CN117453572A - Regression test item determination method, device, equipment and storage medium - Google Patents

Regression test item determination method, device, equipment and storage medium Download PDF

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Publication number
CN117453572A
CN117453572A CN202311634670.4A CN202311634670A CN117453572A CN 117453572 A CN117453572 A CN 117453572A CN 202311634670 A CN202311634670 A CN 202311634670A CN 117453572 A CN117453572 A CN 117453572A
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business
regression
transaction
current candidate
parameter
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金宝珠
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Agricultural Bank of China
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Agricultural Bank of China
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Priority to CN202311634670.4A priority Critical patent/CN117453572A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • G06F11/3672—Test management

Abstract

The application discloses a regression test item determining method, device, equipment and storage medium, which relate to the technical field of computers. The method comprises the following steps: obtaining each candidate test item of a transaction system to be tested; for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item; and determining at least one regression test item from the candidate test items based on the regression test parameters corresponding to the candidate test items.

Description

Regression test item determination method, device, equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining regression test items.
Background
Currently, in the process of performing project development on an online transaction system of a bank, when a tester iterates to update a service function (newly adding the service function or updating an original service function) of the system, a regression testing range of the iteration (namely, determining which part of code data needs to be tested) is determined according to the influence of the service function updated in the iteration on other service functions, and then, performing regression testing on the online transaction system according to the determined regression testing range.
In the prior art, the manner of determining the regression testing range of an online transaction system is generally: and the testers analyze the business functions possibly influenced by the iteration updating according to development experience, and then determine the final regression testing range by combining the testing results of the online transaction system in the historical iteration process.
However, the above conventional method for determining the regression testing range has low determination efficiency, and is affected by subjective factors, and the accuracy of the determined regression testing range is not high.
Disclosure of Invention
The application provides a regression test item determining method, device, equipment and storage medium, which can improve the efficiency of determining a regression test range and improve the accuracy of the determined regression test range.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a method for determining a regression test term, including: obtaining each candidate test item of a transaction system to be tested; for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item; and determining at least one regression test item from the candidate test items based on the regression test parameters corresponding to the candidate test items.
In the technical scheme provided by the application, when the regression testing range aiming at the transaction system to be tested needs to be determined, each candidate test item of the transaction system to be tested can be acquired first, one candidate test item can correspond to one transaction service of the transaction system to be tested, and service functions of different transaction services are different; then, for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item; at least one regression test term may then be determined from each candidate test term based on the regression test parameters respectively corresponding to each candidate test term. The business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item can be used for representing the probability that the operation abnormal condition of the transaction system to be tested possibly occurs when the transaction business corresponding to the current candidate test item is operated (the higher the business risk is, the higher the probability that the operation abnormal condition occurs is, and the higher the business complexity is, the higher the probability that the operation abnormal condition occurs is, and the higher the probability that the operation abnormal condition occurs is, which means that the iteration process has a greater influence on the transaction business corresponding to the current candidate test item. Therefore, based on the regression testing parameters determined by the business risk parameters and the business complexity parameters, the possibility that the iterative process affects the transaction business corresponding to the current candidate testing item can be accurately evaluated. Then, based on the regression testing parameters, at least one regression testing term can be accurately and quickly determined (i.e. the regression testing range is determined). According to the method and the device, the regression testing parameters of the candidate testing items are evaluated through two dimensions of the business complexity and the business risk, the regression testing range of the transaction system to be tested is determined according to the evaluation result, the efficiency of determining the regression testing range can be improved, and the accuracy of the determined regression testing range is improved.
Optionally, after obtaining each candidate test item of the transaction system to be tested, the method for determining the regression test item provided by the application further includes:
for each candidate test item, determining a business risk parameter of the business corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the business corresponding to the current candidate test item.
Optionally, after obtaining each candidate test item of the transaction system to be tested, the method for determining the regression test item provided by the application further includes:
and for each candidate test item, determining the service complexity parameter of the transaction service corresponding to the current candidate test item based on the service importance parameter and the system performance requirement parameter of the transaction service corresponding to the current candidate test item.
Optionally, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes:
and determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the test cost parameters corresponding to the current candidate test items.
