Test number generation method and device for multi-calculation factor combination scene

Abstract

The invention relates to a test number generation method and device for a multi-calculation-factor combination scene. The method comprises the following steps: acquiring scene information and scene condition information of each calculation factor combination scene of an insurance system, and identifying scene association information among the calculation factor combination scenes; based on the scene condition information and the scene association information of each calculation factor combination scene, generating a combination test case generation strategy corresponding to each calculation factor combination scene, thereby generating each initial test case corresponding to each calculation factor combination scene through an initial test case generation program; and based on each initial test case corresponding to each calculation factor combination scene and the prediction result thereof, performing optimization processing on the initial test case generation program to obtain a target test case generation program so as to generate a target test case of each calculation factor combination scene. By adopting the method, the calculation accuracy of the insurance system in the calculation factor combination can be improved.

Description

Technical Field

[0001] The present application relates to the field of automated testing technology, and in particular to a test number generation method and device for a multi-calculation factor combination scenario. Background Art

[0002] During the testing of the insurance system, there will be scenarios where the calculation results are different when there are many combinations of calculation factors. If this scenario is a key calculation node, frequent regression testing is required to ensure the accuracy of data calculation. As a result, when encountering a combination of calculation factors, the accuracy of the calculation results is low. Therefore, how to improve the calculation accuracy of the insurance system in the combination of calculation factors is the current research focus.

[0003] The traditional technical solution is to conduct data training tests on scenarios with multiple calculation factor combinations through manual testing and number generation, thereby improving the calculation accuracy of multiple combination scenarios. However, manual testing is time-consuming and repetitive, and prone to errors. It also lacks flexibility and reusability. If the data is contaminated or adjusted, it takes the same amount of time to regenerate the test data. For frequent regression testing, the cost is too high and the efficiency is too low, resulting in poor calculation accuracy of the insurance system in the calculation factor combination. Summary of the invention

[0004] Based on this, it is necessary to provide a test number generation method, device, computer equipment, computer-readable storage medium and computer program product for a multi-calculation factor combination scenario in response to the above technical problems.

[0005] In a first aspect, the present application provides a test number generation method for a multi-calculation factor combination scenario, comprising:

[0006] Acquire scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario, and identify scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario;

[0007] Based on the scenario condition information of each computing factor combination scenario and the scenario association information between the computing factor combination scenarios, a combination test case generation strategy corresponding to each computing factor combination scenario is generated, and based on the combination test case generation strategy corresponding to each computing factor combination scenario, each initial test case corresponding to each computing factor combination scenario is generated through an initial test case generation program;

[0008] Based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case, the initial test case generation program is optimized to obtain a target test case generation program, and based on each of the combination test case generation strategies, the target test cases for each of the calculation factor combination scenarios are generated through the target test case generation program.

[0009] Optionally, the identifying the scene association information between the computing factor combination scenes based on the scene information of each computing factor combination scene includes:

[0010] For each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, identifying each scenario factor type of the computing factor combination scenario and factor association information between each scenario factor type;

[0011] Based on the scenario factor types of each computing factor combination scenario, the associated computing factor combination scenarios of the same scenario factor type are identified, and based on the factor association information between each scenario factor type of each associated computing factor combination scenario, the scenario association information between each associated computing factor combination scenario is identified.

[0012] Optionally, the identifying the scenario association information between the associated calculation factor combination scenarios based on the factor association information between each scenario factor type of the associated calculation factor combination scenarios includes:

[0013] For each associated calculation factor combination scenario, based on the factor association information between each scenario factor type of the associated calculation factor combination scenario, identifying the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type;

[0014] Based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, the use case generation logic of the associated calculation factor combination scenario is identified, and based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, the associated logic content between each associated calculation factor combination scenario is identified through a logical association identification strategy;

[0015] The associated logical contents between the associated calculation factor combination scenes are used as the scene association information between the associated calculation factor combination scenes.

[0016] Optionally, the generating a combination test case generation strategy corresponding to each computing factor combination scenario based on the scenario condition information of each computing factor combination scenario and the scenario association information between the computing factor combination scenarios includes:

[0017] Based on the scenario condition information of each computing factor combination scenario, identifying the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identifying the scenario use case scope of each scenario factor type;

[0018] Based on the scenario use case scope of each scenario factor type of each associated calculation factor combination scenario and the associated logical content between the associated calculation factor combination scenarios, a combination test case generation strategy corresponding to each associated calculation factor combination scenario of the same scenario factor type is generated.

[0019] Optionally, the strategy for generating a combined test case corresponding to each of the calculation factor combination scenarios is based on generating an initial test case generation program to generate each initial test case corresponding to each of the calculation factor combination scenarios, including:

[0020] Input the combination test case generation strategy between the associated calculation factor combination scenarios of the same scenario factor type into the initial test case generation program to generate each associated test information for each associated calculation factor combination scenario;

[0021] Based on the scenario use case range of each scenario factor type of each associated calculation factor combination scenario, each target associated test information is screened in each associated test information as each initial test case corresponding to each calculation factor combination scenario.

