Test question sheet processing method and device, electronic device, and storage medium

Abstract

The application discloses a test problem single processing method and device, electronic equipment and storage medium, which are applied to the financial field or other fields. The method comprises the following steps: when a new problem is detected, determining the demand to which the new problem belongs from each demand pre-divided based on the description information of the new problem; distributing the new problem to the developer corresponding to the demand to which the new problem belongs based on the pre-recorded personnel association information; if the demand to which the new problem belongs does not belong to the demand of the current version, setting the type of the new problem as demand change; if the demand to which the new problem belongs belongs to the demand of the current version, monitoring the change information corresponding to the new problem in real time; inputting the description information of the new problem and the change information corresponding to the new problem into a pre-trained classification model, and calculating the prediction type of the new problem through the classification model; and setting the prediction type of the new problem as the type of the new problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the functional test technical field, in particular to a test problem single processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] Before the system is put into operation, the system usually needs to be functionally tested to find out problems and solve them in time.

[0003] In the current functional test process, the tester will upload a new problem single after finding out a problem. Then the relevant staff will assign the new problem single to the developer for processing. The developer will select the problem type and other information of the new problem single according to the corresponding information and experience, so as to analyze the new problem single according to these information in the future.

[0004] Obviously, the manual operation and circulation mode is relatively slow and prone to errors, such as assigning a new problem single generated by a requirement of a developer to another developer. Moreover, the developer cannot effectively guarantee the accuracy of the problem type of the new problem single by experience. SUMMARY

[0005] Based on the above deficiencies of the prior art, the present application provides a test problem single processing method and device, electronic equipment and storage medium to solve the problem of low efficiency and easy error in the prior art.

[0006] In order to achieve the above purpose, the present application provides the following technical solutions:

[0007] The first aspect of the present application provides a test problem single processing method, comprising:

[0008] When a new problem is detected, the new problem is determined to belong to a requirement based on the description information of the new problem from each requirement pre-divided;

[0009] The new problem is distributed to the developer corresponding to the requirement to which the new problem belongs based on the pre-recorded personnel association information; wherein the personnel association information includes the corresponding relationship between each requirement and each developer;

[0010] If the requirement to which the new problem belongs does not belong to the requirement of the current version, the type of the new problem is set as requirement change;

[0011] If the requirement to which the new problem belongs belongs to the requirement of the current version, the change information corresponding to the new problem is monitored in real time; wherein the change information of the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer;

[0012] inputting the description information of the new problem and the change information corresponding to the new problem into a pre-trained classification model, and calculating a predicted type of the new problem through the classification model;

[0013] setting the predicted type of the new problem as the type of the new problem.

[0014] Optionally, in the test problem sheet processing method described above, the method further comprises:

[0015] in response to the recording operation of the user, recording the developers corresponding to each requirement to obtain personnel association information;

[0016] uploading each piece of information in the personnel association information to a blockchain according to a task number;

[0017] constructing a system association graph based on the association information of each system and each requirement;

[0018] uploading the system association graph to the blockchain.

[0019] Optionally, in the test problem sheet processing method described above, before the real-time monitoring of the change information corresponding to the new problem, the method further comprises:

[0020] based on the description information of the new problem, determining the system corresponding to the new problem from the system association graph;

[0021] determining whether the system corresponding to the new problem belongs to a local system; if it is determined that the system corresponding to the new problem belongs to the local system, the real-time monitoring of the change information corresponding to the new problem is performed;

[0022] if it is determined that the system corresponding to the new problem does not belong to the local system, the new problem is transferred to the system corresponding to the new problem.

[0023] Optionally, in the test problem sheet processing method described above, the training method of the classification model comprises:

[0024] obtaining description information and change information of a plurality of historical problems, and actual types of each of the historical problems;

[0025] respectively inputting the description information and the change information of each of the historical problems into the classification model, and calculating predicted types of each of the historical problems through the classification model;

[0026] based on a comparison result of the predicted type of each of the historical problems and the actual type of each of the historical problems, determining a current accuracy of the classification model;

[0027] Determine whether the current accuracy of the classification model is greater than a preset threshold;

[0028] If it is determined that the current accuracy of the classification model is not greater than a preset threshold, the parameters of the classification model are adjusted, and the process of inputting the description information and change information of each historical problem into the classification model is returned, and the prediction type of each historical problem is calculated by the classification model.

