Functional testing method and device for banking software system

Abstract

The present invention discloses a function testing method and device for a banking software system, and relates to the technical field of computer data processing. The method comprises: for each transaction attribute, determining a first data volume of the transaction attribute and a corresponding value in transaction input data of an existing function, and a second data volume of the transaction attribute and a corresponding value associated with abnormal transaction output data; determining the abnormal transaction attribute based on the second data volume and the first data volume, forming an abnormal feature vector space for clustering, and using the clustering result to divide multiple existing functions into function sets to generate corresponding test cases; when a new function has multiple abnormal transaction attributes, determining the function set with the largest second data volume containing each abnormal transaction attribute and a corresponding value as the standard function set; after determining weights in descending order according to the second data volume of the abnormal transaction attributes of the new function corresponding to each standard function set, selecting test cases based on a preset number to test the new function, thereby reducing the error rate.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a function testing method and device for a banking software system. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] In banking systems, new functions will lead to contextual changes in the entire system's functions. Current manual testing methods cannot fully predict all the complex data in the production environment. In other words, the testers' work experience cannot meet the testing requirements of actual production. This can easily lead to the inability to cope with abnormal situations in the actual production environment after the new functions are launched, affecting the user experience and possibly causing losses to the bank.

[0004] To address the above issues, no effective solutions have been proposed so far. Summary of the Invention

[0005] An embodiment of the present invention provides a method for functional testing of a banking software system, which utilizes transaction data of existing functions to test new functions, thereby reducing the error rate when the new functions are put into production and improving user experience. The method includes:

[0006] For each existing function in the banking software system, obtain transaction data of the existing function, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values;

[0007] For each transaction attribute, determining a first data volume of the transaction attribute and a corresponding transaction attribute value in transaction input data of the existing function, and a second data volume of the transaction attribute and a corresponding transaction attribute value associated with abnormal transaction output data;

[0008] determining a transaction attribute in which the ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute;

[0009] An abnormal feature vector space of an existing function is formed using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data amount of the abnormal transaction attribute and the corresponding transaction attribute value;

[0010] Clustering abnormal feature vectors of existing functions, using the clustering results of the abnormal feature vectors of existing functions to divide multiple existing functions into first-category function sets, thereby obtaining multiple first-category function sets; generating test cases corresponding to each function set based on the transaction input data and transaction output data of the existing functions in each function set;

[0011] When the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, determine the function set with the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value as the first standard function set; for each first standard function set, determine the weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order;

[0012] According to the weight of each first standard function set and the pre-set number of test case selections, the number of test case selections corresponding to each first standard function set is determined; according to the number of test case selections corresponding to each first standard function set, multiple test cases are selected to test the new functions.

[0013] An embodiment of the present invention further provides a functional testing device for a banking software system, which utilizes transaction data of existing functions to test new functions, thereby reducing the error rate when the new functions are put into production and improving user experience. The device includes:

[0014] An acquisition module is used to acquire transaction data of each existing function in the banking software system, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values;

[0015] a data volume determination module for determining, for each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in transaction input data of an existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with abnormal transaction output data;

[0016] an abnormal transaction attribute determination module, configured to determine a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute;

[0017] An abnormal feature vector space construction module is used to construct an abnormal feature vector space of an existing function using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data amount of the abnormal transaction attribute and the corresponding transaction attribute value;

[0018] A partitioning module is configured to cluster abnormal feature vectors of existing functions, and use the clustering results of the abnormal feature vectors of existing functions to partition multiple existing functions into first-category function sets to obtain multiple first-category function sets; and generate test cases corresponding to each function set based on the transaction input data and transaction output data of the existing functions in each function set;

[0019] A weight determination module is configured to, when the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, determine, for each abnormal transaction attribute included in the newly added function, a function set having the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value as a first standard function set; and for each first standard function set, determine a weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order.

[0020] The testing module is used to determine the number of test cases corresponding to each first standard function set based on the weight of each first standard function set and the pre-set number of test cases; and select multiple test cases to test the new functions based on the number of test cases corresponding to each first standard function set.

[0021] An embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the functional testing method of the banking software system when executing the computer program.

[0022] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the functional testing method of the banking software system is implemented.

[0023] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the functional testing method of the banking software system.

