CN113064836A - Business abnormity repairing method and device based on bank system automation test - Google Patents
Business abnormity repairing method and device based on bank system automation test Download PDFInfo
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Abstract
The embodiment of the application provides a business abnormity repairing method and device based on bank system automation test, which can be used in the technical field of test safety, and the method comprises the following steps: determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information; screening one of the repair action chains from the repair action chain set; and executing the repair behavior chain and repairing the abnormal service information. According to the method and the device, the self-adaptation of the test system is realized by independently learning the historical behavior data of the user, the intervention of manual programming on the data is avoided, and the problem of calling failure among different subsystems is solved; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
Description
Technical Field
The application relates to the technical field of test safety, in particular to a service abnormity repairing method and device based on automatic testing of a bank system.
Background
Large commercial banking transaction systems are often divided into multiple subsystems, and development and maintenance are performed by different development and test teams. Moreover, there is a dependence call relationship between subsystems, and a certain development team generally realizes the call of another subsystem through an API description document provided by another team, and is not familiar with the internal implementation logic of the other party in nature, so that it is difficult for the developed automated testing system to properly and thoroughly solve the business logic error occurring when another subsystem program is called, thereby affecting the testing success rate. For example, a large amount of money needs to be exchanged between certain enterprise accounts, and the functions of a public account transaction subsystem and an anti-money laundering subsystem are often involved in the IT system of a commercial bank. When a customer transfers a large amount of money, the system usually needs to identify whether the transaction may involve money laundering, when the two subsystems are maintained by different research and development teams, the problems (such as abnormal parameter settings of fund amount, switches and the like of the anti-money laundering system) are easy to occur, the problems are difficult to be solved properly by the public research and development teams as an upstream system, and a method for properly solving the problems is needed.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a service abnormity repairing method and device based on automatic testing of a bank system, which realizes the autonomous adaptation of a testing system by autonomously learning the historical behavior data of a user, avoids the intervention of artificial programming on the data, and also solves the problem of calling failure among different subsystems; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
In order to solve the technical problem, the application provides the following technical scheme:
one aspect of the present invention provides a method for repairing a business anomaly based on an automated testing of a banking system, including:
determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
screening one of the repair action chains from the repair action chain set;
executing the repair behavior chain and repairing the abnormal service information; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
In a preferred embodiment, the screening one of the repair action chains from the repair action chain set includes:
and screening the repairing behavior chain information from the repairing behavior chain set according to a preset evaluation model.
In a preferred embodiment, further comprising:
searching all corresponding historical repair action chains from the historical repair data according to each piece of historical service abnormal information;
and aggregating all the found historical repair action chains to form a repair action chain set corresponding to the abnormal service information.
In a preferred embodiment, the searching for all corresponding historical repair action chains from the historical repair data according to each piece of historical service anomaly information includes:
extracting corresponding characteristic information from historical service abnormal information, wherein the characteristic information comprises: the method comprises the following steps of (1) service name, service number, service running time and abnormal information code;
searching all historical service normal information corresponding to the characteristic information in the historical repair data according to the characteristic information;
and generating a corresponding repair action chain of the historical service abnormity according to the action chain in the normal information of each historical service.
In a preferred embodiment, further comprising: updating the set of repair action chains.
In a preferred embodiment, the historical repair data is taken from a set time window, and the updating the set of repair action chains includes:
the abnormal occurrence time point is used as the starting point of the set time window, the abnormal solution time point is used as the end point of the set time window, and the historical repair data of a plurality of users are reselected;
and generating an updated repairing behavior chain set based on the reselected historical repairing data, and replacing the original repairing behavior chain set with the updated repairing behavior chain set so as to update the repairing behavior chain set.
In a preferred embodiment, each repair action chain information corresponds to an evaluation score; the selecting an optimal repair action chain from a preset repair action chain set includes:
sequencing each repairing behavior chain in the repairing behavior chain set according to the sequence of the evaluation scores from large to small to obtain a repairing behavior chain sequence;
and selecting the repair action chain at a set position in the repair action chain sequence as the screened repair action chain information.
In a preferred embodiment, the setting position is a first position in the repair action chain sequence, and the selecting a repair action chain at the setting position in the repair action chain sequence as the screened repair action chain information includes:
and selecting the repair behavior chain positioned at the head in the repair behavior chain sequence as the screened repair behavior chain information.
In a preferred embodiment, each repair action chain information corresponds to an evaluation score; the method further comprises the following steps:
and generating all repair behavior chain scores according to the complexity and the historical use frequency of the repair behavior chain.
In a preferred embodiment, the complexity of the chain of repair actions includes: the method comprises the following steps of (1) generating scores of all the repair action chains according to the complexity and the historical use frequency of the repair action chains, wherein the length of the repair action chains and the circle complexity of the repair action chains comprise:
and generating all repairing behavior chain scores according to the length of the repairing behavior chain, the circle complexity and the historical use frequency.