Optionally, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the business influence parameters of the transaction business corresponding to the current candidate test items; the business influence degree parameter of the transaction business corresponding to the current candidate test item is used for representing the association degree of the transaction business corresponding to the current candidate test item and the transaction business corresponding to other candidate test items.
Optionally, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the parameter weights of the business risk parameters, the business complexity parameters of the transaction business corresponding to the current candidate test items and the parameter weights of the business complexity parameters.
Optionally, determining at least one regression test item from the candidate test items based on the regression test parameters respectively corresponding to the candidate test items includes:
for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on regression test parameters corresponding to the current candidate test item and a preset mapping relation list; the preset mapping relation list is used for representing the corresponding relation between the parameter value of the regression testing parameter and the regression testing grade;
and determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
In a second aspect, the present application provides a device for determining regression test items, including: an acquisition module and a determination module;
the acquisition module is used for acquiring each candidate test item of the transaction system to be tested;
the determining module is used for determining regression testing parameters corresponding to the current candidate testing items based on the business risk parameters of the transaction business corresponding to the current candidate testing items and the business complexity parameters of the transaction business corresponding to the current candidate testing items for each candidate testing item;
the determining module is further configured to determine at least one regression test item from the candidate test items based on the regression test parameters corresponding to the candidate test items.
Optionally, the determining module is further configured to: after the acquisition module acquires each candidate test item of the transaction system to be tested, for each candidate test item, determining a transaction risk parameter of a transaction service corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the transaction service corresponding to the current candidate test item.
Optionally, the determining module is further configured to: after the acquisition module acquires each candidate test item of the transaction system to be tested, for each candidate test item, determining the service complexity parameter of the transaction service corresponding to the current candidate test item based on the service importance parameter of the transaction service corresponding to the current candidate test item and the system performance requirement parameter.
Optionally, the determining module is specifically configured to:
and determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the test cost parameters corresponding to the current candidate test items.
Optionally, the determining module is further specifically configured to:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the business influence parameters of the transaction business corresponding to the current candidate test items; the business influence degree parameter of the transaction business corresponding to the current candidate test item is used for representing the association degree of the transaction business corresponding to the current candidate test item and the transaction business corresponding to other candidate test items.
Optionally, the determining module is further specifically configured to:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the parameter weights of the business risk parameters, the business complexity parameters of the transaction business corresponding to the current candidate test items and the parameter weights of the business complexity parameters.
Optionally, the determining module is further specifically configured to:
for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on regression test parameters corresponding to the current candidate test item and a preset mapping relation list; the preset mapping relation list is used for representing the corresponding relation between the parameter value of the regression testing parameter and the regression testing grade;
and determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
In a third aspect, the present application provides a regression testing item determination apparatus, including a memory, a processor, a bus, and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the regression test item determination device is running, the processor executes computer-executable instructions stored in the memory to cause the regression test item determination device to perform the regression test item determination method as provided in the first aspect above.
In a fourth aspect, the present application provides a computer-readable storage medium having instructions stored therein, which when executed by a computer, cause the computer to perform the method of determining regression test terms as provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method of determining regression test terms as provided in the first aspect.
It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer readable storage medium may be packaged together with the processor of the regression testing apparatus or may be packaged separately from the processor of the regression testing apparatus, which is not limited in this application.
The description of the second, third, fourth and fifth aspects of the present application may refer to the detailed description of the first aspect; further, the advantageous effects described in the second aspect, the third aspect, the fourth aspect, and the fifth aspect may refer to the advantageous effect analysis of the first aspect, and are not described herein.
In the present application, the names of the above-mentioned devices or functional modules are not limited, and in actual implementation, these devices or functional modules may appear under other names. Insofar as the function of each device or function module is similar to the present application, it is within the scope of the present application and the equivalents thereof.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
FIG. 1 is a flow chart of a method for determining regression testing terms according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for determining regression testing terms according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for determining regression testing terms according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a regression testing term determining device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a regression testing term determining device according to an embodiment of the present application.
Detailed Description
The method, the device, the equipment and the storage medium for determining the regression test item provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.