[0024] Different from the method of testing new functions based on the work experience of testers in the prior art, in an embodiment of the present invention, for each existing function in the banking software system, transaction data of the existing function is obtained, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values; for each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in the transaction input data of the existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with the abnormal transaction output data are determined; a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold is determined as an abnormal transaction attribute; an abnormal feature vector space of the existing function is constructed using the abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value; the abnormal feature vectors of the existing function are clustered, and the clustering results of the abnormal feature vectors of the existing function are used to divide the multiple existing functions into a first category function set to obtain multiple The first type of function set; based on the transaction input data and transaction output data of the existing functions in each function set, generate test cases corresponding to the function set; when the transaction attributes of the newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, determine the function set with the largest second data volume containing the abnormal transaction attribute and the corresponding transaction attribute value as the first standard function set; for each first standard function set, determine the weight of each first standard function set according to its corresponding abnormal transaction attribute of the newly added function: the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value, in descending order; determine the number of test cases corresponding to each first standard function set according to the weight of each first standard function set and the pre-set number of test cases; select multiple test cases according to the number of test cases corresponding to each first standard function set to test the newly added function, and use the transaction data of the existing function to test the new function, thereby reducing the error rate when the new function is put into production and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0026] Figure 1 1 is a process flow chart of a functional testing method for a banking software system according to an embodiment of the present invention;

[0027] Figure 2This is a schematic diagram of a specific example of a method for determining abnormal transaction attributes in an embodiment of the present invention;

[0028] Figure 3 Schematic diagram of the structure of a functional testing device for a software system according to an embodiment of the present invention;

[0029] Figure 4 A schematic structural diagram of a specific example of a functional testing device for a software system according to an embodiment of the present invention;

[0030] Figure 5 This is a schematic structural diagram of a specific example of a functional testing device for a banking software system according to an embodiment of the present invention;

[0031] Figure 6 FIG. 1 is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0033] The inventors discovered that in banking software systems, similar functions often require similar data and information. For example, product and branch recommendation functions both require user-related information, such as identity information and mobile phone numbers. When a new function is added, the historical transaction data of the existing function can be used to test the new function. Furthermore, if a branch recommendation function is already present in the banking system, the actual transaction data from the product recommendation function can be used to test the branch recommendation function. In other words, the historical transaction data of the existing function can be reused to simulate a complex production environment for testing the new function.

[0034] Figure 1 FIG. 1 is a process flow chart of a functional testing method for a banking software system according to an embodiment of the present invention. Figure 1 As shown, the functional testing method of the banking software system in the embodiment of the present invention may include:

[0035] Step 101: For each existing function in the banking software system, obtain transaction data of the existing function, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values;

[0036] Step 102: For each transaction attribute, determine a first data volume of the transaction attribute and the corresponding transaction attribute value in the transaction input data of the existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with the abnormal transaction output data;

[0037] Step 103: Determine the transaction attribute in which the ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute;

[0038] Step 104: construct an abnormal feature vector space of the existing function using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data volume of the abnormal transaction attribute and the corresponding transaction attribute value;

[0039] Step 105: Cluster the abnormal feature vectors of the existing functions. Using the clustering results of the abnormal feature vectors of the existing functions, divide the multiple existing functions into first-category function sets to obtain multiple first-category function sets. Generate test cases corresponding to each function set based on the transaction input data and transaction output data of the existing functions in each function set.

[0040] Step 106: When the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, determine the function set with the largest second data volume containing the abnormal transaction attribute and the corresponding transaction attribute value as the first standard function set; for each first standard function set, determine the weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order;

[0041] Step 107: Determine the number of test cases corresponding to each first standard function set based on the weight of each first standard function set and the preset number of test cases; select multiple test cases to test the newly added functions based on the number of test cases corresponding to each first standard function set.

[0042] In order to perform functional testing on different transactions of the banking software system, the transaction data of each existing function in the banking software system can be obtained first, where the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values.

[0043] In one embodiment, the transaction attributes include at least one of the following categories:

[0044] Transaction time, transaction location, transaction type, transaction amount, transaction counterparty, terminal number, and core transaction code.

[0045] For each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in the transaction input data of the existing function, as well as a second data volume of the transaction attribute and the corresponding transaction attribute value associated with the abnormal transaction output data can be determined; a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold is determined as an abnormal transaction attribute.