In another aspect of the present invention, a device for repairing a business abnormality based on an automated testing of a banking system is provided, which includes:
the repair behavior chain set determining module is used for determining a repair behavior chain set according to the service abnormal information in the automatic test process and the corresponding relation between the service abnormal information and the repair behavior chain set; the repair action chain information comprises at least one piece of repair action information;
the repairing behavior chain screening module is used for screening one repairing behavior chain from the repairing behavior chain set;
the execution repairing module executes the repairing behavior chain and repairs the abnormal service information; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
In another aspect of the present invention, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for repairing a business anomaly based on an automated testing of a banking system.
In still another aspect of the present invention, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for repairing a business anomaly based on an automated testing of a banking system.
According to the technical scheme, the service abnormity repairing method based on the automatic testing of the bank system comprises the following steps: determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information; screening one of the repair action chains from the repair action chain set; and executing the repair behavior chain and repairing the abnormal service information. According to the invention, the self-adaptation of the test system is realized by independently learning the historical behavior data of the user, so that the intervention of manual programming on the data is avoided, and the problem of call failure among different subsystems is solved; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a service anomaly repairing method based on automated testing of a banking system.
Fig. 2 is a schematic diagram of a repair action chain set generation flow.
Fig. 3 is a schematic diagram of a historical repair action chain acquisition flow.
Fig. 4 is a schematic diagram of a repair action chain set update flow.
FIG. 5 is a schematic flow chart of a chain of screening repair actions.
Fig. 6 is a schematic structural diagram of a service abnormality repairing device based on automated testing of a banking system.
Fig. 7 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the method and the device for repairing the business anomaly based on the automated testing of the bank system disclosed by the application can be used in the technical field of testing safety and can also be used in any field except the technical field of computers.
In a specific embodiment, the present application provides a method for repairing a business anomaly based on an automated testing of a banking system, as shown in fig. 1, including:
s1, determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
in particular, automated testing is a process that translates human-driven testing behavior into machine execution. Typically, after a test case is designed and passes review, the test is performed step by a tester according to the procedures described in the test case, resulting in a comparison of the actual results with the expected results. In the process, in order to save manpower, time or hardware resources, the testing efficiency is improved. In the process of the automated testing, due to some reasons, the business logic in the test case cannot be normally executed, the business exception occurs, and the business exception information is recorded in the test log. For example, a test case is used for testing account withdrawal business, because the withdrawal amount of the input test case is greater than the total account amount, the test case is abnormal in execution, and the recorded abnormal code in the abnormal information is insufficient in balance. And determining a corresponding repair action chain set according to the service abnormality information, wherein the repair action chain set at least comprises one repair action chain aiming at the service abnormality, such as insufficient balance. In a particular embodiment, the chain of repair actions is a chain of actions consisting of at least one repair action. For example, two chains of repair actions are included in the set of chains of repair actions for an insufficient balance. Wherein, the first repair action chain is: 1. reducing the withdrawal amount to be lower than the total amount, 2, executing withdrawal business operation, wherein the repair action chain comprises two repair actions; the second repair action chain is: 1. and (2) supplementing the total amount of the account to the amount of withdrawal, and performing withdrawal service operation, wherein the repair action chain also comprises two repair actions. The basic logic of the repair action chain generation is the time-efficient requirement of problem resolution and the invariance of the processing object. Business errors typically require resolution on the day, or even immediately, with few instances of delays. When the counter transaction of the teller agent is abnormal, the same teller is usually responsible for handling the transaction. The users are more limited to processing the services belonging to the users. And finally, the closed loop of final repair is realized by a transaction which belongs to the same service with the failed transaction and is successfully transacted. Only a series of transactions between the user making a transaction error and the user making a transaction successful need to be found, which can be considered as a chain of repair actions for modifying the error. In a specific embodiment, the step of generating the set of repair action chains, as shown in fig. 2, includes:
s11, searching all corresponding historical repair action chains from the historical repair data according to the abnormal information of each historical service;
specifically, the set of the repair action chain for each service exception is generated based on a summary of a historical repair action chain for the service exception, and the historical repair data includes the historical repair action chain for the service exception. In a specific embodiment, the historical repair data is all repair data included in one historical time window, and the repair data is repair behavior data of a user on a service abnormality in an actual service operation. For example, the end point of the historical time window is set as the current time point, and the start point of the window is the time point corresponding to the subtraction of 24 hours from the current time point, that is, the duration of the historical time window is 24 hours.