[0029] If the current accuracy of the classification model is determined to be greater than a preset threshold, the iterative training of the classification model is terminated.

[0030] Optionally, in the above-described method for processing test question forms, after setting the prediction type of the new question to the type of the new question, the method further includes:

[0031] Monitor in real time whether a type modification instruction is received within a preset time period;

[0032] If a type modification instruction is detected within the preset time period, the type of the new problem will be changed to the type indicated by the current type modification instruction;

[0033] After the preset time period, the classification model is optimized using the description information of the new problem, the change information corresponding to the new problem, and the type of the changed new problem.

[0034] A second aspect of this application provides a device for processing test problem forms, comprising:

[0035] The requirement determination unit is used to determine the requirement to which the new problem belongs from a pre-divided set of requirements when a new problem is detected, based on the description information of the new problem.

[0036] A distribution unit is used to distribute the new problem to the developer corresponding to the requirement to which the new problem belongs, based on pre-recorded personnel association information; wherein, the personnel association information includes the correspondence between each requirement and each developer;

[0037] The first setting unit is used to set the type of the new problem to "requirement change" when the requirement to which the new problem belongs is not a requirement of the current version.

[0038] The first monitoring unit is used to monitor the change information corresponding to the new problem in real time when the requirement to which the new problem belongs is a requirement of the current version; wherein, the change information of the new problem refers to the change information of various specified data when the new problem is solved by the corresponding developer;

[0039] The first prediction unit is configured to input the description information of the new question and the change information corresponding to the new question into a pre-trained classification model, and calculate a predicted type of the new question by using the classification model.

[0040] The second setting unit is configured to set the predicted type of the new question as the type of the new question.

[0041] Optionally, the test question sheet processing apparatus further comprises:

[0042] The recording unit is configured to record a developer corresponding to each requirement in response to a recording operation of the user, and obtain personnel association information.

[0043] The first uploading unit is configured to upload each piece of information in the personnel association information to a blockchain according to a task number.

[0044] The construction unit is configured to construct a system association graph based on the association information of each system and each requirement.

[0045] The second uploading unit is configured to upload the system association graph to the blockchain.

[0046] Optionally, the test question sheet processing apparatus further comprises:

[0047] The system determination unit is configured to determine a system corresponding to the new question from the system association graph based on the description information of the new question.

[0048] The first judgment unit is configured to determine whether the system corresponding to the new question belongs to a local system, and if the system corresponding to the new question is determined to belong to the local system, the first monitoring unit performs the real-time monitoring of the change information corresponding to the new question.

[0049] The system flow unit is configured to transfer the new question to the system corresponding to the new question if it is determined that the system corresponding to the new question does not belong to the local system.

[0050] Optionally, the test question sheet processing apparatus further comprises:

[0051] The acquisition unit is configured to acquire description information and change information of a plurality of historical questions, and actual types of each of the historical questions.

[0052] The second prediction unit is configured to input the description information and the change information of each of the historical questions into the classification model respectively, and calculate a predicted type of each of the historical questions by using the classification model.

[0053] a calculation unit configured to determine a current accuracy of the classification model based on a comparison result of a predicted type of each of the historical questions and an actual type of each of the historical questions;

[0054] a second determination unit configured to determine whether the current accuracy of the classification model is greater than a preset threshold value;

[0055] an adjustment unit configured to, when it is determined that the current accuracy of the classification model is not greater than the preset threshold value, adjust a parameter of the classification model, and return the second prediction unit to perform the inputting of the description information and the change information of each of the historical questions into the classification model, and the calculation of the predicted type of each of the historical questions by the classification model;

[0056] an ending unit configured to, when it is determined that the current accuracy of the classification model is greater than the preset threshold value, end the iterative training of the classification model.

[0057] Optionally, in the processing apparatus of the test question sheet, further comprising:

[0058] a second monitoring unit configured to monitor whether a type modification instruction is received in real time within a preset time length;

[0059] a changing unit configured to, when it is monitored that the type modification instruction is received within the preset time length, change a type of the new question to a type indicated by the current type modification instruction;

[0060] an optimization unit configured to, after the preset time length, optimize the classification model by using the description information of the new question, the change information corresponding to the new question, and the changed type of the new question.

[0061] The third aspect of the present application provides an electronic device, comprising:

[0062] a memory and a processor;

[0063] wherein the memory is configured to store a program;

[0064] the processor is configured to execute the program, and the program, when executed, is specifically configured to implement the processing method of the test question sheet according to any one of the preceding aspects.