[0046] Figure 2 This is a schematic diagram of a specific example of the first method for determining abnormal transaction attributes in an embodiment of the present invention. Figure 2 As shown, take the "account" in the transaction as an example:

[0047] Step 201: Determine the data volume of "account" as input data as the first data volume among all input data of the existing function;

[0048] Step 202: Determine all input data corresponding to each abnormal value in the existing function as abnormal input data, and determine the data volume of "account" in the abnormal input data as the second data volume;

[0049] Step 203: The ratio of the second data volume to the first data volume is used as the probability that the "account" causes abnormal transaction output. When the probability that the "account" causes abnormal transaction output exceeds a threshold, the "account" is determined to be an abnormal transaction attribute.

[0050] After determining all abnormal transaction attributes in the existing functions through the above steps, the abnormal transaction attributes can be used to construct an abnormal feature vector space of the existing functions, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is the second data amount of the abnormal transaction attribute and the corresponding transaction attribute value; then, the abnormal feature vectors of the existing functions are clustered, and the clustering results of the abnormal feature vectors of the existing functions are used to divide the multiple existing functions into first-category function sets to obtain multiple first-category function sets. Next, test cases corresponding to the function sets can be generated based on the transaction input data and transaction output data of the existing functions in each function set.

[0051] In a specific embodiment, abnormal feature vectors of existing functions are clustered, and the clustering results of the abnormal feature vectors of existing functions are used to divide multiple existing functions into first-category function sets to obtain multiple first-category function sets. The specific process may be:

[0052] 1. Select K cluster centers from the abnormal feature vectors of the existing functions. Each cluster center corresponds to a first-class feature set. The initial elements of the first-class feature set only contain the existing functions corresponding to the abnormal feature vectors corresponding to the cluster centers.

[0053] 2. For each abnormal feature vector, select multiple cluster centers from all cluster centers that are consistent with the main abnormal category of the existing function corresponding to the abnormal feature vector; calculate the distance between each selected cluster center and the abnormal feature vector, and select the minimum value among the multiple distances as the first distance of the abnormal feature vector; calculate the distance between each unselected cluster center and the abnormal feature vector, and select the minimum value among the multiple distances as the second distance of the abnormal feature vector;

[0054] 3. For each abnormal feature vector, if the first distance of the abnormal feature vector is less than the second distance of the abnormal feature vector, the existing function corresponding to the abnormal feature vector is divided into the first category function set corresponding to the cluster center corresponding to the first distance; otherwise, a new cluster center is created, and the newly created cluster center corresponds to a new first category function set, and the initial elements of the new first category function set only include the existing function corresponding to the abnormal feature vector;

[0055] 4. After executing steps 2 and 3 above for all abnormal feature vectors, for each first-category feature set, update the cluster center corresponding to the first-category feature set to the mean of all abnormal feature vectors corresponding to the first-category feature set;

[0056] 5. Repeat steps 2, 3, and 4 above until the change in the cluster center is very small (the absolute value of the difference between the two values ​​of the cluster center before and after the update is less than the threshold), thereby obtaining multiple first-category feature sets.

[0057] For each existing function, the abnormal transaction attribute with the largest data volume can be determined as the main abnormal category of the existing function.

[0058] When the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, the function set with the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value can be determined as the first standard function set; for each first standard function set, the weight of each first standard function set is determined in descending order based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in its corresponding abnormal transaction attribute of the newly added function; then, the number of test cases corresponding to each first standard function set is determined based on the weight of each first standard function set and the pre-set number of test cases; and based on the number of test cases corresponding to each first standard function set, multiple test cases are selected to test the newly added function.

[0059] In specific implementation, after determining multiple first standard function sets, the weight of each first standard function set can be determined according to the following formula. Assuming that there are m first standard function sets, the weight of the i-th first standard function set can be set to:

[0060]

[0061] Among them, W i is the weight of the first standard function set of i, N i The second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the newly added function abnormal transaction attribute corresponding to the i-th first standard function set.

[0062] The following is an illustration using a specific example: assuming that the transaction attributes of the newly added function H include the abnormal transaction attributes of three existing functions: transaction time, transaction location, and transaction category; the above method can be used to determine three first standard function sets: first standard function set A, first standard function set B, and first standard function set C, where the abnormal transaction attribute of the newly added function corresponding to the first standard function set A is the transaction time, the abnormal transaction attribute of the newly added function corresponding to the first standard function set B is the transaction location, and the abnormal transaction attribute of the newly added function corresponding to the first standard function set C is the transaction category.