In addition, the technical scheme of the application is used for acquiring, storing, using, processing and the like data, and the data are in accordance with relevant regulations of national laws and regulations.
In the prior art, the manner of determining the regression testing range of an online transaction system is generally: and the testers analyze the business functions possibly influenced by the iteration updating according to development experience, and then determine the final regression testing range by combining the testing results of the online transaction system in the historical iteration process. However, the above conventional method for determining the regression testing range has low determination efficiency, and is affected by subjective factors, and the accuracy of the determined regression testing range is not high.
Aiming at the problems in the prior art, the embodiment of the application provides a regression test item determining method, which evaluates regression test parameters of candidate test items from two dimensions of business complexity and business risk, determines a regression test range of a transaction system to be tested according to an evaluation result, and can improve the efficiency of determining the regression test range and improve the accuracy of the determined regression test range.
The method for determining the regression test item provided by the embodiment of the application can be executed by the device for determining the regression test item provided by the embodiment of the application, and the device can be realized in a software and/or hardware mode and is integrated in the device for determining the regression test item for executing the method.
The method for determining regression test items provided in the present application is described below with reference to the accompanying drawings.
Referring to fig. 1, the method for determining regression testing terms provided in the embodiment of the present application includes S101-S103:
s101, obtaining each candidate test item of the transaction system to be tested.
One candidate test item can correspond to one transaction service of the transaction system to be tested, and the service functions of different transaction services are different. For example, each candidate test item of the to-be-tested transaction system may include candidate test items corresponding to different types of transaction services, such as a query service (e.g., account balance query service), a payment service (e.g., transfer payment service), a content recommendation service (e.g., financial product recommendation service), and the like.
In one possible implementation manner, when a tester needs to perform a regression test on a to-be-tested transaction system, each candidate test item of the to-be-tested transaction system may be uploaded in a client, and the client may send each candidate test item of the to-be-tested transaction system to a server based on an uploading operation of a user (the determining device of the regression test item provided in the embodiment of the present application may be configured at the server). Illustratively, each candidate test item may be code data of a corresponding transaction service.
S102, for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item.
In one possible implementation manner, the tester may upload the regression test related configuration information corresponding to each candidate test item of the transaction system to be tested at the same time as uploading each candidate test item of the transaction system to be tested, and then the client may send each candidate test item of the transaction system to be tested and the regression test related configuration information corresponding to each candidate test item to the server based on the uploading operation of the user. After receiving the regression test related configuration information corresponding to each candidate test item, the server side can respectively determine the business risk parameters of the transaction business corresponding to each candidate test item and the business complexity parameters of the transaction business corresponding to each candidate test item according to the regression test related configuration information corresponding to each candidate test item and the predetermined data processing rule aiming at the regression test related configuration information.
The regression test related configuration information records information such as historical service data, service function configuration and the like of the transaction service corresponding to the candidate test item, the information can represent the service complexity of the transaction service corresponding to the candidate test item and the service risk degree, and the evaluation indexes (namely the service risk parameter and the service complexity parameter) of the transaction service corresponding to the candidate test item in two dimensions of the service complexity and the service risk can be obtained by processing the information through a predetermined data processing rule.
The business risk parameters of the transaction business corresponding to the current candidate test item and the business complexity parameters of the transaction business corresponding to the current candidate test item can be used for representing the probability that the to-be-tested transaction system possibly has abnormal operation conditions when the transaction business corresponding to the current candidate test item is operated. Specifically, the higher the business risk, the higher the probability of occurrence of an abnormal operation condition, and the higher the business complexity, the higher the probability of occurrence of an abnormal operation condition, whereas the lower the business risk, the lower the probability of occurrence of an abnormal operation condition, and the lower the business complexity, the lower the probability of occurrence of an abnormal operation condition. In addition, the higher the probability of occurrence of the abnormal operation condition is, the greater the possibility that the iterative process affects the transaction service corresponding to the current candidate test item is (on the contrary, the lower the probability of occurrence of the abnormal operation condition is, the lower the possibility that the iterative process affects the transaction service corresponding to the current candidate test item is). Therefore, after determining the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item, the embodiment of the application can integrate the business risk parameter and the business complexity parameter to obtain the index (i.e. regression test parameter) for evaluating the possibility that the iteration process affects the transaction business corresponding to the current candidate test item.