[0022] Optionally, the initial test case generation program is optimized based on each initial test case corresponding to each calculation factor combination scenario and the prediction result of each initial test case to obtain a target test case generation program, including:

[0023] Based on each initial test case corresponding to each calculation factor combination scenario, generate the test results corresponding to each initial test case of each calculation factor combination scenario through the automated test interface program, and obtain the prediction results corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation;

[0024] For each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario, the estimation accuracy of each initial test case is evaluated by a result evaluation algorithm;

[0025] Screening each abnormal initial test case whose accuracy is lower than the estimated accuracy threshold, and training the initial test case generation program based on each abnormal initial test case and the prediction result of each abnormal initial test case to obtain a new test case generation program;

[0026] The new test case generation program replaces the initial test case generation program, and returns to execute the combined test case generation strategy corresponding to each of the calculation factor combination scenarios, and generates each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and the initial test case generation program obtained in the last iteration is used as the target test case generation program.

[0027] In a second aspect, the present application also provides a test number generation device for a multi-calculation factor combination scenario, comprising:

[0028] An acquisition module, used to acquire scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario, and identify scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario;

[0029] A generation module, used to generate a combination test case generation strategy corresponding to each of the calculation factor combination scenarios based on the scenario condition information of each of the calculation factor combination scenarios and the scenario association information between the calculation factor combination scenarios, and generate each initial test case corresponding to each of the calculation factor combination scenarios through an initial test case generation program based on the combination test case generation strategy corresponding to each of the calculation factor combination scenarios;

[0030] The optimization module is used to optimize the initial test case generation program based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case to obtain a target test case generation program, and based on each of the combination test case generation strategies, generate the target test case for each of the calculation factor combination scenarios through the target test case generation program.

[0031] Optionally, the acquisition module is specifically used to:

[0032] For each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, identifying each scenario factor type of the computing factor combination scenario and factor association information between each scenario factor type;

[0033] Based on the scenario factor types of each computing factor combination scenario, the associated computing factor combination scenarios of the same scenario factor type are identified, and based on the factor association information between each scenario factor type of each associated computing factor combination scenario, the scenario association information between each associated computing factor combination scenario is identified.

[0034] Optionally, the acquisition module is specifically used to:

[0035] For each associated calculation factor combination scenario, based on the factor association information between each scenario factor type of the associated calculation factor combination scenario, identifying the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type;

[0036] Based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, the use case generation logic of the associated calculation factor combination scenario is identified, and based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, the associated logic content between each associated calculation factor combination scenario is identified through a logical association identification strategy;

[0037] The associated logical contents between the associated calculation factor combination scenes are used as the scene association information between the associated calculation factor combination scenes.

[0038] Optionally, the generating module is specifically used for:

[0039] Based on the scenario condition information of each computing factor combination scenario, identifying the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identifying the scenario use case scope of each scenario factor type;

[0040] Based on the scenario use case scope of each scenario factor type of each associated calculation factor combination scenario and the associated logical content between the associated calculation factor combination scenarios, a combination test case generation strategy corresponding to each associated calculation factor combination scenario of the same scenario factor type is generated.

[0041] Optionally, the generating module is specifically used for:

[0042] Input the combination test case generation strategy between the associated calculation factor combination scenarios of the same scenario factor type into the initial test case generation program to generate each associated test information for each associated calculation factor combination scenario;

[0043] Based on the scenario use case range of each scenario factor type of each associated calculation factor combination scenario, each target associated test information is screened in each associated test information as each initial test case corresponding to each calculation factor combination scenario.

[0044] Optionally, the optimization module is specifically used to:

[0045] Based on each initial test case corresponding to each calculation factor combination scenario, generate the test results corresponding to each initial test case of each calculation factor combination scenario through the automated test interface program, and obtain the prediction results corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation;

[0046] For each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario, the estimation accuracy of each initial test case is evaluated by a result evaluation algorithm;

[0047] Screening each abnormal initial test case whose accuracy is lower than the estimated accuracy threshold, and training the initial test case generation program based on each abnormal initial test case and the prediction result of each abnormal initial test case to obtain a new test case generation program;

[0048] The new test case generation program replaces the initial test case generation program, and returns to execute the combined test case generation strategy corresponding to each of the calculation factor combination scenarios, and generates each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and the initial test case generation program obtained in the last iteration is used as the target test case generation program.