[0063] According to the above weight calculation formula, it is calculated respectively that: the weight of the first standard function set A is 30%, the weight of the first standard function set B is 20%, and the weight of the first standard function set C is 50%. If the preset number of test case selections is 10, it can be determined that the number of test case selections corresponding to the first standard function set A is 3, the number of test case selections corresponding to the first standard function set B is 2, and the number of test case selections corresponding to the first standard function set C is 5. Then, the newly added function H is tested based on the 10 selected test cases.

[0064] In the prior art, for each abnormal transaction attribute included in a new function, the new function is tested directly using the test case corresponding to the second largest function set containing the abnormal transaction attribute and the corresponding transaction attribute value. This requires a large number of tests, reduces the test efficiency, and cannot accurately grasp the key test direction of the new function. However, the present application introduces weights by using a reasonable calculation method, and conducts a comprehensive analysis of the multiple abnormal transaction attributes included in the new function. It can then conduct targeted testing on the parts of the new function that are prone to abnormal situations, thereby improving the test efficiency and reliability of the new function.

[0065] During specific implementation, the number of samples of abnormal transaction output data contained in the existing functional transaction data may be small, which is insufficient to obtain a sufficient number of test cases for the abnormal transaction attributes of the new function. To this end, multiple existing functions similar to the new function can be determined based on the transaction attributes, and then the union of the test case sets corresponding to the first-category function set to which each existing function belongs is used as the abnormal situation test case set for the new function. The new function is tested using the test cases in the abnormal situation test case set. The similarity of transaction attributes can be used to expand the scope of test cases for the abnormal transaction attributes of the new function, avoiding determining test cases based solely on the abnormal transaction attributes contained in the new function, resulting in a small number of test cases and failure to achieve testing results.

[0066] Based on transaction attributes, a general method for determining functional similarity may be: for any two functions, determine the attributes corresponding to the two functions, and then determine multiple attributes commonly included in the two functions to form a common attribute set corresponding to the two functions, and multiple attributes included in only one of the functions to form a unique attribute set corresponding to the two functions; the ratio obtained by the number of elements in the common attribute set / (the number of elements in the common attribute set + the number of elements in the unique attribute set) is used as the similarity between the two functions; in an embodiment of the present invention, a similarity threshold can be set in advance, and then the above method is used to select multiple existing functions whose similarity with the newly added function exceeds the threshold from the existing functions as multiple existing functions similar to the newly added function.

[0067] In one embodiment, the method may further include: forming a normal feature vector space of an existing function with normal transaction attributes other than abnormal transaction attributes, wherein each vector in the normal feature vector space corresponds to each normal transaction attribute, and the vector value is a third data amount of the normal transaction attribute and the corresponding transaction attribute value; clustering the normal feature vectors of the existing function, and using the clustering results of the normal feature vectors of the existing function to divide the multiple existing functions into second-category function sets to obtain multiple second-category function sets; generating test cases corresponding to the function set based on the transaction input data and transaction output data of the existing function in each function set; and including the normal transaction attributes of multiple existing functions in the transaction attributes of the newly added function. When the function is newly added, for each normal transaction attribute included in the new function, determine the function set with the largest third data volume including the normal transaction attribute and the corresponding transaction attribute value as the second standard function set; for each second standard function set, determine the weight of each second standard function set according to the third data volume of the normal transaction attribute and the corresponding transaction attribute value of the corresponding new function in descending order; determine the number of test cases selected corresponding to each second standard function set according to the weight of each second standard function set and the preset number of test cases selected; select multiple test cases according to the number of test cases selected corresponding to each second standard function set to test the new function.

[0068] The transaction input data and transaction output data of existing functions in the function set divided according to abnormal transaction attributes can generate test cases for abnormal transaction attributes, which is helpful to discover possible problems in the actual application of new functions before the new functions are put into use, and can timely adjust and optimize the new functions based on the problems discovered, thereby reducing the error rate when the new functions are put into production; however, in the specific implementation, since the transaction attributes of the new functions can include not only abnormal transaction attributes but also normal transaction attributes, in order to ensure that the new functions can be fully tested, the test method of the abnormal transaction attributes of the new functions can also be referred to, and the normal transaction attributes of the new functions can be tested. By combining the test results of abnormal transaction attributes and normal transaction attributes, the new functions can be further optimized and the effectiveness and comprehensiveness of the function testing can be improved.