In one possible implementation manner, since the influence directions of the business risk parameter and the business complexity parameter on the regression testing parameter are consistent, the parameter values of the business risk parameter and the business complexity parameter can be directly added and calculated to obtain the regression testing parameter.
Optionally, in another possible implementation manner, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes: determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the parameter weights of the business risk parameters, the business complexity parameters of the transaction business corresponding to the current candidate test items and the parameter weights of the business complexity parameters.
The parameter weight of the business risk parameter and the parameter weight of the business complexity parameter may be predetermined values by a tester, and are used for representing the influence degree of the business risk parameter and the business complexity parameter on the running condition of the transaction system to be tested. In one possible implementation, the parameter weight of the business risk parameter is smaller than the parameter weight of the business complexity parameter, for example, the parameter weight of the business risk parameter is 0.4, and the parameter weight of the business complexity parameter is 0.6. Regression testing parameters = 0.4 traffic risk parameters +0.6 traffic complexity parameters.
Because the influence degrees of the business risk parameter and the business complexity parameter on the running condition of the transaction system to be tested are different, in the embodiment of the application, in order to further improve the accuracy of the determined regression testing range, the business risk parameter and the business complexity parameter can be weighted based on the parameter weight so as to determine the regression testing parameter.
Optionally, after obtaining each candidate test item of the transaction system to be tested, the method for determining the regression test item provided in the embodiment of the present application further includes: for each candidate test item, determining a business risk parameter of the business corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the business corresponding to the current candidate test item.
The transaction account moving parameter is used for representing whether transaction business corresponding to the current candidate test item triggers account moving or not. The parameter values of the transaction account moving parameter include a parameter value A1 and a parameter value A2, where the parameter value A1 indicates that the transaction service corresponding to the current candidate test item will trigger the account moving, and the parameter value A2 indicates that the transaction service corresponding to the current candidate test item will not trigger the account moving. For example, the parameter A1 is 100, and the parameter A2 is 50.
The historical transaction amount parameter is used for representing the ordering of the transaction amount (such as the inquiry times, the transfer times and the like) corresponding to the current candidate test item in all transaction services. Illustratively, the parameter values of the historical transaction amount parameter include a parameter value B1, a parameter value B2, and a parameter value B3. The parameter value B1 indicates that the service amount of the transaction service corresponding to the current candidate test item is ranked as the first 10 in all transaction services, the parameter value B2 indicates that the service amount of the transaction service corresponding to the current candidate test item is ranked between 11 th and 20 th in all transaction services, and the parameter value B3 indicates that the service amount of the transaction service corresponding to the current candidate test item is ranked as the 21 st or 21 st and later in all transaction services. For example, the parameter B1 is 100, the parameter B2 is 90, and the parameter B3 is 50.
The user group category is used for representing whether the transaction service corresponding to the current candidate test item is a face passenger service or not. The parameter values of the user group category include a parameter value C1 and a parameter value C2, wherein the parameter value C1 indicates that the transaction service corresponding to the current candidate test item is a face-to-face service, and the parameter value C2 indicates that the transaction service corresponding to the current candidate test item is not a face-to-face service. For example, the parameter value C1 is 100, and the parameter value C2 is 50.
The historical operation state parameter is used for representing the historical failure times of the transaction system to be tested when the transaction service corresponding to the current candidate test item is operated. Illustratively, the parameter values of the historical operating state parameters include a parameter value D1, a parameter value D2, and a parameter value D3. Wherein the parameter value D1 indicates that the number of faults is greater than 3, the parameter value D2 indicates that the number of faults is between 1 and 3, and the parameter value D3 indicates that no fault has occurred. For example, the parameter D1 is 100, the parameter D2 is 90, and the parameter D3 is 50.
Optionally, in one possible implementation manner, since the transaction account moving parameter, the historical transaction amount parameter, the historical operation state parameter, and the user group category have different influence degrees on the business risk, in this embodiment of the present application, in order to improve the accuracy of the determined business risk parameter, the parameter values of the transaction account moving parameter, the historical transaction amount parameter, the historical operation state parameter, and the user group category may be weighted based on the parameter weight, so as to obtain the business risk parameter.