[0065] The fourth aspect of the present application provides a computer storage medium for storing a computer program, and the computer program, when executed, is configured to implement the processing method of the test question sheet according to any one of the preceding aspects.

[0066] The application provides a processing method of a test problem list. When a new problem is detected, the new problem belongs to a demand is determined based on description information of the new problem from each demand divided in advance, and then the new problem is distributed to a developer corresponding to the demand to which the new problem belongs based on pre-recorded personnel association information. The personnel association information includes the corresponding relationship between each demand and each developer. Therefore, the accurate circulation of the problem list is realized through the pre-configured personnel association information, and the efficiency of the circulation is improved. Moreover, when the demand to which the new problem belongs does not belong to the demand of the current version, the type of the new problem is set as a demand change. When the demand to which the new problem belongs belongs to the demand of the current version, the change information corresponding to the new problem is monitored in real time. The change information corresponding to the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer. Then, the description information of the new problem and the change information corresponding to the new problem are input into a pre-trained classification model, the prediction type of the new problem is calculated through the classification model, and finally the prediction type of the new problem is set as the type of the new problem. Therefore, based on the description information of the new problem and the change information corresponding to the new problem, the type to which the problem belongs is accurately determined through the classification model. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0068] Figure 1 A flowchart of a test problem list processing method provided by an embodiment of the present application;

[0069] Figure 2 A flowchart of an information configuration method provided by an embodiment of the present application;

[0070] Figure 3 A flowchart of a classification model training method provided by an embodiment of the present application;

[0071] Figure 4 A flowchart of a new problem type processing method provided by an embodiment of the present application;

[0072] Figure 5 A structural schematic diagram of a test problem list processing device provided by an embodiment of the present application;

[0073] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0075] In the present application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0076] The embodiment of the present application provides a processing method of a test problem list, as shown in the following figure, comprising the following steps: Figure 1

[0077] S101, when detecting a new problem, determining the demand to which the new problem belongs based on the description information of the new problem from the pre-divided demands.

[0078] The specific tester finds a problem in the test process, and can select to upload the new problem to the problem library in the form of a problem list, and the problem list includes the corresponding information of the new problem, at least including the description information of the new problem.

[0079] It should be noted that in the embodiment of the present application, the demand is pre-subdivided to obtain a plurality of smaller demands, and the information of each demand is recorded. Therefore, the description information of the new problem can be compared with the information of the demand, so as to determine the demand to which the new problem belongs.

[0080] Since the pre-configured information such as personnel association information is needed in the subsequent process, optionally, in another embodiment of the present application, the pre-configuration of information is further included before step S101 is performed. As shown in the following figure, the embodiment of the present application provides a configuration method of information, comprising: Figure 2

[0081] S201, in response to the recording operation of the user, recording the corresponding developer of each demand to obtain personnel association information. ​​

[0082] Optionally, specifically, the name, contact information, ID, corresponding node information, etc. of the developer corresponding to each requirement can be recorded.

[0083] S202, upload each piece of information in the personnel association information to the blockchain according to the task number.

[0084] In order to avoid tampering with information, and also for subsequent use, in the embodiments of the present application, the configured information is uploaded to the blockchain for storage.

[0085] S203, based on the association information of each system and each requirement, a system association relationship graph is constructed.

[0086] Since the requirements to which the problems belong may belong to different systems, the corresponding system of the new problem is determined for subsequent distribution to the corresponding system, so in the embodiments of the present application, a system association relationship graph is further constructed.

[0087] Specifically, each system and each type of requirement are taken as nodes, and the nodes with an association relationship are connected, so as to obtain the constructed system association relationship graph.

[0088] S204, upload the system association relationship graph to the blockchain.

[0089] S102, based on the pre-recorded personnel association information, distribute the new problem to the developer corresponding to the requirement to which the new problem belongs.

[0090] The personnel association information includes the corresponding relationship between each requirement and each developer.

[0091] It should be noted that different requirements may be developed by different developers, and when a problem occurs, the new problem is assigned to the corresponding developer, which can solve the problem more quickly and is also convenient for subsequent accountability. Therefore, in the embodiments of the present application, the personnel association information is pre-constructed, so that the new problem can be distributed to the developer corresponding to the requirement to which the new problem belongs.