[0069] In one embodiment, it may also include: when the transaction attributes of the newly added function do not include the abnormal transaction attributes of the existing function and the normal transaction attributes of the existing function, the transaction attributes of the newly added function are uploaded to the management end of the bank software system, and the management end generates a test case corresponding to the transaction attribute characteristics based on the transaction attribute characteristics of the newly added function.

[0070] In actual implementation, as banking software systems become increasingly complex, unprecedented new features will continue to emerge. These new features often have transaction attributes that differ radically from existing ones. Testing these new features based on test cases generated for existing features will not yield the desired test results, and various issues will likely arise after the new features are put into production. Therefore, the transaction attributes of these new features can be uploaded to the management side of the banking software system. Based on these transaction attribute characteristics, the management side can predict potential anomalies and generate test cases corresponding to these transaction attribute characteristics. This approach can combine automated and manual testing methods, improving the flexibility and effectiveness of functional testing.

[0071] The present invention also provides a functional testing device for a software system, as described in the following embodiments. Since the principle of solving the problem of the device is similar to the functional testing method of the software system, the implementation of the device can refer to the implementation of the functional testing method of the software system, and the repeated parts will not be repeated.

[0072] Figure 3 FIG. 1 is a schematic diagram of the structure of a functional testing device for a software system according to an embodiment of the present invention. Figure 3 As shown, the functional testing device of the software system in the embodiment of the present invention may specifically include:

[0073] An acquisition module 301 is configured to acquire transaction data of each existing function in the banking software system, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values;

[0074] A data volume determination module 302 is configured to determine, for each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in the transaction input data of the existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with the abnormal transaction output data;

[0075] The abnormal transaction attribute determination module 303 is configured to determine a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute;

[0076] An abnormal feature vector space forming module 304 is configured to form an abnormal feature vector space of an existing function using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data volume of the abnormal transaction attribute and the corresponding transaction attribute value;

[0077] The partitioning module 305 is configured to cluster the abnormal feature vectors of existing functions, and use the clustering results of the abnormal feature vectors of existing functions to partition the multiple existing functions into first-category function sets to obtain multiple first-category function sets; and generate test cases corresponding to each function set based on the transaction input data and transaction output data of the existing functions in each function set;

[0078] The weight determination module 306 is configured to, when the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, determine, for each abnormal transaction attribute included in the newly added function, the function set with the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value as the first standard function set; and for each first standard function set, determine the weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order.

[0079] The testing module 307 is used to determine the number of test cases corresponding to each first standard function set based on the weight of each first standard function set and the pre-set number of test cases; and select multiple test cases to test the new functions based on the number of test cases corresponding to each first standard function set.

[0080] In one embodiment, the transaction attributes include at least one of the following categories:

[0081] Transaction time, transaction location, transaction type, transaction amount, transaction counterparty, terminal number, and core transaction code.

[0082] Figure 4 FIG. 1 is a schematic diagram showing a specific example of a functional testing device for a software system according to an embodiment of the present invention. Figure 4 As shown, in the embodiment, Figure 3 The functional testing device of the software system shown also includes:

[0083] Normal transaction attribute testing module 401 is used to:

[0084] A normal feature vector space of the existing function is formed using normal transaction attributes other than abnormal transaction attributes, wherein each dimensional vector in the normal feature vector space corresponds to each normal transaction attribute, and the vector value is a third data quantity of the normal transaction attribute and the corresponding transaction attribute value;

[0085] Clustering normal feature vectors of existing functions, and using the clustering results of the normal feature vectors of existing functions to divide multiple existing functions into second-category function sets to obtain multiple second-category function sets; generating test cases corresponding to each function set based on the transaction input data and transaction output data of the existing functions in each function set;

[0086] When the transaction attributes of a newly added function include normal transaction attributes of multiple existing functions, for each normal transaction attribute included in the newly added function, determine the function set with the largest third data volume including the normal transaction attribute and the corresponding transaction attribute value as the second standard function set; for each second standard function set, determine the weight of each second standard function set based on the third data volume of the normal transaction attribute and the corresponding transaction attribute value of the corresponding new function normal transaction attribute, in descending order;

[0087] According to the weight of each second standard function set and the pre-set number of test case selections, the number of test case selections corresponding to each second standard function set is determined; according to the number of test case selections corresponding to each second standard function set, multiple test cases are selected to test the new functions.