Exemplary, the transaction account moving parameters, the historical transaction amount parameters, the historical running state parameters and the parameter weights corresponding to the user group categories are respectively: 1. 0.8, 0.5, 0.9.
Optionally, after obtaining each candidate test item of the transaction system to be tested, the method for determining the regression test item provided in the embodiment of the present application further includes: and for each candidate test item, determining the service complexity parameter of the transaction service corresponding to the current candidate test item based on the service importance parameter and the system performance requirement parameter of the transaction service corresponding to the current candidate test item.
The service importance parameter is used for indicating whether the transaction service corresponding to the current candidate test item is a core service (for example, the transfer payment service is a core service, and the content recommendation service is not a core service). The parameter values of the service importance parameter include a parameter value E1 and a parameter value E2, where the parameter value E1 indicates that the transaction service corresponding to the current candidate test item is a core service, and the parameter value E2 indicates that the transaction service corresponding to the current candidate test item is not a core service. For example, the parameter value C1 is 100, and the parameter value C2 is 50.
The system performance requirement parameter can be used for representing the system performance requirement of the transaction service corresponding to the current candidate test item, and can be specifically determined according to concurrency number requirement information, response time requirement information, peak value per second transaction number and the like. Exemplary parameter values for the system performance requirement parameter include parameter value F1, parameter value F2, and parameter value F3. Wherein, the parameter value F1 represents that the requirement on the system performance is high, the parameter value F2 represents that the requirement on the system performance is general, and the parameter value F3 represents that the requirement on the system performance is low.
In the embodiment of the application, the service importance degree parameter and the system performance requirement parameter can be weighted based on the parameter weight to obtain the service complexity parameter.
Optionally, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes: and determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the test cost parameters corresponding to the current candidate test items.
The test cost parameter may be used to characterize the test cost required for regression testing of the current candidate test item, for example, the test cost may include test man-hour, test expense, and the like.
In order to improve the test efficiency and reduce the test cost on the basis of ensuring the accuracy of the test result, in the embodiment of the application, the regression test parameters can be determined by combining the test cost parameters. Illustratively, the parameter values of the test cost parameter include a parameter value G1, a parameter value G2, and a parameter value G3. The parameter value G1 represents higher test cost, the parameter value G2 represents medium test cost, and the parameter value G3 represents lower test cost.
Optionally, determining the regression testing parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item includes: determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the business influence parameters of the transaction business corresponding to the current candidate test items.
In order to determine regression testing parameters more accurately, the embodiment of the application can also determine the regression testing parameters by combining the business influence degree parameters. The business influence degree parameter of the transaction business corresponding to the current candidate test item is used for representing the association degree of the transaction business corresponding to the current candidate test item and the transaction business corresponding to other candidate test items.
The parameter values of the business impact parameters include a parameter value H1, a parameter value H2, and a parameter value H3. The parameter value H1 indicates that the association degree of the transaction service corresponding to the current candidate test item and the transaction service corresponding to the other candidate test items is relatively high, the parameter value H2 indicates that the association degree of the transaction service corresponding to the current candidate test item and the transaction service corresponding to the other candidate test items is intermediate, and the parameter value H3 indicates that the association degree of the transaction service corresponding to the current candidate test item and the transaction service corresponding to the other candidate test items is relatively low.
S103, determining at least one regression test item from the candidate test items based on regression test parameters respectively corresponding to the candidate test items.
Optionally, determining at least one regression test item from the candidate test items based on the regression test parameters respectively corresponding to the candidate test items includes: for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on regression test parameters corresponding to the current candidate test item and a preset mapping relation list; and determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
The preset mapping relationship list is used for representing the corresponding relationship between the parameter value of the regression testing parameter and the regression testing grade, and the preset screening rule can be a predetermined screening rule.