[0092] Optionally, the method provided in the embodiments of the present application can be implemented in combination with the blockchain, so that the new problem is distributed to the developer corresponding to the requirement to which the new problem belongs, specifically, the new problem is distributed to the node used by the developer corresponding to the requirement to which the new problem belongs, and in order to enable the developer to process in time, a reminder message can be sent to the developer.

[0093] Optionally, after the new problem is distributed, the state of the new problem is automatically changed to a received state, and then the distribution result is pushed into the blockchain.

[0094] S103, judging whether the demand to which the new problem belongs belongs to the demand of the current version.

[0095] Since the development process is usually iteratively updated and developed, i.e., the version is updated, the demands of different versions are different. The problems existing in the demands of the previous version indicate that changes are needed, rather than the problems existing in the demands are solved. Therefore, if it is judged that the demand to which the new problem belongs does not belong to the demand of the current version, step S104 is executed. If it is judged that the demand to which the new problem belongs belongs to the demand of the current version, step S105 is executed.

[0096] S104, setting the type of the new problem as demand change.

[0097] S105, monitoring the change information corresponding to the new problem in real time.

[0098] The change information of the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer. Optionally, the specified data can refer to the code, configuration, database, environment, etc. of the TGit library.

[0099] It should be noted that after the new problem is assigned to the corresponding developer, the developer will process the new problem, and part or all of the specified data will change. According to the type of the changed specified data, the specific situation of the change is different, which indicates that the type of the new problem is different, and therefore the change information corresponding to the new problem needs to be checked in real time to obtain the change information corresponding to the new problem, which is used for subsequent determination of the type of the new problem.

[0100] Optionally, when the configuration method of the information as shown in Figure 2 is adopted, the corresponding step S105 can be further executed before the step S105 is executed in another embodiment of the present application.

[0101] Based on the description information of the new problem, the system corresponding to the new problem is determined from the system association graph, and it is judged whether the system corresponding to the new problem belongs to the local system.

[0102] If it is judged that the system corresponding to the new problem belongs to the local system, step S105 can be directly executed.

[0103] If the system corresponding to the new problem does not belong to the local system, it is indicated that the subsequent recording and processing of the information about the new problem do not belong to the local system, and therefore the new problem needs to be transferred to the system corresponding to the new problem.

[0104] S106, inputting the description information of the new problem and the change information corresponding to the new problem into a pre-trained classification model, and obtaining the predicted type of the new problem by the classification model.

[0105] Since the description information of the problem can reflect the type of the problem, and since the variation information generated when solving is different due to different problem types, the variation information corresponding to the problem can also reflect the type to which the problem belongs, so in the embodiments of the present application, the type to which the new problem belongs is determined based on the description information of the new problem and the variation information corresponding to the new problem, thereby effectively ensuring the accuracy of the result.

[0106] Moreover, instead of determining in an artificial manner, a pre-trained classification model is used for determination.

[0107] Optionally, another embodiment of the present application provides a training method of a classification model, as shown in Figure 3 The method comprises the following steps:

[0108] S301, obtaining description information and variation information of a plurality of historical problems, and actual types of the historical problems.

[0109] S302, inputting the description information and the variation information of each historical problem into the classification model respectively, and calculating the predicted type of each historical problem through the classification model.

[0110] S303, determining the current accuracy of the classification model based on the comparison result of the predicted type of each historical problem and the actual type of each historical problem.

[0111] Specifically, the number of comparison results in which the predicted type is consistent with the actual type is divided by the number of historical problems to obtain the current accuracy of the classification model.

[0112] S304, judging whether the current accuracy of the classification model is greater than a preset threshold.

[0113] If it is judged that the current accuracy of the classification model is not greater than the preset threshold, it means that the training effect has not reached the expectation, so step S305 is executed at this time. If it is judged that the current accuracy of the classification model is greater than the preset threshold, it means that the training effect has met the expected requirement, so step S306 is executed at this time.

[0114] S305, adjusting the parameters of the classification model.

[0115] It should be noted that since the training effect at this time has not reached the expectation, after the parameters are adjusted in step S305, it is necessary to return to step S302.

[0116] S306, ending the iterative training of the classification model.

[0117] S107, setting the predicted type of the new problem as the type of the new problem.