In a specific embodiment, the searching for all corresponding historical repair action chains from the historical repair data according to each piece of historical service anomaly information, as shown in fig. 3, includes:
s111, extracting corresponding characteristic information from the historical service abnormal information, wherein the characteristic information comprises: the method comprises the following steps of (1) service name, service number, service running time and abnormal information code;
s112, searching all historical service normal information corresponding to the characteristic information in the historical repair data according to the characteristic information;
and S113, generating a corresponding repair action chain of the historical service abnormity according to the action chain in the historical service normal information.
Specifically, historical repair data in a set time window is acquired, all corresponding historical repair action chains are found out according to characteristics of a business name, a business code, an abnormal information code and the like in business abnormal information, for example, the business name in one piece of business abnormal information is a deposit business, the business code is 001, the abnormal information corresponding to the abnormal information code 01 is a password error, and 20 historical repair action chains corresponding to the password error occurring in the deposit business in all historical repair data in 24 hours in the time window are found out according to the business abnormal information.
And S12, collecting all the found historical repair action chains to form a repair action chain set corresponding to the abnormal service information.
Specifically, all historical repair action chains corresponding to the found business abnormality are collected together to form a repair action chain set. It is understood that, the collection of all the repair action chains for a certain business anomaly may be performed in various ways, for example, all the repair action chains for a deposit password error are marked as 1, or stored in a folder or a database table to form a repair action chain collection, which facilitates subsequent query.
In a specific embodiment, the set of repair action chains is updatable, and the specific steps of updating are as shown in fig. 4, including:
s121, reselecting historical repair data of a plurality of users by taking an abnormal occurrence time point as a starting point of the set time window and taking an abnormal solution time point as an end point of the set time window;
specifically, when the test case is executed, an execution exception occurs, and it may be directly preferred from the repair action chain, after the exception is repaired and executed, in order to make it possible to include all the repair action chains for the exception in the set of repair action chains for the exception later, a time point when the exception occurs is taken as a starting point of a set time window, and a solved time point is taken as an end point, and a plurality of user historical repair data are reselected, where the user is a user actually executing the business, for example, a user of a deposit business has a bank counter operator, an ATM machine user, a mobile banking user, and the like.
And S122, generating an updated repairing behavior chain set based on the reselected historical repairing data, and replacing the original repairing behavior chain set with the updated repairing behavior chain set so as to update the repairing behavior chain set.
Specifically, after the historical repair data is reselected, all repair behavior chains in the new historical repair data are searched again according to the characteristic information in the abnormal service information, and the set is an updated repair behavior chain set to replace the original repair behavior chain set.
S2, screening one of the repair action chains from the repair action chain set;
specifically, when a service execution exception occurs in the test process, after a corresponding repair action chain set is determined according to the service exception information, a repair action chain needs to be screened from the repair action chain set to repair the exception. In a specific embodiment, in order to obtain an optimal repair action chain, evaluation criteria for the repair action chain are introduced. And generating all repair behavior chain scores according to the complexity and the historical use frequency of the repair behavior chain, wherein the complexity of the repair behavior chain comprises the length of the repair behavior chain and the circle complexity of the repair behavior chain. For example, if one chain of repair actions consists of 2 repair actions, without loops or recursions, its complexity score may be considered as 80, another chain of repair actions consists of 8 repair actions, and if there are no loops and recursions, its complexity may be considered as 20, and if one chain of repair actions consists of 6 repair actions, and if there is a loop action, its complexity may be considered as 35. The smaller the complexity of the repair action chain is, the simpler the repair step is, so the scores of the repair action chain are increased in sequence from large to small according to the repair complexity, the more universal or reasonable scores are indicated by the historical use frequency, and the scores of the repair action chain are increased in sequence from small to large according to the occurrence frequency.
The overall scoring formula is as follows:
S＝S1*W1+S2*W2
s is total score, S1 is repair action chain complexity single score (single score rule self-defined, 0< ═ S1< ═ 100), S2 is history use frequency single score (single score rule self-defined, 0< ═ S2< ═ 100), W1 is repair action chain complexity single weight (weight value self-defined, 0< W1<1), history use frequency single weight (weight value self-defined, 0< W2<1), W1+ W2 is 100%. For example, for an abnormal repair action chain with insufficient withdrawal balance, there are 4 repair action chains, and their individual scores and weights are: 80, 0.2, 50, 0.4; 70, 0.6, 90, 0.8; 40, 0.9, 90, 0.4; 45,0.6, 30,0.8. The scores of the 4 repair behavior chains are calculated by using a scoring formula and are respectively as follows: 36, 114, 72, 51. Generally, in order to repair the service abnormality appearing in the test most quickly and best, a chain of repair actions with the highest score is adopted. Selecting a repair action chain from a preset repair action chain set, as shown in fig. 5, includes:
s21, sequencing each repairing behavior chain in the repairing behavior chain set according to the sequence of the evaluation scores from large to small to obtain a repairing behavior chain sequence;
in a specific embodiment, for example, the evaluation scores of 4 repair behavior chains are: and if the first is 36, the second is 114, the third is 72 and the fourth is 51, sequencing the repair action chains according to the sequence of the evaluation scores from large to small to obtain a repair action chain sequence, namely the second, the third, the fourth and the first.