[0049] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0051] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0052] The test case generation method and device for the above-mentioned multi-calculation factor combination scenario obtains the scenario information of each calculation factor combination scenario of the insurance system and the scenario condition information of each calculation factor combination scenario, and identifies the scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario; generates a combination test case generation strategy corresponding to each calculation factor combination scenario based on the scenario condition information of each calculation factor combination scenario and the scenario association information between each calculation factor combination scenario, and generates each initial test case corresponding to each calculation factor combination scenario through an initial test case generation program based on the combination test case generation strategy corresponding to each calculation factor combination scenario; optimizes the initial test case generation program based on each initial test case corresponding to each calculation factor combination scenario and the prediction result of each initial test case to obtain a target test case generation program, and generates a target test case for each calculation factor combination scenario through the target test case generation program based on each combination test case generation strategy. This solution conducts a comprehensive analysis on different computing factor combination scenarios to identify the scenario association information between the computing factor combination scenarios, and then generates a combination test case generation strategy corresponding to each computing factor combination scenario based on the scenario association information between the computing factor combination scenarios, so that there is no need to generate a large number of test cases for each computing factor combination scenario. Instead, only a small number of test cases are needed to meet the test process of all computing factor combination scenarios, thereby improving the test case generation optimization effect. Moreover, this solution does not require manual test case generation, manual detection, and manual scenario analysis. It can not only effectively improve the testing efficiency, but also intelligently process the entire process of generating, optimizing, and detecting each test case, avoiding the problem of high manual error rate. Moreover, this method can effectively optimize the accuracy, automatically generate, and automatically detect test cases, thereby improving the accuracy of test cases, thereby improving the calculation accuracy of the insurance system in the calculation factor combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a flowchart of a method for generating test numbers for a scenario with multiple calculation factors combined in one embodiment;

[0055] Figure 2An example diagram of generating an automated test case in one embodiment;

[0056] Figure 3 A schematic diagram of a flow chart of an example of test number generation for a scenario with multiple calculation factors combined in one embodiment;

[0057] Figure 4 It is a structural block diagram of a test number generation device for a multi-calculation factor combination scenario in one embodiment;

[0058] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The test number generation method for a multi-calculation factor combination scenario provided in an embodiment of the present application can be applied to an application environment for test number generation for a multi-calculation factor combination scenario. The method can be applied to a terminal, a server, or a system including a terminal and a server, and implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-size computers, etc. Among them, the terminal conducts a comprehensive analysis of different computing factor combination scenarios to identify the scenario association information between each computing factor combination scenario, and then generates a combination test case generation strategy corresponding to each computing factor combination scenario based on the scenario association information between each computing factor combination scenario, so that there is no need to generate a large number of test cases for each computing factor combination scenario separately, but only a small number of test cases are needed to meet the test process of all computing factor combination scenarios, thereby improving the generation and optimization effect of test cases. Moreover, this solution does not require manual test case generation, manual detection, and manual scenario analysis. It can not only effectively improve the testing efficiency, but also this method can intelligently process the entire process of generating, optimizing, and detecting each test case, avoiding the problem of high manual error rate, and this method can also effectively optimize the accuracy of test cases, automatically generate, and automatically detect test cases, thereby improving the accuracy of test cases, thereby improving the calculation accuracy of insurance system in computing factor combinations.

[0061] In an exemplary embodiment, Figure 1 As shown, a test number generation method for a multi-calculation factor combination scenario is provided, and the method is applied to a terminal as an example for explanation, including the following steps S101 to S103. Among them:

[0062] Step S101, obtaining scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario, and identifying scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario.

[0063] In this embodiment, the terminal obtains the scenario information of each calculation factor combination scenario and the scenario condition information of each calculation factor combination scenario in response to the staff's information upload operation. The scenario information is the scenario information including the types of each scenario factor and the factor association information between the types of scenario factors. In the calculation factor combination scenario where the insurance policies of the sales personnel are transferred to each other, the scenario factor type includes but is not limited to the sales personnel's attribute type, insurance policy type and other scenario factor types. Then, based on the scenario information of each calculation factor combination scenario, the terminal identifies the scenario association information between the calculation factor combination scenarios. The scenario association information is the association logic content of each scenario factor type that can be commonly applied between two calculation factor combination scenarios. The specific association logic content identification process will be described in detail later.

[0064] Step S102, based on the scenario condition information of each computing factor combination scenario and the scenario association information between the computing factor combination scenarios, generate a combination test case generation strategy corresponding to each computing factor combination scenario, and based on the combination test case generation strategy corresponding to each computing factor combination scenario, generate each initial test case corresponding to each computing factor combination scenario through an initial test case generation program.