[0118] Optionally, in another embodiment of the present application, after step S107 is performed, the type of the new problem can be further processed. As shown in Figure 4 The type processing method of the new problem provided by the embodiment of the present application comprises the following steps:

[0119] S401, real-time monitoring whether a type modification instruction is received within a preset time length.

[0120] Optionally, in the embodiment of the present application, the type of the new problem can be pushed into the blockchain. If the developers, testers and the like in the blockchain have objections to the type of the new problem, the type modification instruction can be sent within the preset time length, so if the type modification instruction is received within the preset time length, step S402 is performed.

[0121] It should be noted that within the preset time length, the user can issue the type modification instruction multiple times, and each time the type modification instruction is received, step S402 is performed. Thus, each user can modify the type of the new problem. Of course, the preset time length is set to determine the final type as soon as possible, and the preset time length can also not be set, so that the type of the problem can be modified at any time.

[0122] S402, changing the type of the new problem to the type indicated by the current type modification instruction.

[0123] S403, after the preset time length, optimizing the classification model by using the description information of the new problem, the corresponding change information of the new problem and the changed type of the new problem.

[0124] Since the type of the new problem is modified, it means that the result of the classification model is biased, so at this time, the classification model can be further trained by using the description information of the new problem, the corresponding change information of the new problem and the changed type of the new problem, thereby achieving the effect of optimizing the classification model.

[0125] Optionally, after the type of the new problem is determined, and when it is checked that the current version CQ system version is in an archived state, the state of the new problem is set to a submitted version. After it is detected that the version on the DevOps has been automatically deployed, the state of the new problem is automatically set to a to-be-tested state, and the state conversion is pushed into the blockchain to remind the corresponding tester.

[0126] The embodiment of the present application provides a processing method of a test problem list, when a new problem is detected, the new problem belongs to a demand based on description information of the new problem, and then the new problem is distributed to a developer corresponding to the demand to which the new problem belongs based on pre-recorded personnel association information. Wherein, the personnel association information includes the corresponding relationship between each demand and each developer. Therefore, the accurate circulation of the problem list is realized through the pre-configured personnel association information, and the efficiency of the circulation is improved. And, when the demand to which the new problem belongs does not belong to the demand of the current version, the type of the new problem is set as demand change. When the demand to which the new problem belongs belongs to the demand of the current version, the change information corresponding to the new problem is monitored in real time. Wherein, the change information of the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer. Then, the description information of the new problem and the change information corresponding to the new problem are input into a pre-trained classification model, and the prediction type of the new problem is calculated through the classification model. Finally, the prediction type of the new problem is set as the type of the new problem. Therefore, based on the description information of the new problem and the change information corresponding to the new problem, the type to which the problem belongs is accurately determined through the classification model.

[0127] Another embodiment of the present application provides a processing device of a test problem list, as shown in the figure, Figure 5 The processing device comprises:

[0128] The demand determination unit 501 is configured to determine the demand to which the new problem belongs from the pre-divided demands based on the description information of the new problem when the new problem is detected.

[0129] The distribution unit 502 is configured to distribute the new problem to the developer corresponding to the demand to which the new problem belongs based on the pre-recorded personnel association information.

[0130] Wherein, the personnel association information includes the corresponding relationship between each demand and each developer.

[0131] The first setting unit 503 is configured to set the type of the new problem as demand change when the demand to which the new problem belongs does not belong to the demand of the current version.

[0132] The first monitoring unit 504 is configured to monitor the change information corresponding to the new problem in real time when the demand to which the new problem belongs belongs to the demand of the current version.

[0133] Wherein, the change information of the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer.

[0134] The first prediction unit 505 is configured to input the description information of the new problem and the change information corresponding to the new problem into a pre-trained classification model, and calculate the prediction type of the new problem through the classification model.

[0135] The second setting unit 506 is configured to set the predicted type of the new question as the type of the new question.

[0136] Optionally, the processing apparatus for the test question sheet further includes:

[0137] The recording unit is configured to record the developers corresponding to each requirement in response to a recording operation of a user, to obtain personnel association information.

[0138] The first uploading unit is configured to upload each piece of information in the personnel association information to the blockchain according to a task number.

[0139] The constructing unit is configured to construct a system association graph based on the association information of each system and each requirement.

[0140] The second uploading unit is configured to upload the system association graph to the blockchain.

[0141] Optionally, the processing apparatus for the test question sheet further includes:

[0142] The system determining unit is configured to determine the system corresponding to the new question from the system association graph based on the description information of the new question.