And S22, selecting the repair action chain at the set position in the repair action chain sequence as the screened repair action chain information.
Specifically, the set position is generally at a head position of the sequence, for example, a head of the sequence. In a specific embodiment, if the sequence of the repair action chain is 1, 2, 3, 4, 5, the repair action chain 1 is selected first, and if the repair action chain 1 cannot obtain an effective repair effect, the repair action chain 2 is selected, and so on.
S3, executing the repair action chain to repair the abnormal information of the service; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
The present invention will be further described with reference to a specific scenario.
The program scans that a business error occurs in a current withdrawal transaction of the ATM, the error information is that the account balance is insufficient, the client transacts the transaction is A, and the occurrence time is 2000-01-0110: 00: 01.994766. Then the program continues to search the desensitization log pool with the condition of client A, extracts all transaction records about A between 2000-01-0110: 00:01.994766 to 2000-01-0200: 00:00.000000, completes a transaction of equal-occurrence current withdrawal when A is 2000-01-0110: 10:01.994766 after the search, and during the period A, a petty loan agreement signing transaction and a system automatically issue a petty loan to A are also generated, namely the program judges that the current withdrawal fails, the petty loan agreement signing succeeds, the system releases money successfully and the current withdrawal succeeds to form a repair action chain, wherein the repair transaction is petty loan agreement signing and system loan, the complexity of the repair action chain is judged to be 2, and meanwhile, test cases for signing and paying by the system of the petty loan agreement are also found in the test case pool. And searching the repair action chain set by the program to find out the repair action chain set with insufficient balance of the current inventory withdrawal at the current time, and assuming that two repair action chains (current transfer, and redemption of T +0 financial products) are searched, wherein the lengths of the two repair action chains are both 1, and the occurrence times are respectively 20 times and 10 times. The program then determines the chain of repair actions (petty loan agreement sign-system loan) as the first chain of new repair actions to occur. Assuming that a linear strategy (1:100, 2:90, 3:80.... 10 or more: 10) is adopted for the complexity single scoring of the repair action chain, the weight accounts for 40%, and a ranking scoring strategy (a first name: 100, a second name: 90, a third name: 80.... 10 or more) is adopted for the historical usage frequency single scoring of the repair action chain, and the weight accounts for 60%. The current three chains of repair actions are scored (live transfer: 100 × 40% +100 × 60%: 100, T +0 cash-management product redemption: 100 × 40% +90 × 60%: 94, small loan agreement sign-system payout: 90 × 40% +80 × 60%: 84). Because the current repair action chain ranking order is live transfer, T +0 financial product redemption, petty loan agreement sign-system loan. And finally, updating the latest results of the three repair action chains to the repair action chain set.
In the specific test process, the following six test cases A, B, C, D, E and F are assumed to exist in the test case pool
The arrangement sequence of case execution is A- > B- > C- > D- > E- > F.
The chain of repair actions for each function is shown in the following table:
| function name | Repairing a chain of behaviors |
| A | C->F |
| B | A |
| C | E |
| D | F->B |
| E | D |
| F | C |
And after the test is started, executing the test cases according to the sequence of A- > B- > C- > D- > E- > F. Assuming that A, B, C all executed successfully, an exception occurs at execution-to-D. At this time, the system performs the following steps:
1. and extracting the repair action chain (F- > B) of the D.
2. And executing the case F, and when the F is similarly executed abnormally, repairing the F by recursion first and extracting a repairing behavior chain (C) of the F.
3. And F is executed, the repairing step is executed by using the case C, after C is successfully executed, the state of C is set to be executed, and then F is executed again.
4. And after the case F is successfully executed, setting the state of the F as executed.
5. And executing the case B, and setting the state of the case B as executed after the case B is successfully executed.
6. And re-executing the case D, wherein D can be successfully executed after the repair action chain (F- > B) is completely executed, and meanwhile, D is set to be in an executed state.
7. Case E is executed.
8. If the system finds that the case F is already in the executed state, the case F is executed, and the case F is skipped and is not executed any more, so that unnecessary repeated execution is avoided.