[0088] Figure 5 FIG. 1 is a schematic diagram showing a specific example of a functional testing device for a banking software system according to an embodiment of the present invention. Figure 5 As shown, in the embodiment, Figure 3 The functional testing device of the banking software system shown also includes:

[0089] The upload and generation module 501 is used to:

[0090] When the transaction attributes of the newly added function do not include the abnormal transaction attributes and normal transaction attributes of the existing function, the transaction attributes of the newly added function will be uploaded to the management end of the bank software system, and the management end will generate test cases corresponding to the transaction attribute characteristics based on the transaction attribute characteristics of the newly added function.

[0091] Based on the above invention concept, Figure 6 As shown, the present invention also proposes a computer device 600, including a memory 610, a processor 620 and a computer program 630 stored in the memory 610 and executable on the processor 620, wherein the processor 620 implements the functional testing method of the aforementioned banking software system when executing the computer program 630.

[0092] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the functional testing method of the banking software system is implemented.

[0093] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the functional testing method of the banking software system.

[0094] In summary, in an embodiment of the present invention, for each existing function in a banking software system, transaction data of the existing function is obtained, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values; for each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in the transaction input data of the existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with the abnormal transaction output data are determined; a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold is determined as an abnormal transaction attribute; an abnormal feature vector space of the existing function is constructed using the abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value; the abnormal feature vectors of the existing function are clustered, and the clustering results of the abnormal feature vectors of the existing function are used to divide the multiple existing functions into first-category function sets to obtain multiple first-category function sets; according to each function The transaction input data and transaction output data of the existing functions in the function set can be used to generate test cases corresponding to the function set; when the transaction attributes of the newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, the function set with the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value is determined as the first standard function set; for each first standard function set, the weight of each first standard function set is determined according to the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attribute of the new function, in descending order; the number of test cases corresponding to each first standard function set is determined according to the weight of each first standard function set and the pre-set number of test cases; according to the number of test cases corresponding to each first standard function set, multiple test cases are selected to test the newly added function, and the transaction data of the existing function can be used to test the newly added function, thereby reducing the error rate when the new function is put into production and improving the user experience.

[0095] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0099] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A functional testing method for a banking software system, characterized in that: include: For each existing function in the banking software system, obtain transaction data of the existing function, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values; For each transaction attribute, determining a first data volume of the transaction attribute and a corresponding transaction attribute value in transaction input data of the existing function, and a second data volume of the transaction attribute and a corresponding transaction attribute value associated with abnormal transaction output data; determining a transaction attribute in which the ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute; An abnormal feature vector space of an existing function is formed using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data amount of the abnormal transaction attribute and the corresponding transaction attribute value; Clustering abnormal feature vectors of existing functions, using the clustering results of the abnormal feature vectors of existing functions to divide multiple existing functions into first-category function sets to obtain multiple first-category function sets; generating test cases corresponding to the first-category function sets based on the transaction input data and transaction output data of the existing functions in each first-category function set; When the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, for each abnormal transaction attribute included in the newly added function, determine the function set with the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value as the first standard function set; for each first standard function set, determine the weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order; According to the weight of each first standard function set and the pre-set number of test case selections, the number of test case selections corresponding to each first standard function set is determined; according to the number of test case selections corresponding to each first standard function set, multiple test cases are selected to test the new functions.

2. The method according to claim 1, wherein The transaction attributes include at least one of the following categories: Transaction time, transaction location, transaction type, transaction amount, transaction counterparty, terminal number, and core transaction code.

3. The method according to claim 1, wherein Also includes: A normal feature vector space of the existing function is formed using normal transaction attributes other than abnormal transaction attributes, wherein each vector in the normal feature vector space corresponds to each normal transaction attribute, and the vector value is a third data quantity of the normal transaction attribute and the corresponding transaction attribute value; Clustering normal feature vectors of existing functions, and using the clustering results of the normal feature vectors of existing functions to divide multiple existing functions into second-category function sets to obtain multiple second-category function sets; generating test cases corresponding to the second-category function sets based on the transaction input data and transaction output data of the existing functions in each second-category function set; When the transaction attributes of a newly added function include normal transaction attributes of multiple existing functions, for each normal transaction attribute included in the newly added function, determine the function set with the largest third data volume including the normal transaction attribute and the corresponding transaction attribute value as the second standard function set; for each second standard function set, determine the weight of each second standard function set based on the third data volume of the normal transaction attribute and the corresponding transaction attribute value of the corresponding new function normal transaction attribute, in descending order; According to the weight of each second standard function set and the pre-set number of test case selections, the number of test case selections corresponding to each second standard function set is determined; according to the number of test case selections corresponding to each second standard function set, multiple test cases are selected to test the new functions.