For example, if the parameter value of the regression testing parameter is smaller than the first preset value, the regression testing grade may be determined to be the first grade; if the parameter value of the regression testing parameter is larger than or equal to the first preset value and smaller than the second preset value, determining that the regression testing grade is the second grade; if the parameter value of the regression testing parameter is greater than or equal to the second preset value, the regression testing grade is determined to be the third grade. For example, the preset screening rule may be that if the regression testing grade corresponding to the current candidate testing item is the second grade or the third grade, the current candidate testing item is determined to be the regression testing item. Or, the preset screening rule may also be that if the regression testing grade corresponding to the current candidate testing item is the third grade, the current candidate testing item is determined to be the regression testing item. Wherein the first preset value and the second preset value may be predetermined values.
In view of the above, in the method for determining regression testing items provided in the embodiments of the present application, when a regression testing range for a to-be-tested transaction system needs to be determined, each candidate testing item of the to-be-tested transaction system may be obtained first, one candidate testing item may correspond to one transaction service of the to-be-tested transaction system, and service functions of different transaction services are different; then, for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item; at least one regression test term may then be determined from each candidate test term based on the regression test parameters respectively corresponding to each candidate test term. The business risk parameter of the transaction business corresponding to the current candidate test item and the business complexity parameter of the transaction business corresponding to the current candidate test item can be used for representing the probability that the operation abnormal condition of the transaction system to be tested possibly occurs when the transaction business corresponding to the current candidate test item is operated (the higher the business risk is, the higher the probability that the operation abnormal condition occurs is, and the higher the business complexity is, the higher the probability that the operation abnormal condition occurs is, and the higher the probability that the operation abnormal condition occurs is, which means that the iteration process has a greater influence on the transaction business corresponding to the current candidate test item. Therefore, based on the regression testing parameters determined by the business risk parameters and the business complexity parameters, the possibility that the iterative process affects the transaction business corresponding to the current candidate testing item can be accurately evaluated. Then, based on the regression testing parameters, at least one regression testing term can be accurately and quickly determined (i.e. the regression testing range is determined). It can be seen that, in the embodiment of the application, the regression testing parameters of each candidate test item are evaluated through two dimensions of the business complexity and the business risk, and the regression testing range of the transaction system to be tested is determined according to the evaluation result, so that the efficiency of determining the regression testing range can be improved, and the accuracy of the determined regression testing range can be improved.
Optionally, as shown in fig. 2, the embodiment of the present application further provides a method for determining a regression test item, including S201-S205:
s201, obtaining each candidate test item of the transaction system to be tested.
S202, for each candidate test item, determining a business risk parameter of a transaction business corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the transaction business corresponding to the current candidate test item; and determining the business complexity parameter of the transaction business corresponding to the current candidate test item based on the business importance parameter of the transaction business corresponding to the current candidate test item and the system performance requirement parameter.
S203, for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the business corresponding to the current candidate test item, the parameter weight of the business risk parameter, the business complexity parameter of the business corresponding to the current candidate test item, and the parameter weight of the business complexity parameter.
S204, for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on the regression test parameter corresponding to the current candidate test item and a preset mapping relation list.
S205, determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
Optionally, as shown in fig. 3, the embodiment of the present application further provides a method for determining a regression test item, including the following steps:
s301, obtaining each candidate test item of the transaction system to be tested.
S302, for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on the business risk parameter of the transaction business corresponding to the current candidate test item, the business complexity parameter of the transaction business corresponding to the current candidate test item, the test cost parameter corresponding to the current candidate test item and the business influence degree parameter of the transaction business corresponding to the current candidate test item.
S303, for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on the regression test parameter corresponding to the current candidate test item and a preset mapping relation list.
S304, determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
As shown in fig. 4, the embodiment of the present application further provides a device for determining a regression test item, where the device may include: the acquisition module 11 and the determination module 21.
Wherein the acquisition module 11 performs S101 in the above-described method embodiment, and the determination module 21 performs S102 and S103 in the above-described method embodiment.
Specifically, an obtaining module 11, configured to obtain each candidate test item of the transaction system to be tested; a determining module 21, configured to determine, for each candidate test item, a regression test parameter corresponding to the current candidate test item based on a business risk parameter of a transaction business corresponding to the current candidate test item and a business complexity parameter of a transaction business corresponding to the current candidate test item; the determining module 21 is further configured to determine at least one regression test item from the candidate test items based on the regression test parameters corresponding to the candidate test items.