According to the service abnormity repairing method based on the automatic testing of the bank system, provided by the invention, the self-adaptation of the testing system is realized by independently learning the historical behavior data of the user, the intervention of manual programming on the data is avoided, and the problem of calling failure among different subsystems is solved; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
In terms of software, the present application provides an embodiment of a service anomaly repairing apparatus based on an automated banking system test, for executing all or part of the contents in the service anomaly repairing method based on an automated banking system test, and referring to fig. 6, the service anomaly repairing apparatus based on an automated banking system test specifically includes the following contents:
the repair action chain set determining module 1 is used for determining a repair action chain set according to the service abnormal information in the automatic test process and the corresponding relation between the service abnormal information and the repair action chain set; the repair action chain information comprises at least one piece of repair action information;
the repair action chain screening module 2 is used for screening one repair action chain from the repair action chain set;
the execution repairing module 3 is used for executing the repairing behavior chain and repairing the abnormal service information; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
In a specific embodiment, the present application provides a service anomaly repairing apparatus based on an automated testing of a banking system, which is configured to perform the following steps:
s1, determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
in particular, automated testing is a process that translates human-driven testing behavior into machine execution. Typically, after a test case is designed and passes review, the test is performed step by a tester according to the procedures described in the test case, resulting in a comparison of the actual results with the expected results. In the process, in order to save manpower, time or hardware resources, the testing efficiency is improved. In the process of the automated testing, due to some reasons, the business logic in the test case cannot be normally executed, the business exception occurs, and the business exception information is recorded in the test log. For example, a test case is used for testing account withdrawal business, because the withdrawal amount of the input test case is greater than the total account amount, the test case is abnormal in execution, and the recorded abnormal code in the abnormal information is insufficient in balance. And determining a corresponding repair action chain set according to the service abnormality information, wherein the repair action chain set at least comprises one repair action chain aiming at the service abnormality, such as insufficient balance. In a particular embodiment, the chain of repair actions is a chain of actions consisting of at least one repair action. For example, two chains of repair actions are included in the set of chains of repair actions for an insufficient balance. Wherein, the first repair action chain is: 1. reducing the withdrawal amount to be lower than the total amount, 2, executing withdrawal business operation, wherein the repair action chain comprises two repair actions; the second repair action chain is: 1. and (2) supplementing the total amount of the account to the amount of withdrawal, and performing withdrawal service operation, wherein the repair action chain also comprises two repair actions. The basic logic of the repair action chain generation is the time-efficient requirement of problem resolution and the invariance of the processing object. Business errors typically require resolution on the day, or even immediately, with few instances of delays. When the counter transaction of the teller agent is abnormal, the same teller is usually responsible for handling the transaction. The users are more limited to processing the services belonging to the users. And finally, the closed loop of final repair is realized by a transaction which belongs to the same service with the failed transaction and is successfully transacted. Only a series of transactions between the user making a transaction error and the user making a transaction successful need to be found, which can be considered as a chain of repair actions for modifying the error. In a specific embodiment, a repair action chain set generation module in the apparatus is configured to perform the following steps:
s11, searching all corresponding historical repair action chains from the historical repair data according to the abnormal information of each historical service;
specifically, the set of the repair action chain for each service exception is generated based on a summary of a historical repair action chain for the service exception, and the historical repair data includes the historical repair action chain for the service exception. In a specific embodiment, the historical repair data is all repair data included in one historical time window, and the repair data is repair behavior data of a user on a service abnormality in an actual service operation. For example, the end point of the historical time window is set as the current time point, and the start point of the window is the time point corresponding to the subtraction of 24 hours from the current time point, that is, the duration of the historical time window is 24 hours.
In a specific embodiment, the repair action chain lookup unit of the apparatus is configured to perform the following steps:
s111, extracting corresponding characteristic information from the historical service abnormal information, wherein the characteristic information comprises: the method comprises the following steps of (1) service name, service number, service running time and abnormal information code;
s112, searching all historical service normal information corresponding to the characteristic information in the historical repair data according to the characteristic information;
and S113, generating a corresponding repair action chain of the historical service abnormity according to the action chain in the historical service normal information.
Specifically, historical repair data in a set time window is acquired, all corresponding historical repair action chains are found out according to characteristics of a business name, a business code, an abnormal information code and the like in business abnormal information, for example, the business name in one piece of business abnormal information is a deposit business, the business code is 001, the abnormal information corresponding to the abnormal information code 01 is a password error, and 20 historical repair action chains corresponding to the password error occurring in the deposit business in all historical repair data in 24 hours in the time window are found out according to the business abnormal information.
And S12, collecting all the found historical repair action chains to form a repair action chain set corresponding to the abnormal service information.
Specifically, all historical repair action chains corresponding to the found business abnormality are collected together to form a repair action chain set. It is understood that, the collection of all the repair action chains for a certain business anomaly may be performed in various ways, for example, all the repair action chains for a deposit password error are marked as 1, or stored in a folder or a database table to form a repair action chain collection, which facilitates subsequent query.