4. The method according to any one of claims 1 to 3, wherein Also includes: When the transaction attributes of the newly added function do not include the abnormal transaction attributes and normal transaction attributes of the existing function, the transaction attributes of the newly added function will be uploaded to the management end of the bank software system, and the management end will generate test cases corresponding to the transaction attribute characteristics based on the transaction attribute characteristics of the newly added function.

5. A functional testing device for a banking software system, characterized in that: include: An acquisition module is used to acquire transaction data of each existing function in the banking software system, wherein the transaction data includes transaction input data and transaction output data, and the transaction input data includes transaction attributes and corresponding transaction attribute values; a data volume determination module for determining, for each transaction attribute, a first data volume of the transaction attribute and the corresponding transaction attribute value in transaction input data of an existing function, and a second data volume of the transaction attribute and the corresponding transaction attribute value associated with abnormal transaction output data; an abnormal transaction attribute determination module, configured to determine a transaction attribute whose ratio of the second data volume to the first data volume exceeds a threshold as an abnormal transaction attribute; An abnormal feature vector space construction module is used to construct an abnormal feature vector space of an existing function using abnormal transaction attributes, wherein each vector in the abnormal feature vector space corresponds to each abnormal transaction attribute, and the vector value is a second data amount of the abnormal transaction attribute and the corresponding transaction attribute value; a partitioning module for clustering abnormal feature vectors of existing functions, and using the clustering results of the abnormal feature vectors of existing functions to partition multiple existing functions into first-category function sets to obtain multiple first-category function sets; and generating test cases corresponding to the first-category function sets based on the transaction input data and transaction output data of the existing functions in each first-category function set; A weight determination module is configured to, when the transaction attributes of a newly added function include abnormal transaction attributes of multiple existing functions, determine, for each abnormal transaction attribute included in the newly added function, a function set having the largest second data volume including the abnormal transaction attribute and the corresponding transaction attribute value as a first standard function set; and for each first standard function set, determine a weight of each first standard function set based on the second data volume of the abnormal transaction attribute and the corresponding transaction attribute value in the corresponding abnormal transaction attributes of the newly added function, in descending order. The testing module is used to determine the number of test cases corresponding to each first standard function set based on the weight of each first standard function set and the pre-set number of test cases; and select multiple test cases to test the new functions based on the number of test cases corresponding to each first standard function set.

6. The device according to claim 5, characterized in that The transaction attributes include at least one of the following categories: Transaction time, transaction location, transaction type, transaction amount, transaction counterparty, terminal number, and core transaction code.

7. The device according to claim 5, characterized in that Also included are normal transaction attribute testing modules for: A normal feature vector space of the existing function is formed using normal transaction attributes other than abnormal transaction attributes, wherein each dimensional vector in the normal feature vector space corresponds to each normal transaction attribute, and the vector value is a third data quantity of the normal transaction attribute and the corresponding transaction attribute value; Clustering normal feature vectors of existing functions, and using the clustering results of the normal feature vectors of existing functions to divide multiple existing functions into second-category function sets to obtain multiple second-category function sets; generating test cases corresponding to the second-category function sets based on the transaction input data and transaction output data of the existing functions in each second-category function set; When the transaction attributes of a newly added function include normal transaction attributes of multiple existing functions, for each normal transaction attribute included in the newly added function, determine the function set with the largest third data volume including the normal transaction attribute and the corresponding transaction attribute value as the second standard function set; for each second standard function set, determine the weight of each second standard function set based on the third data volume of the normal transaction attribute and the corresponding transaction attribute value of the corresponding new function normal transaction attribute, in descending order; According to the weight of each second standard function set and the pre-set number of test case selections, the number of test case selections corresponding to each second standard function set is determined; according to the number of test case selections corresponding to each second standard function set, multiple test cases are selected to test the new functions.

8. The device according to any one of claims 5 to 7, characterized in that Also includes upload and generate modules for: When the transaction attributes of the newly added function do not include the abnormal transaction attributes and normal transaction attributes of the existing function, the transaction attributes of the newly added function will be uploaded to the management end of the bank software system, and the management end will generate test cases corresponding to the transaction attribute characteristics based on the transaction attribute characteristics of the newly added function.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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