[0065] In this embodiment, the terminal generates a combination test case generation strategy corresponding to each calculation factor combination scenario based on the scenario condition information of each calculation factor combination scenario and the scenario association information between each calculation factor combination scenario, and generates each initial test case corresponding to each calculation factor combination scenario through an initial test case generation program based on the combination test case generation strategy corresponding to each calculation factor combination scenario. Among them, the initial test case generation program, the interface automation tool designed by this solution, can also be a use case generation module, which can simulate the operation of allocating insurance policies from the original salesperson to the assigned salesperson. The customer base of the two salespeople will produce corresponding data changes according to different policy types and allocation scenarios. The specific generation process will be described in detail later.

[0066] Step S103, based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case, the initial test case generation program is optimized to obtain a target test case generation program, and based on each combination test case generation strategy, the target test case for each calculation factor combination scenario is generated through the target test case generation program.

[0067] In this embodiment, the terminal calculates the prediction accuracy of each initial test case based on the test results corresponding to each initial test case corresponding to each calculation factor combination scenario and the prediction result of each initial test case, thereby optimizing the initial test case generation program to obtain a target test case generation program, and based on each combination test case generation strategy, generates a target test case for each calculation factor combination scenario through the target test case generation program. The specific iterative optimization process will be described in detail later.

[0068] Based on the above scheme, by comprehensively analyzing different computing factor combination scenarios, the scenario association information between each computing factor combination scenario is identified, and then based on the scenario association information between each computing factor combination scenario, a combination test case generation strategy corresponding to each computing factor combination scenario is generated, so that there is no need to generate a large number of test cases for each computing factor combination scenario separately. Instead, only a small number of test cases are needed to meet the test process of all computing factor combination scenarios, thereby improving the optimization effect of test case generation. Moreover, this scheme does not require manual test case generation, manual detection, and manual scenario analysis. It can not only effectively improve the testing efficiency, but also this method can intelligently process the entire process of generating, optimizing, and detecting each test case, avoiding the problem of high manual error rate. Moreover, this method can also effectively optimize the accuracy of test cases, automatically generate, and automatically detect test cases, thereby improving the accuracy of test cases, thereby improving the calculation accuracy of the insurance system in the calculation factor combination.

[0069] Optionally, based on the scenario information of each computing factor combination scenario, scene association information between each computing factor combination scenario is identified, including: for each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, each scenario factor type of the computing factor combination scenario and factor association information between each scenario factor type are identified; based on each scenario factor type of each computing factor combination scenario, each associated computing factor combination scenario of the same scenario factor type is identified, and based on the factor association information between each scenario factor type of each associated computing factor combination scenario, scene association information between each associated computing factor combination scenario is identified.

[0070] In this embodiment, the terminal identifies the scenario factor types of the calculation factor combination scenario and the factor association information between each scenario factor type for each calculation factor combination scenario based on the scenario information of the calculation factor combination scenario. The factor association information between each scenario factor type is the change correspondence between each scenario factor type. For example, when insurance officer A transfers the insurance policy corresponding to customer B to insurance officer C, the factor association information between the insurance officer attribute type and the customer's insurance policy type is the insurance policy transfer relationship.

[0071] Then, the terminal identifies each associated computing factor combination scenario of the same scenario factor type based on each scenario factor type of each computing factor combination scenario, and identifies the scenario association information between each associated computing factor combination scenario based on the factor association information between each scenario factor type of each associated computing factor combination scenario. The specific process of identifying the scenario association information will be described in detail later. Among them, the same scenario factor type means that the scenario factor types contained in the two computing factor combination scenarios are the same.

[0072] Based on the above scheme, after splitting the aviation warning factor type, identifying the associated calculation factor combination scenarios of the same scenario factor type, and then identifying the scene association information between the associated calculation factor combination scenarios, the screening accuracy of the associated calculation factor combination scenarios is improved.

[0073] Optionally, based on the factor association information between each scenario factor type of each associated computing factor combination scenario, the scenario association information between each associated computing factor combination scenario is identified, including: for each associated computing factor combination scenario, based on the factor association information between each scenario factor type of the associated computing factor combination scenario, the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type are identified; based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, the use case generation logic of the associated computing factor combination scenario is identified, and based on the use case generation logic of each associated computing factor combination scenario of the same scenario factor type, the associated logic content between each associated computing factor combination scenario is identified through a logical association identification strategy; and the associated logic content between each associated computing factor combination scenario is used as the scenario association information between each associated computing factor combination scenario.

[0074] In this embodiment, for each associated calculation factor combination scenario, the terminal identifies the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type based on the factor association information between each scenario factor type of the associated calculation factor combination scenario. The logical correspondence is the scenario change logic between two scenario factor types, for example, no more policies are allocated to sales personnel with saturated policies, and the attributes of policies change with the change of sales personnel.

[0075] Then, the terminal identifies the use case generation logic of the associated calculation factor combination scenario based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type. The use case generation strategy is to use the logical correspondence as the use case structure of the generated test case and the parameter association range as the use case data range of the generated test case.