In a specific embodiment, the repair action chain set update module of the apparatus is configured to perform the following steps:
s121, reselecting historical repair data of a plurality of users by taking an abnormal occurrence time point as a starting point of the set time window and taking an abnormal solution time point as an end point of the set time window;
specifically, when the test case is executed, an execution exception occurs, and it may be directly preferred from the repair action chain, after the exception is repaired and executed, in order to make it possible to include all the repair action chains for the exception in the set of repair action chains for the exception later, a time point when the exception occurs is taken as a starting point of a set time window, and a solved time point is taken as an end point, and a plurality of user historical repair data are reselected, where the user is a user actually executing the business, for example, a user of a deposit business has a bank counter operator, an ATM machine user, a mobile banking user, and the like.
And S122, generating an updated repairing behavior chain set based on the reselected historical repairing data, and replacing the original repairing behavior chain set with the updated repairing behavior chain set so as to update the repairing behavior chain set.
Specifically, after the historical repair data is reselected, all repair behavior chains in the new historical repair data are searched again according to the characteristic information in the abnormal service information, and the set is an updated repair behavior chain set to replace the original repair behavior chain set.
S2, screening one of the repair action chains from the repair action chain set;
specifically, when a service execution exception occurs in the test process, after a corresponding repair action chain set is determined according to the service exception information, a repair action chain needs to be screened from the repair action chain set to repair the exception. In a specific embodiment, in order to obtain an optimal repair action chain, evaluation criteria for the repair action chain are introduced. And generating all repair behavior chain scores according to the complexity and the historical use frequency of the repair behavior chain, wherein the complexity of the repair behavior chain comprises the length of the repair behavior chain and the circle complexity of the repair behavior chain. For example, if one chain of repair actions consists of 2 repair actions, without loops or recursions, its complexity score may be considered as 80, another chain of repair actions consists of 8 repair actions, and if there are no loops and recursions, its complexity may be considered as 20, and if one chain of repair actions consists of 6 repair actions, and if there is a loop action, its complexity may be considered as 35. The smaller the complexity of the repair action chain is, the simpler the repair step is, so the scores of the repair action chain are increased in sequence from large to small according to the repair complexity, the more universal or reasonable scores are indicated by the historical use frequency, and the scores of the repair action chain are increased in sequence from small to large according to the occurrence frequency.
The overall scoring formula is: S1W 1+ S2W 2
S is total score, S1 is repair action chain complexity single score (single score rule self-defined, 0< ═ S1< ═ 100), S2 is history use frequency single score (single score rule self-defined, 0< ═ S2< ═ 100), W1 is repair action chain complexity single weight (weight value self-defined, 0< W1<1), history use frequency single weight (weight value self-defined, 0< W2<1), W1+ W2 is 100%. For example, for an abnormal repair action chain with insufficient withdrawal balance, there are 4 repair action chains, and their individual scores and weights are: 80, 0.2, 50, 0.4; 70, 0.6, 90, 0.8; 40, 0.9, 90, 0.4; 45,0.6, 30,0.8. The scores of the 4 repair behavior chains are calculated by using a scoring formula and are respectively as follows: 36, 114, 72, 51. Generally, in order to repair the service abnormality appearing in the test most quickly and best, a chain of repair actions with the highest score is adopted. In a specific embodiment, the screening repair action chain unit of the apparatus is configured to perform the following steps:
s21, sequencing each repairing behavior chain in the repairing behavior chain set according to the sequence of the evaluation scores from large to small to obtain a repairing behavior chain sequence;
in a specific embodiment, for example, the evaluation scores of 4 repair behavior chains are: and if the first is 36, the second is 114, the third is 72 and the fourth is 51, sequencing the repair action chains according to the sequence of the evaluation scores from large to small to obtain a repair action chain sequence, namely the second, the third, the fourth and the first.
And S22, selecting the repair action chain at the set position in the repair action chain sequence as the screened repair action chain information.
Specifically, the set position is generally at a head position of the sequence, for example, a head of the sequence. In a specific embodiment, if the sequence of the repair action chain is 1, 2, 3, 4, 5, the repair action chain 1 is selected first, and if the repair action chain 1 cannot obtain an effective repair effect, the repair action chain 2 is selected, and so on.
S3, executing the repair action chain to repair the abnormal information of the service; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
The present invention will be further described with reference to a specific scenario.