Optionally, the determining module 21 is further configured to: after the obtaining module 11 obtains each candidate test item of the to-be-tested transaction system, for each candidate test item, determining a transaction risk parameter of a transaction service corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the transaction service corresponding to the current candidate test item.
Optionally, the determining module 21 is further configured to: after the obtaining module 11 obtains each candidate test item of the transaction system to be tested, for each candidate test item, determining a service complexity parameter of the transaction service corresponding to the current candidate test item based on the service importance parameter and the system performance requirement parameter of the transaction service corresponding to the current candidate test item.
Optionally, the determining module 21 is specifically configured to:
and determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the test cost parameters corresponding to the current candidate test items.
Optionally, the determining module 21 is further specifically configured to:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the business influence parameters of the transaction business corresponding to the current candidate test items; the business influence degree parameter of the transaction business corresponding to the current candidate test item is used for representing the association degree of the transaction business corresponding to the current candidate test item and the transaction business corresponding to other candidate test items.
Optionally, the determining module 21 is further specifically configured to:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the parameter weights of the business risk parameters, the business complexity parameters of the transaction business corresponding to the current candidate test items and the parameter weights of the business complexity parameters.
Optionally, the determining module 21 is further specifically configured to:
for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on regression test parameters corresponding to the current candidate test item and a preset mapping relation list; the preset mapping relation list is used for representing the corresponding relation between the parameter value of the regression testing parameter and the regression testing grade;
and determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
Optionally, the determining device of the regression testing term may further include a storage module, where the storage module is configured to store program codes of the determining device of the regression testing term, and the like.
As shown in FIG. 5, embodiments of the present application also provide a regression test item determination apparatus including a memory 41, a processor (such as 42-1 and 42-2 in FIG. 5), a bus 43, and a communication interface 44; the memory 41 is used for storing computer-executed instructions, and the processor is connected with the memory 41 through the bus 43; when the determination device of regression test items is operated, the processor executes computer-executable instructions stored in the memory 41 to cause the determination device of regression test items to execute the determination method of regression test items as provided in the above-described embodiment.
In a particular implementation, the processor may include, as one embodiment, one or more central processing units (central processing unit, CPU), such as CPU0 and CPU1 shown in fig. 5. And as one example, the regression test term determination device may include a plurality of processors, such as processor 42-1 and processor 42-2 shown in fig. 5. Each of these processors may be a single-Core Processor (CPU) or a multi-core processor (multi-CPU). A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be stand alone and be connected to the processor via a bus 43. The memory 41 may also be integrated with the processor.
In a specific implementation, the memory 41 is used for storing data in the application and computer-executable instructions corresponding to executing a software program of the application. The processor may determine the various functions of the device by running or executing a software program stored in memory 41 and invoking data stored in memory 41 to regress the test items.
Communication interface 44, using any transceiver-like device, is used to communicate with other devices or communication networks, such as a control system, a radio access network (radio access network, RAN), a wireless local area network (wireless local area networks, WLAN), etc. The communication interface 44 may include a receiving unit to implement a receiving function and a transmitting unit to implement a transmitting function.
Bus 43 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus 43 may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
As an example, in connection with fig. 4, the function implemented by the acquisition module in the regression test item determination apparatus is the same as the function implemented by the receiving unit in fig. 5, and the function implemented by the determination module in the regression test item determination apparatus is the same as the function implemented by the processor in fig. 5. When the determining device of the regression test item includes a memory module, the function implemented by the memory module is the same as that implemented by the memory in fig. 5.
The explanation of the related content in this embodiment may refer to the above method embodiment, and will not be repeated here.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein again.
The embodiment of the application also provides a computer readable storage medium, wherein instructions are stored in the computer readable storage medium, and when the computer executes the instructions, the computer is caused to execute the regression test item determination method provided by the embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (erasable programmable read only memory, EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (application specific integrated circuit, ASIC). In the context of the present application, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method of determining regression testing terms, comprising:
obtaining each candidate test item of a transaction system to be tested;
for each candidate test item, determining a regression test parameter corresponding to the current candidate test item based on a business risk parameter of a transaction business corresponding to the current candidate test item and a business complexity parameter of the transaction business corresponding to the current candidate test item;
and determining at least one regression test item from the candidate test items based on regression test parameters respectively corresponding to the candidate test items.