[0076] Then, based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, the terminal identifies the associated logic content between each associated calculation factor combination scenario through the logical association identification strategy. The associated logic content between each associated calculation factor combination scenario is used as the scenario association information between each associated calculation factor combination scenario. The associated logic content is the use case generation logic for the same use case structure, overlapping use case structures, and overlapping use case data ranges.

[0077] Based on the above solution, the use case generation logic of each associated calculation factor combination scenario is first identified, and then the associated logic content between two associated calculation factor combination scenarios of the same scenario factor type is queried, thereby improving the accuracy of identification.

[0078] Optionally, based on the scenario condition information of each computing factor combination scenario and the scenario association information between the computing factor combination scenarios, a combination test case generation strategy corresponding to each computing factor combination scenario is generated, including: based on the scenario condition information of each computing factor combination scenario, identifying the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identifying the scenario use case scope of each scenario factor type; based on the scenario use case scope of each scenario factor type of each associated computing factor combination scenario and the associated logical content between the associated computing factor combination scenarios, generating a combination test case generation strategy corresponding to each associated computing factor combination scenario of the same scenario factor type.

[0079] In this embodiment, the terminal identifies the scenario use case requirement information of each scenario factor type of each computing factor combination scenario based on the scenario condition information of each computing factor combination scenario, and identifies the scenario use case range of each scenario factor type based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario. The scenario use case range is the use case range for each scenario use case requirement type, for example, the range of various personnel attributes of sales personnel, the range of policy types of insurance policies, and the range of customer identity information.

[0080] Finally, the terminal generates a combination test case generation strategy corresponding to each associated calculation factor combination scenario of the same scenario factor type based on the scenario use case range of each scenario factor type of each associated calculation factor combination scenario and the associated logic content between each associated calculation factor combination scenario. Among them, the combination test case generation strategy is a test strategy that generates a single test case that can test each associated calculation factor combination scenario. Figure 2, which is based on 46 different combination scenarios, involving more than 100 different types of insurance policies, and all calculation factor combination scenarios with 6 types of data that need to be verified after the test is completed. The 29 automated test cases generated by the use case generation through this technical solution. That is, through this solution, thousands of test cases involving more than 100 insurance policies in 46 scenarios can be optimized and screened to obtain test cases, optimize the number of test cases, and improve the test effect of test cases.

[0081] Based on the above scheme, by identifying the associated logical content of the test cases, a combined test case generation strategy is obtained, which improves the generation efficiency of the combined test case generation strategy and the case accuracy of the combined test cases.

[0082] Optionally, based on the combination test case generation strategy corresponding to each computing factor combination scenario, initial test cases corresponding to each computing factor combination scenario are generated through an initial test case generation program, including: inputting the combination test case generation strategy between each associated computing factor combination scenario of the same scenario factor type into the initial test case generation program to generate each associated test information for each associated computing factor combination scenario; based on the scenario case range of each scenario factor type of each associated computing factor combination scenario, in each associated test information, screening each target associated test information as each initial test case corresponding to each computing factor combination scenario.

[0083] In this embodiment, the terminal inputs the combination test case generation strategy between each associated calculation factor combination scenario of the same scenario factor type into the initial test case generation program to generate each associated test information of each associated calculation factor combination scenario. Among them, the associated test information is a directly generated test case, and the test case contains the test case corresponding to each associated calculation factor combination scenario. In the test case, the test case of some associated calculation factor combination scenarios can be respectively applied to different associated calculation factor combination scenarios. Therefore, when identifying each initial test case corresponding to each calculation factor combination scenario, it is necessary to select and adapt the initial test case corresponding to each associated calculation factor combination scenario in each associated test information according to the scenario case range of each scenario factor type. Specifically, the terminal selects each target associated test information in each associated test information based on the scenario case range of each scenario factor type of each associated calculation factor combination scenario as each initial test case corresponding to each calculation factor combination scenario.

[0084] Based on the above scheme, a program is automatically generated based on the initial test case generation program. After the test case is generated, the initial test cases corresponding to each calculation factor combination scenario are screened, thereby improving the recognition accuracy of the initial test cases corresponding to each calculation factor combination scenario.

[0085] Optionally, based on each initial test case corresponding to each calculation factor combination scenario and the predicted result of each initial test case, the initial test case generation program is optimized to obtain a target test case generation program, including: based on each initial test case corresponding to each calculation factor combination scenario, generating test results corresponding to each initial test case of each calculation factor combination scenario through an automated test interface program, and obtaining the predicted result corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation; for each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the predicted result corresponding to each initial test case of the calculation factor combination scenario, An evaluation algorithm is provided to evaluate the estimated accuracy of each initial test case; each abnormal initial test case below the estimated accuracy threshold is screened, and based on each abnormal initial test case and the prediction result of each abnormal initial test case, the initial test case generation program is trained to obtain a new test case generation program; the new test case generation program is used to replace the initial test case generation program, and the combination test case generation strategy corresponding to each calculation factor combination scenario is returned to execute, and each initial test case step corresponding to each calculation factor combination scenario is generated through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and the initial test case generation program obtained in the last iteration is used as the target test case generation program.