The program scans that a business error occurs in a current withdrawal transaction of the ATM, the error information is that the account balance is insufficient, the client transacts the transaction is A, and the occurrence time is 2000-01-0110: 00: 01.994766. Then the program continues to search the desensitization log pool with the condition of client A, extracts all transaction records about A between 2000-01-0110: 00:01.994766 to 2000-01-0200: 00:00.000000, completes a transaction of equal-occurrence current withdrawal when A is 2000-01-0110: 10:01.994766 after the search, and during the period A, a petty loan agreement signing transaction and a system automatically issue a petty loan to A are also generated, namely the program judges that the current withdrawal fails, the petty loan agreement signing succeeds, the system releases money successfully and the current withdrawal succeeds to form a repair action chain, wherein the repair transaction is petty loan agreement signing and system loan, the complexity of the repair action chain is judged to be 2, and meanwhile, test cases for signing and paying by the system of the petty loan agreement are also found in the test case pool. And searching the repair action chain set by the program to find out the repair action chain set with insufficient balance of the current inventory withdrawal at the current time, and assuming that two repair action chains (current transfer, and redemption of T +0 financial products) are searched, wherein the lengths of the two repair action chains are both 1, and the occurrence times are respectively 20 times and 10 times. The program then determines the chain of repair actions (petty loan agreement sign-system loan) as the first chain of new repair actions to occur. Assuming that a linear strategy (1:100, 2:90, 3:80.... 10 or more: 10) is adopted for the complexity single scoring of the repair action chain, the weight accounts for 40%, and a ranking scoring strategy (a first name: 100, a second name: 90, a third name: 80.... 10 or more) is adopted for the historical usage frequency single scoring of the repair action chain, and the weight accounts for 60%. The current three chains of repair actions are scored (live transfer: 100 × 40% +100 × 60%: 100, T +0 cash-management product redemption: 100 × 40% +90 × 60%: 94, small loan agreement sign-system payout: 90 × 40% +80 × 60%: 84). Because the current repair action chain ranking order is live transfer, T +0 financial product redemption, petty loan agreement sign-system loan. And finally, updating the latest results of the three repair action chains to the repair action chain set.
In the specific test process, the following six test cases A, B, C, D, E and F are assumed to exist in the test case pool
The arrangement sequence of case execution is A- > B- > C- > D- > E- > F.
The chain of repair actions for each function is shown in the following table:
| function name | Repairing a chain of behaviors |
| A | C->F |
| B | A |
| C | E |
| D | F->B |
| E | D |
| F | C |
And after the test is started, executing the test cases according to the sequence of A- > B- > C- > D- > E- > F. Assuming that A, B, C all executed successfully, an exception occurs at execution-to-D. At this time, the system performs the following steps:
1. and extracting the repair action chain (F- > B) of the D.
2. And executing the case F, and when the F is similarly executed abnormally, repairing the F by recursion first and extracting a repairing behavior chain (C) of the F.
3. And F is executed, the repairing step is executed by using the case C, after C is successfully executed, the state of C is set to be executed, and then F is executed again.
4. And after the case F is successfully executed, setting the state of the F as executed.
5. And executing the case B, and setting the state of the case B as executed after the case B is successfully executed.
6. And re-executing the case D, wherein D can be successfully executed after the repair action chain (F- > B) is completely executed, and meanwhile, D is set to be in an executed state.
7. Case E is executed.
8. If the system finds that the case F is already in the executed state, the case F is executed, and the case F is skipped and is not executed any more, so that unnecessary repeated execution is avoided.
As can be seen from the above description, the service abnormality repairing apparatus based on the automated testing of the banking system according to the present invention is configured to perform the following steps: the self-adaptation of the test system is realized by independently learning the historical behavior data of the user, so that the intervention of manual programming on the data is avoided, and the problem of call failure among different subsystems is solved; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
In terms of hardware, the present application provides an embodiment of an electronic device for implementing all or part of contents in a service exception recovery method based on an automated testing of a banking system, where the electronic device specifically includes the following contents:
fig. 7 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 7, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 7 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the service exception repair function based on the automated testing of the banking system can be integrated into the central processor. Wherein the central processor may be configured to control:
s1, determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
s2, screening one of the repair action chains from the repair action chain set;
s3, executing the repair action chain to repair the abnormal information of the service; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
As can be seen from the above description, the electronic device provided in the embodiment of the present application implements the self-adaptation of the test system by independently learning the historical behavior data of the user, thereby avoiding the intervention of human programming on data and solving the problem of call failure between different subsystems; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
In another embodiment, the service abnormality repairing apparatus based on the bank system automation test may be configured separately from the central processor 9100, for example, the service abnormality repairing apparatus based on the bank system automation test may be configured as a chip connected to the central processor 9100, and the service abnormality repairing function based on the bank system automation test is realized by the control of the central processor.
As shown in fig. 7, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 7; further, the electronic device 9600 may further include components not shown in fig. 7, which may be referred to in the art.