[0143] The first judging unit is configured to judge whether the system corresponding to the new question belongs to the local system, and if the system corresponding to the new question is determined to belong to the local system, the first monitoring unit performs real-time monitoring on the change information corresponding to the new question.

[0144] The system flow unit is configured to transfer the new question to the system corresponding to the new question if it is determined that the system corresponding to the new question does not belong to the local system.

[0145] Optionally, the processing apparatus for the test question sheet further includes:

[0146] The obtaining unit is configured to obtain the description information and the change information of a plurality of historical questions and the actual types of the historical questions.

[0147] The second predicting unit is configured to input the description information and the change information of each historical question into a classification model respectively, and calculate the predicted types of the historical questions by the classification model.

[0148] The calculating unit is configured to determine the current accuracy of the classification model based on a comparison result of the predicted types of the historical questions and the actual types of the historical questions.

[0149] The second judging unit is configured to judge whether the current accuracy of the classification model is greater than a preset threshold.

[0150] The adjusting unit is configured to adjust the parameters of the classification model when it is determined that the current accuracy of the classification model is not greater than the preset threshold, and return the second predicting unit to input the description information and the change information of each historical question into the classification model to calculate the predicted type of each historical question by the classification model.

[0151] The ending unit is configured to end the iterative training of the classification model when it is determined that the current accuracy of the classification model is greater than the preset threshold.

[0152] Optionally, another embodiment of the present application provides a processing device for a test question list, further comprising:

[0153] The second monitoring unit is configured to monitor in real time whether the type modification instruction is received within a preset time length.

[0154] The changing unit is configured to change the type of the new question to the type indicated by the current type modification instruction when it is monitored that the type modification instruction is received within the preset time length.

[0155] The optimizing unit is configured to optimize the classification model by using the description information of the new question, the change information corresponding to the new question, and the changed type of the new question after the preset time length.

[0156] It should be noted that the specific working processes of the various units provided in the above embodiments of the present application can be correspondingly referred to the corresponding steps in the above method embodiments, which will not be described here.

[0157] Another embodiment of the present application provides an electronic device, as shown in the accompanying drawings, comprising: Figure 6

[0158] The memory 601 and the processor 602.

[0159] The memory 601 is configured to store a program.

[0160] The processor 602 is configured to execute the program stored in the memory 601, and the program is executed to implement the processing method of the test question list provided in any one of the above embodiments.

[0161] Another embodiment of the present application provides a computer storage medium for storing a computer program, and the computer program is executed to implement the processing method of the test question list provided in any one of the above embodiments.

[0162] ​Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information for access by a computing device. According to the definitions provided herein, computer readable media does not include transitory computer readable medium, such as a modulated data signal and a carrier wave.

[0163] The test question slip processing method and device, the electronic device, and the storage medium provided by the present application can be used in the financial field or other fields, for example, can be used in the question slip processing application scenario in the financial field. The other fields are any fields except the financial field, for example, the system development field. The above are only examples, and do not limit the application field of the test question slip processing method and device, the electronic device, and the storage medium provided by the present application.

[0164] Those skilled in the art will further appreciate that the individual steps of the example methods and algorithm steps described in connection with the embodiments disclosed herein can be embodied in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein in terms of their general application to the principles of functionality provided by the steps and components. The specific design and implementation details are left to the discretion of the designer for each particular application, and are not intended to limit the scope of the present application. It is therefore contemplated to support one implementation of the described functionality on a suitable electronic hardware platform and / or a computer software platform such as a general purpose computer having an appropriate application program interface.

[0165] The above description of disclosed embodiments provides enabling disclosure sufficient for one of ordinary skill in the art to practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of processing a test trouble ticket, the method comprising: The method comprises the following steps: When a new problem is detected, determining the demand to which the new problem belongs based on the description information of the new problem from pre-divided demands; Based on the pre-recorded personnel association information, the new problem is distributed to the developer corresponding to the demand to which the new problem belongs; wherein the personnel association information includes the correspondence between each demand and each developer; If the demand to which the new problem belongs does not belong to the demand of the current version, the type of the new problem is set as demand change; If the demand to which the new problem belongs belongs to the demand of the current version, the change information corresponding to the new problem is monitored in real time; wherein the change information of the new problem refers to the change information of each specified data when the new problem is solved by the corresponding developer; The description information of the new problem and the change information corresponding to the new problem are input into a pre-trained classification model, and the predicted type of the new problem is calculated through the classification model; The predicted type of the new problem is set as the type of the new problem.