[0086] In this embodiment, the terminal generates test results corresponding to each initial test case of each calculation factor combination scenario based on each initial test case corresponding to each calculation factor combination scenario through an automated testing interface program, and obtains the prediction results corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation.

[0087] For each calculation factor combination scenario, the terminal evaluates the estimated accuracy of each initial test case based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario through the result evaluation algorithm. Specifically, the terminal calculates the deviation value between the test result and the prediction result, and then the terminal queries the accuracy corresponding to the deviation value in the deviation database to obtain the estimated accuracy of the initial test case.

[0088] Afterwards, the terminal screens each abnormal initial test case that is lower than the estimated accuracy threshold, and trains the initial test case generation program based on each abnormal initial test case and the prediction results of each abnormal initial test case to obtain a new test case generation program. Finally, the terminal replaces the initial test case generation program with the new test case generation program, and returns to execute the combined test case generation strategy corresponding to each calculation factor combination scenario, and generates each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases that are lower than the estimated accuracy threshold, and the initial test case generation program obtained by the last iteration is used as the target test case generation program.

[0089] Based on the above scheme, the initial test case generation program is optimized by predicting the results and the prediction accuracy deviation between the actual results, thereby improving the generation accuracy of the initial test case generation program.

[0090] This application also provides an example of test number generation for a multi-calculation factor combination scenario, such as Figure 3 As shown, the specific processing process includes the following steps:

[0091] Step S301, obtaining scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario.

[0092] Step S302 : for each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, identifying the scenario factor types of the computing factor combination scenario and the factor association information between each scenario factor type.

[0093] Step S303: Based on the scenario factor types of each computing factor combination scenario, identifying associated computing factor combination scenarios of the same scenario factor type.

[0094] Step S304: for each associated calculation factor combination scenario, based on the factor association information between each scenario factor type of the associated calculation factor combination scenario, identify the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type.

[0095] Step S305, based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, identify the use case generation logic of the associated calculation factor combination scenario, and based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, identify the association logic content between each associated calculation factor combination scenario through the logical association identification strategy.

[0096] Step S306: taking the associated logical contents between the associated calculation factor combination scenarios as the scenario association information between the associated calculation factor combination scenarios.

[0097] Step S307, based on the scenario condition information of each computing factor combination scenario, identify the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identify the scenario use case scope of each scenario factor type.

[0098] Step S308, based on the scenario use case scope of each scenario factor type of each associated computing factor combination scenario and the associated logical content between each associated computing factor combination scenario, generate a combination test case generation strategy corresponding to each associated computing factor combination scenario of the same scenario factor type.

[0099] Step S309, input the combined test case generation strategy between the associated calculation factor combination scenarios of the same scenario factor type into the initial test case generation program to generate the associated test information of each associated calculation factor combination scenario.

[0100] Step S310 , based on the scenario use case range of each scenario factor type of each associated computing factor combination scenario, in each associated test information, each target associated test information is screened as each initial test case corresponding to each computing factor combination scenario.

[0101] Step S311, based on the initial test cases corresponding to each calculation factor combination scenario, generate the test results corresponding to the initial test cases of each calculation factor combination scenario through the automated test interface program, and obtain the prediction results corresponding to the initial test cases of the calculation factor combination scenario in response to the information upload operation of the staff.

[0102] Step S312, for each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario, the estimation accuracy of each initial test case is evaluated through a result evaluation algorithm.

[0103] Step S313, screening each abnormal initial test case below the estimated accuracy threshold, and based on each abnormal initial test case and the prediction result of each abnormal initial test case, training the initial test case generation program to obtain a new test case generation program.

[0104] Step S314, replace the initial test case generation program with the new test case generation program, and return to execute the combined test case generation strategy corresponding to each calculation factor combination scenario, generate each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and use the initial test case generation program obtained in the last iteration as the target test case generation program.

[0105] Step S315 , based on each combination test case generation strategy, a target test case for each calculation factor combination scenario is generated through a target test case generation program.

[0106] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0107] Based on the same inventive concept, the embodiment of the present application also provides a test number generation device for a multi-calculation factor combination scenario for implementing the test number generation method for the multi-calculation factor combination scenario involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the test number generation device for one or more multi-calculation factor combination scenarios provided below can refer to the limitations of the test number generation method for the multi-calculation factor combination scenario above, and will not be repeated here.