As shown in fig. 7, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the method for repairing a business anomaly based on an automated banking system test in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps of the method for repairing a business anomaly based on an automated banking system test, where an execution subject of the method is a server or a client, for example, when the processor executes the computer program, the processor implements the following steps:
s1, determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
s2, screening one of the repair action chains from the repair action chain set;
s3, executing the repair action chain to repair the abnormal information of the service; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application implements the self-adaptation of the test system by independently learning the historical behavior data of the user, thereby avoiding the intervention of human programming on data and solving the problem of call failure between different subsystems; on the other hand, the evaluation model of the repair behavior chain is introduced to assist, and the repair behavior chain which is simpler, more common and more reasonable is preferentially used, so that the test practice is closer to and simulates the actual application condition.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (13)
1. A business abnormity repairing method based on bank system automation test is characterized by comprising the following steps:
determining a repair action chain set according to the service exception information and the corresponding relation between the service exception information and the repair action chain set in the automatic test process; the repair action chain information comprises at least one piece of repair action information;
screening one of the repair action chains from the repair action chain set;
executing the repair behavior chain and repairing the abnormal service information; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
2. The method for repairing business abnormality based on automated testing of banking system as claimed in claim 1, wherein said screening out one of the repair action chains from the set of repair action chains comprises:
and screening the repairing behavior chain information from the repairing behavior chain set according to a preset evaluation model.
3. The method for repairing the business abnormality based on the automated testing of the banking system according to claim 1, further comprising:
searching all corresponding historical repair action chains from the historical repair data according to each piece of historical service abnormal information;
and aggregating all the found historical repair action chains to form a repair action chain set corresponding to the abnormal service information.
4. The method for repairing business abnormality based on automated testing of banking system according to claim 3, wherein the step of searching all corresponding historical repair action chains from the historical repair data according to each historical business abnormality information includes:
extracting corresponding characteristic information from historical service abnormal information, wherein the characteristic information comprises: the method comprises the following steps of (1) service name, service number, service running time and abnormal information code;
searching all historical service normal information corresponding to the characteristic information in the historical repair data according to the characteristic information;
and generating a corresponding repair action chain of the historical service abnormity according to the action chain in the normal information of each historical service.
5. The method for repairing the business abnormality based on the automated testing of the banking system according to claim 1, further comprising: updating the set of repair action chains.
6. The method for repairing business abnormality based on automated testing of banking system as claimed in claim 5, wherein said historical repair data is taken from a set time window, and said updating said set of chain of repair actions comprises:
the abnormal occurrence time point is used as the starting point of the set time window, the abnormal solution time point is used as the end point of the set time window, and the historical repair data of a plurality of users are reselected;
and generating an updated repairing behavior chain set based on the reselected historical repairing data, and replacing the original repairing behavior chain set with the updated repairing behavior chain set so as to update the repairing behavior chain set.
7. The business anomaly repair method based on the bank system automation test as claimed in claim 1, wherein each repair action chain information corresponds to an evaluation score; the screening out one of the repair action chains from the repair action chain set comprises:
sequencing each repairing behavior chain in the repairing behavior chain set according to the sequence of the evaluation scores from large to small to obtain a repairing behavior chain sequence;
and selecting the repair action chain at a set position in the repair action chain sequence as the screened repair action chain information.
8. The method according to claim 7, wherein the set position is a first position in the repair action chain sequence, and the selecting a repair action chain at a set position in the repair action chain sequence as the screened repair action chain information includes:
and selecting the repair behavior chain positioned at the head in the repair behavior chain sequence as the screened repair behavior chain information.
9. The business anomaly repair method based on the bank system automation test as claimed in claim 1, wherein each repair action chain information corresponds to an evaluation score; the method further comprises the following steps:
and generating all repair behavior chain scores according to the complexity and the historical use frequency of the repair behavior chain.
10. The method for repairing business abnormality based on automated testing of bank system according to claim 9, wherein the complexity of the chain of repairing behaviors includes: the method comprises the following steps of (1) generating scores of all the repair action chains according to the complexity and the historical use frequency of the repair action chains, wherein the length of the repair action chains and the circle complexity of the repair action chains comprise:
and generating all repairing behavior chain scores according to the length of the repairing behavior chain, the circle complexity and the historical use frequency.
11. A business abnormity repairing device based on bank system automation test is characterized by comprising:
the repair behavior chain set determining module is used for determining a repair behavior chain set according to the service abnormal information in the automatic test process and the corresponding relation between the service abnormal information and the repair behavior chain set; the repair action chain information comprises at least one piece of repair action information;
the repairing behavior chain screening module is used for screening one repairing behavior chain from the repairing behavior chain set;
the execution repairing module executes the repairing behavior chain and repairs the abnormal service information; wherein each repair action chain in the set of repair action chains is determined based on historical repair data for a plurality of users; the historical repair data comprises historical service exception information and a corresponding historical repair behavior chain.
12. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for repairing a business anomaly based on an automated testing of a banking system according to any one of claims 1 to 10 when executing the program.
13. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for repairing a business anomaly based on an automated testing of a banking system according to any one of claims 1 to 10.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117333127A (en) * | 2023-10-09 | 2024-01-02 | 广州嘉磊元新信息科技有限公司 | Service automatic processing method based on RPA |
| CN117333127B (en) * | 2023-10-09 | 2024-04-05 | 广州嘉磊元新信息科技有限公司 | Service automatic processing method based on RPA |
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