2. The method for determining regression testing terms of claim 1, further comprising, after the obtaining each candidate testing term for the transaction system under test:
and for each candidate test item, determining a business risk parameter of the transaction business corresponding to the current candidate test item based on at least one of a transaction account parameter, a historical transaction amount parameter, a historical running state parameter and a user group category of the transaction business corresponding to the current candidate test item.
3. The method for determining regression testing terms of claim 1, further comprising, after the obtaining each candidate testing term for the transaction system under test:
and for each candidate test item, determining the service complexity parameter of the transaction service corresponding to the current candidate test item based on the service importance parameter and the system performance requirement parameter of the transaction service corresponding to the current candidate test item.
4. The method for determining regression testing terms according to claim 1, wherein the determining the regression testing parameters corresponding to the current candidate testing terms based on the business risk parameters of the transaction business corresponding to the current candidate testing terms and the business complexity parameters of the transaction business corresponding to the current candidate testing terms includes:
and determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the test cost parameters corresponding to the current candidate test items.
5. The method for determining regression testing terms according to claim 1, wherein the determining the regression testing parameters corresponding to the current candidate testing terms based on the business risk parameters of the transaction business corresponding to the current candidate testing terms and the business complexity parameters of the transaction business corresponding to the current candidate testing terms includes:
Determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the business complexity parameters of the transaction business corresponding to the current candidate test items and the business influence parameters of the transaction business corresponding to the current candidate test items; the business influence degree parameter of the transaction business corresponding to the current candidate test item is used for representing the association degree of the transaction business corresponding to the current candidate test item and the transaction business corresponding to other candidate test items.
6. The method for determining regression testing terms according to claim 1, wherein the determining the regression testing parameters corresponding to the current candidate testing terms based on the business risk parameters of the transaction business corresponding to the current candidate testing terms and the business complexity parameters of the transaction business corresponding to the current candidate testing terms includes:
determining regression test parameters corresponding to the current candidate test items based on the business risk parameters of the transaction business corresponding to the current candidate test items, the parameter weights of the business risk parameters, the business complexity parameters of the transaction business corresponding to the current candidate test items and the parameter weights of the business complexity parameters.
7. The method for determining regression testing terms according to any one of claims 1 to 6, wherein the determining at least one regression testing term from the candidate testing terms based on the regression testing parameters respectively corresponding to the candidate testing terms includes:
for each candidate test item, determining a regression test grade corresponding to the current candidate test item based on regression test parameters corresponding to the current candidate test item and a preset mapping relation list; the preset mapping relation list is used for representing the corresponding relation between the parameter value of the regression testing parameter and the regression testing grade;
and determining at least one regression test item from the candidate test items based on a preset screening rule and regression test grades corresponding to the candidate test items respectively.
8. A regression test item determination apparatus, comprising:
the acquisition module is used for acquiring each candidate test item of the transaction system to be tested;
the determining module is used for determining regression testing parameters corresponding to the current candidate testing items based on the business risk parameters of the transaction business corresponding to the current candidate testing items and the business complexity parameters of the transaction business corresponding to the current candidate testing items for each candidate testing item;
The determining module is further configured to determine at least one regression test item from the candidate test items based on regression test parameters corresponding to the candidate test items.
9. The regression test item determining device is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the regression test item determination device is running, a processor executes the computer-executable instructions stored in the memory to cause the regression test item determination device to perform the regression test item determination method of any one of claims 1-7.
10. A computer readable storage medium having instructions stored therein, which when executed by a computer, cause the computer to perform the method of determining regression test terms of any one of claims 1-7.
CN202311634670.4A 2023-12-01 2023-12-01 Regression test item determination method, device, equipment and storage medium Pending CN117453572A (en)

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Application Number Priority Date Filing Date Title
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Application Number Priority Date Filing Date Title
CN202311634670.4A CN117453572A (en) 2023-12-01 2023-12-01 Regression test item determination method, device, equipment and storage medium

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