[0108] In an exemplary embodiment, Figure 4 As shown, a test number generation device for a multi-calculation factor combination scenario is provided, including: an acquisition module 410, a generation module 420 and an optimization module 430, wherein:

[0109] The acquisition module 410 is used to acquire the scenario information of each calculation factor combination scenario of the insurance system and the scenario condition information of each calculation factor combination scenario, and identify the scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario;

[0110] A generation module 420 is used to generate a combination test case generation strategy corresponding to each of the calculation factor combination scenarios based on the scenario condition information of each of the calculation factor combination scenarios and the scenario association information between the calculation factor combination scenarios, and generate each initial test case corresponding to each of the calculation factor combination scenarios through an initial test case generation program based on the combination test case generation strategy corresponding to each of the calculation factor combination scenarios;

[0111] The optimization module 430 is used to optimize the initial test case generation program based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case to obtain a target test case generation program, and based on each of the combination test case generation strategies, generate the target test case for each of the calculation factor combination scenarios through the target test case generation program.

[0112] Optionally, the acquisition module 410 is specifically configured to:

[0113] For each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, identifying each scenario factor type of the computing factor combination scenario and factor association information between each scenario factor type;

[0114] Based on the scenario factor types of each computing factor combination scenario, the associated computing factor combination scenarios of the same scenario factor type are identified, and based on the factor association information between each scenario factor type of each associated computing factor combination scenario, the scenario association information between each associated computing factor combination scenario is identified.

[0115] Optionally, the acquisition module 410 is specifically configured to:

[0116] For each associated calculation factor combination scenario, based on the factor association information between each scenario factor type of the associated calculation factor combination scenario, identifying the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type;

[0117] Based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, the use case generation logic of the associated calculation factor combination scenario is identified, and based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, the associated logic content between each associated calculation factor combination scenario is identified through a logical association identification strategy;

[0118] The associated logical contents between the associated calculation factor combination scenes are used as the scene association information between the associated calculation factor combination scenes.

[0119] Optionally, the generating module 420 is specifically configured to:

[0120] Based on the scenario condition information of each computing factor combination scenario, identifying the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identifying the scenario use case scope of each scenario factor type;

[0121] Based on the scenario use case scope of each scenario factor type of each associated calculation factor combination scenario and the associated logical content between the associated calculation factor combination scenarios, a combination test case generation strategy corresponding to each associated calculation factor combination scenario of the same scenario factor type is generated.

[0122] Optionally, the generating module 420 is specifically configured to:

[0123] Input the combination test case generation strategy between the associated calculation factor combination scenarios of the same scenario factor type into the initial test case generation program to generate each associated test information for each associated calculation factor combination scenario;

[0124] Based on the scenario use case range of each scenario factor type of each associated calculation factor combination scenario, each target associated test information is screened in each associated test information as each initial test case corresponding to each calculation factor combination scenario.

[0125] Optionally, the optimization module 430 is specifically configured to:

[0126] Based on each initial test case corresponding to each calculation factor combination scenario, generate the test results corresponding to each initial test case of each calculation factor combination scenario through the automated test interface program, and obtain the prediction results corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation;

[0127] For each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario, the estimation accuracy of each initial test case is evaluated by a result evaluation algorithm;

[0128] Screening each abnormal initial test case whose accuracy is lower than the estimated accuracy threshold, and training the initial test case generation program based on each abnormal initial test case and the prediction result of each abnormal initial test case to obtain a new test case generation program;

[0129] The new test case generation program replaces the initial test case generation program, and returns to execute the combined test case generation strategy corresponding to each of the calculation factor combination scenarios, and generates each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and the initial test case generation program obtained in the last iteration is used as the target test case generation program.

[0130] Each module in the test number generation device for the above-mentioned multi-calculation factor combination scenario can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0131] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a test number generation method for a multi-calculation factor combination scenario is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0132] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of a test number generation method for a multi-calculation factor combination scenario are implemented.

[0134] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a test number generation method for a multi-calculation factor combination scenario are implemented.

[0135] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of a test number generation method for a multi-calculation factor combination scenario.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A test number generation method for a multi-calculation factor combination scenario, characterized in that: The method comprises: Acquire scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario, and identify scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario; Based on the scenario condition information of each computing factor combination scenario and the scenario association information between the computing factor combination scenarios, a combination test case generation strategy corresponding to each computing factor combination scenario is generated, and based on the combination test case generation strategy corresponding to each computing factor combination scenario, each initial test case corresponding to each computing factor combination scenario is generated through an initial test case generation program; Based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case, the initial test case generation program is optimized to obtain a target test case generation program, and based on each of the combination test case generation strategies, the target test cases for each of the calculation factor combination scenarios are generated through the target test case generation program.