2. The method of claim 1, wherein, Further comprising: In response to the recording operation of the user, the developer corresponding to each demand is recorded to obtain the personnel association information; Each piece of information in the personnel association information is uploaded to the blockchain according to the task number; Based on the association information between each system and each demand, a system association graph is constructed; The system association graph is uploaded to the blockchain.

3. The method of claim 2, wherein, Before the real-time monitoring of the change information corresponding to the new problem, further comprising: Based on the description information of the new problem, the system corresponding to the new problem is determined from the system association graph; Determine whether the system corresponding to the new problem belongs to the local system; wherein if it is determined that the system corresponding to the new problem belongs to the local system, the real-time monitoring of the change information corresponding to the new problem is performed; If it is determined that the system corresponding to the new problem does not belong to the local system, the new problem is transferred to the system corresponding to the new problem.

4. The method of claim 1, wherein, The training method of the classification model comprises: Obtain the description information and change information of a plurality of historical problems, and the actual type of each historical problem; Respectively input the description information and change information of each historical problem into the classification model, and calculate the predicted type of each historical problem through the classification model; Based on the comparison result of the predicted type of each historical problem and the actual type of each historical problem, the current accuracy of the classification model is determined; Determine whether the current accuracy of the classification model is greater than a preset threshold; If it is determined that the current accuracy of the classification model is not greater than the preset threshold, adjust the parameters of the classification model, and return to execute the step of respectively inputting the description information and change information of each historical problem into the classification model, and calculating the predicted type of each historical problem through the classification model; If it is determined that the current accuracy of the classification model is greater than the preset threshold, the iterative training of the classification model is ended.

5. The method of claim 1, wherein, After setting the predicted type of the new problem as the type of the new problem, further comprising: Real-time monitoring whether a type modification instruction is received within a preset time period; If a type modification instruction is received within the preset time period, the type of the new question is changed to the type indicated by the current type modification instruction; After the preset time period, the classification model is optimized using the description information of the new question, the change information corresponding to the new question, and the changed type of the new question.

6. A test problem sheet processing apparatus characterized by comprising: Comprise: A demand determination unit configured to determine a demand to which a new question belongs based on description information of the new question from pre-divided demands when the new question is detected; A distribution unit configured to distribute the new question to a developer corresponding to the demand to which the new question belongs based on pre-recorded personnel association information, wherein the personnel association information comprises a correspondence relationship between each demand and each developer; A first setting unit configured to set the type of the new question as demand change when the demand to which the new question belongs does not belong to a demand of a current version; A first monitoring unit configured to monitor change information corresponding to the new question in real time when the demand to which the new question belongs belongs to a demand of a current version, wherein the change information of the new question refers to change information of each specified data when the new question is solved by the corresponding developer; A first prediction unit configured to input the description information of the new question and the change information corresponding to the new question into a pre-trained classification model to obtain a predicted type of the new question through the classification model; A second setting unit configured to set the predicted type of the new question as the type of the new question.

7. The apparatus of claim 6, wherein, Further comprise: A recording unit configured to record a developer corresponding to each demand to obtain the personnel association information in response to a recording operation of a user; A first uploading unit configured to upload each piece of information in the personnel association information to a blockchain according to a task number; A construction unit configured to construct a system association graph based on association information between each system and each demand; A second uploading unit configured to upload the system association graph to the blockchain.

8. The apparatus of claim 7, wherein, Further comprise: A system determination unit configured to determine a system corresponding to the new question from the system association graph based on the description information of the new question; A first judgment unit configured to judge whether the system corresponding to the new question belongs to a local system, wherein if it is judged that the system corresponding to the new question belongs to the local system, the first monitoring unit performs the real-time monitoring of the change information corresponding to the new question; A system flow unit configured to transfer the new question to the system corresponding to the new question when it is judged that the system corresponding to the new question does not belong to the local system.

9. An electronic device, comprising: Comprise: A memory and a processor; The memory is configured to store a program; The processor is configured to execute the program, and the program is configured to implement the processing method of the test question sheet according to any one of claims 1 to 5 when executed.

10. A computer storage medium, characterized in that, A computer program is stored, and the computer program is configured to implement the processing method of the test question sheet according to any one of claims 1 to 5 when executed.

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