includes Class I abnormality, Class II abnormality and unknown abnormality. Class I abnormality corresponds to the effective defect of the tested system, Class II abnormality corresponds to the third-party service defect of the non-tested system, and unknown abnormality corresponds to other defects other than Class I abnormality and Class II abnormality. S4. When the abnormal result of the Mock interface is classified as a type of abnormality, output the corresponding abnormal information and generate a matching alarm. S5. When the exception result of the Mock interface is classified as a type II exception, isolate the external dependency corresponding to the Mock interface and re-execute step S3. S6. When the exception result of the Mock interface is classified as an unknown exception, stop the test execution and output the unknown exception information.

2. The method as described in claim 1, characterized in that, The preset decision model includes an encapsulated AC automaton model.

3. The method as described in claim 2, characterized in that, The encapsulated AC automaton model is created by the following method: A1. Configure exception identifiers, which include environment exception identifiers, program logic exception identifiers, and other exception identifiers; wherein, the environment exception identifier contains exception information keywords caused by service deployment and network problems; the program logic exception identifier stores exceptions that occur during Java runtime; and the other exception identifiers store error log information that occurs when the tested system calls external systems. A2. Configure corresponding identifier data based on the anomaly identifier to form an anomaly detection model; A3. Construct corresponding trie trees based on the different anomaly identifiers of the anomaly detection model; A4. Construct mismatch pointers for each node in the trie; A5. Configure the decision set for the output results of each trie.

4. The method as described in claim 3, characterized in that, The decision set for configuring the output results of each trie includes: Set the values ​​to be taken from the output of the trie, where the range of the output values ​​is 1 or 0; Configure the combination decision of the trie output results among the various exception identifiers.

5. The method as described in claim 4, characterized in that, The decision-making process for combining the trie outputs among the various anomaly identifiers includes: When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly. When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class I anomaly. When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly. When the output of the environmental anomaly identifier dictionary is 1, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class II anomaly. When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as other anomalies. When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 0, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class II anomaly. When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 0, the anomaly result is classified as a Class I anomaly. When the output of the environmental anomaly identifier dictionary is 0, the output of the program logic anomaly identifier dictionary is 1, and the output of the other anomaly identifier dictionary is 1, the anomaly result is classified as a Class I anomaly.

6. The method as described in claim 3, characterized in that, The encapsulated AC automaton model also includes: Configure independent execution threads for each trie.

7. An automated testing system for interface isolation control, characterized in that, The method, when executed, is used to implement any one of claims 1 to 6, comprising: The status label configuration module is used to configure and modify the status labels used for the Mock interface; The test set generation module is used to scan the Mock interface status labels of the system under test and form a test interface set by combining all Mock interfaces with the status label of abnormal response interface. The anomaly classification and judgment module is used to analyze the test interface set using a preset decision model and determine the anomaly result classification of the Mock interface; The execution feedback module is used to perform corresponding operations and provide matching information based on the abnormal results of the Mock interface.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method of any one of claims 1 to 6.

9. An electronic device, characterized in that, Including processor and memory; The memory is used to store state tags and preset decision models; The processor is configured to execute the method of any one of claims 1 to 6 by invoking a status label and a preset decision model.

10. A computer program product comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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