2. The method according to claim 1, characterized in that The identifying the scene association information between the calculation factor combination scenes based on the scene information of each calculation factor combination scene includes: For each computing factor combination scenario, based on the scenario information of the computing factor combination scenario, identifying each scenario factor type of the computing factor combination scenario and factor association information between each scenario factor type; Based on the scenario factor types of each computing factor combination scenario, the associated computing factor combination scenarios of the same scenario factor type are identified, and based on the factor association information between each scenario factor type of each associated computing factor combination scenario, the scenario association information between each associated computing factor combination scenario is identified.

3. The method according to claim 2, characterized in that The identifying the scene association information between the associated calculation factor combination scenes based on the factor association information between each scene factor type of the associated calculation factor combination scenes includes: For each associated calculation factor combination scenario, based on the factor association information between each scenario factor type of the associated calculation factor combination scenario, identifying the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type; Based on the logical correspondence between each scenario factor type and the parameter association range between each scenario factor type, the use case generation logic of the associated calculation factor combination scenario is identified, and based on the use case generation logic of each associated calculation factor combination scenario of the same scenario factor type, the associated logic content between each associated calculation factor combination scenario is identified through a logical association identification strategy; The associated logical contents between the associated calculation factor combination scenes are used as the scene association information between the associated calculation factor combination scenes.

4. The method according to claim 3, characterized in that The generating of a combination test case generation strategy corresponding to each of the calculation factor combination scenarios based on the scenario condition information of each of the calculation factor combination scenarios and the scenario association information between the calculation factor combination scenarios includes: Based on the scenario condition information of each computing factor combination scenario, identifying the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, and based on the scenario use case requirement information of each scenario factor type of each computing factor combination scenario, identifying the scenario use case scope of each scenario factor type; Based on the scenario use case scope of each scenario factor type of each associated calculation factor combination scenario and the associated logical content between the associated calculation factor combination scenarios, a combination test case generation strategy corresponding to each associated calculation factor combination scenario of the same scenario factor type is generated.

5. The method according to claim 4, characterized in that The strategy for generating combined test cases corresponding to the combined scenarios of the calculation factors is based on generating initial test cases corresponding to each combined scenario of the calculation factors through an initial test case generation program, including: Input the combination test case generation strategy between the associated calculation factor combination scenarios of the same scenario factor type into the initial test case generation program to generate each associated test information for each associated calculation factor combination scenario; Based on the scenario use case range of each scenario factor type of each associated calculation factor combination scenario, each target associated test information is screened in each associated test information as each initial test case corresponding to each calculation factor combination scenario.

6. The method according to claim 1, characterized in that The initial test case generation program is optimized based on each initial test case corresponding to each calculation factor combination scenario and the prediction result of each initial test case to obtain a target test case generation program, including: Based on each initial test case corresponding to each calculation factor combination scenario, generate the test results corresponding to each initial test case of each calculation factor combination scenario through the automated test interface program, and obtain the prediction results corresponding to each initial test case of the calculation factor combination scenario in response to the staff's information upload operation; For each calculation factor combination scenario, based on the test results corresponding to each initial test case of the calculation factor combination scenario and the prediction results corresponding to each initial test case of the calculation factor combination scenario, the estimation accuracy of each initial test case is evaluated by a result evaluation algorithm; Screening each abnormal initial test case whose accuracy is lower than the estimated accuracy threshold, and training the initial test case generation program based on each abnormal initial test case and the prediction result of each abnormal initial test case to obtain a new test case generation program; The new test case generation program replaces the initial test case generation program, and returns to execute the combined test case generation strategy corresponding to each of the calculation factor combination scenarios, and generates each initial test case step corresponding to each calculation factor combination scenario through the initial test case generation program, until there are no abnormal initial test cases below the estimated accuracy threshold, and the initial test case generation program obtained in the last iteration is used as the target test case generation program.

7. A test number generation device for a multi-calculation factor combination scenario, characterized in that: The device comprises: An acquisition module, used to acquire scenario information of each calculation factor combination scenario of the insurance system and scenario condition information of each calculation factor combination scenario, and identify scenario association information between each calculation factor combination scenario based on the scenario information of each calculation factor combination scenario; A generation module, used to generate a combination test case generation strategy corresponding to each of the calculation factor combination scenarios based on the scenario condition information of each of the calculation factor combination scenarios and the scenario association information between the calculation factor combination scenarios, and generate each initial test case corresponding to each of the calculation factor combination scenarios through an initial test case generation program based on the combination test case generation strategy corresponding to each of the calculation factor combination scenarios; The optimization module is used to optimize the initial test case generation program based on the initial test cases corresponding to each calculation factor combination scenario and the prediction results of each initial test case to obtain a target test case generation program, and based on each of the combination test case generation strategies, generate the target test case for each of the calculation factor combination scenarios through the target test case generation program.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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