CN109165166B - High-simulation test method for financial product valuation and accounting system - Google Patents
High-simulation test method for financial product valuation and accounting system Download PDFInfo
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Abstract
The invention provides a high-simulation test method for a financial product valuation and accounting system, which comprises the following steps: test data preparation, baseline version estimation execution, version estimation execution to be tested, execution result comparison and difference positioning. The invention solves the current situations that the existing valuation system test has serious dependence on valuation and accounting business knowledge, insufficient business coverage rate and lower test execution efficiency, so that the valuation system has an efficient, real and reliable regression test mode, the defects of the system are effectively discovered, and the continuity of the valuation business is ensured.
Description
Technical Field
The invention relates to a method for carrying out valuation system testing based on high simulation data, in particular to a high simulation testing method for a financial product valuation and accounting system, which is applied to regression testing before valuation system upgrading of the securities industry.
Background
The product valuation is an indispensable link in the services of securities investment fund, collective financing product, collective trust product, special financing product, enterprise annuity product, social insurance fund product and the like, and the valuation system is also an important system of the main management bodies of the services of fund companies, securities companies, trust companies and the like. When the market has new service requirements, related operating organizations need to upgrade the valuation system and perform comprehensive regression testing on the system functions.
Because the valuation system comprises a large amount of accounting calculation and complex valuation logic, high requirements are put on the test of the valuation system, and the method comprises the following specific steps: (1) high simulation degree, diversified test scenes, high authenticity of test data and matching of test data magnitude with production; (2) service coverage is comprehensive-all services produced need to be covered as much as possible so as to test more comprehensively and effectively; (3) the method is easy to execute, can reduce the test execution difficulty and the dependence on business knowledge as much as possible, and shortens the execution period. However, the existing testing method is difficult to meet the above requirements, and how to design a regression testing method with high simulation, comprehensive service coverage and easy execution is one of the important subjects in the securities industry.
Disclosure of Invention
Aiming at the problems, the invention provides a high-simulation test method for a financial product valuation and accounting system, which solves the current situations that the existing valuation system test has serious dependence on valuation and accounting business knowledge, insufficient business coverage rate and lower test execution efficiency, enables the valuation system to have an efficient, real and reliable regression test mode, effectively discovers system defects and ensures valuation business continuity.
The technical scheme of the invention is as follows: high imitation of financial product valuation and accounting system
The true testing method comprises the following steps:
s1, preparing test data, selecting a date and a group of products from the production desensitization data as test input data, wherein the date covers the service variety which the test is expected to cover or produces all the service varieties;
s2: performing estimation execution on the baseline version, and performing estimation execution on the selected test data on the baseline version to obtain corresponding accounting data and estimation data;
s3: performing valuation execution on the version to be tested, and performing valuation execution on the selected test data on the version to be tested to obtain corresponding accounting data and valuation data;
s4: and performing result comparison and difference positioning, comparing the operation results of the baseline version and the version to be tested, analyzing and positioning the difference, and obtaining a test report.
Preferably, the test data selection of the test data preparation step comprises a continuous test data selection, i.e. a continuous date of production desensitization input is selected as test data.
Preferably, the algorithm for the continuous test data selection is described as follows:
inputting: a date range within which the test data is selected is limited.
1) Initialization
a) Calculating a service complete set list L and service frequency fi of each service, wherein i belongs to L;
b) calculating an effective product list Lv in a given date range and the business frequency Pf (p1) of each product, wherein p1 belongs to Lv;
c) initializing a service list to be covered L0 into a service complete set list, namely L0 is L;
d) setting the test data set to be null, and setting the covered service list L1 to be null;
e) calculating the maximum service coverage rate Rmax which can be reached by the data within the given date range, and taking the maximum service coverage rate Rmax as the end condition of the algorithm, namely when R is equal to Rmax, the algorithm is terminated;
note: if the test data is not required to reach the maximum service coverage rate, setting a service coverage rate threshold value as algorithm input, and taking the service coverage rate threshold value as an algorithm ending condition;
2) select the list L of valid productsvAdding a test data set into a product with the highest internal service frequency;
3) in the covered service list L1Adding the service covered by the product; list of simultaneous pending coverage services L0Deleting the service covered by the product;
4) in the active product list LvDeleting the product according to the updated service list L to be covered0Recalculating the service frequency of each product;
5) calculating the service coverage rate of the current test data set, and if the service coverage rate reaches the maximum service coverage rate or a given threshold value, finishing the algorithm; otherwise, repeating the steps 2-5 until the algorithm is finished;
and (3) outputting: test data set, covered service list L1List of services to be covered L0Service coverage rate R;
wherein, the service frequency f: service frequency f of a given service iiThe frequency with which the service has appeared in production over the past year; service frequency P of productf: product p1With respect to a given service listl service frequency Pf(p1) The sum of the service frequencies covered by the service list is as follows:
where i ∈ (l)p1∩l)，lp1Is p1A list of covered services; service coverage rate R: testing the occupation ratio of the data covered service in the service full set; the effective product is as follows: and in a given date range, the product always keeps active, and the judgment is carried out according to whether the product has business occurrence in a given starting date and ending date.
Preferably, the test scenario of the continuous time period business high coverage case test is as follows: and selecting a test case for testing according to the requirement of the service frequency and the product service frequency coverage rate according to the set continuous time period.
Preferably, the test purpose of the continuous time period service high coverage case test is: selecting a service high coverage product through an algorithm to perform continuous time testing; and simulating a real production operation mode, and finding the version problem by comparing the output results of the baseline version and the version to be detected after being processed by the estimation system.
Preferably, the test data selection in the test data preparation step further includes discrete test data selection, and for a service which cannot be covered by continuous data, the coverage is further completed by selecting discrete date data.
Preferably, the algorithm for selecting the discrete test data is described as follows:
inputting: a service list to be covered;
1) initialization: the discrete test data set is empty;
2) traversing the covered service list, inquiring the date of the service and the corresponding product in the desensitized production data, and adding the date, product pair into the discrete test data set
And (3) outputting: the test data set is discretized.
Preferably, the test scenario for testing the individual service cases at the discrete time points is as follows: and removing the services covered by the test cases in the continuous time period from the whole service, and selecting the discrete dates as the test cases by an algorithm according to the mode that a certain product covers the part of the services at a certain time point.
Preferably, the test purpose of the discrete-time point individual service case test is as follows: when dispersed
The test cases of the intermediate points are processed by the evaluation system through the baseline version and the version to be tested
And comparing the output results to find the version problem.
Compared with the prior art, the high-simulation test method for the financial product valuation and accounting system scientifically and reasonably selects production desensitization data as test input, carries out valuation execution on the valuation system baseline version and the version to be tested respectively, compares valuation results of the two versions, and locates defects by analyzing differences; the current situation that the existing valuation system tests seriously depend on valuation and accounting business knowledge, the business coverage rate is insufficient and the test execution efficiency is low is solved, so that the valuation system has an efficient, real and reliable regression test mode, the system defects are effectively found, and the valuation business continuity is guaranteed.
Drawings
Fig. 1 is a schematic diagram of a high simulation valuation test of a financial product valuation and accounting system high simulation test method according to the present invention.
FIG. 2 is a flow chart of continuous test data selection of a high simulation testing method of a financial product valuation and accounting system according to the present invention.
FIG. 3 is a diagram illustrating the effect of continuous data selection in the test data selection of the method for high-simulation testing of financial product valuation and accounting systems according to the present invention.
FIG. 4 is a diagram illustrating the effect of selecting discrete data in the test data selection of the method for high-simulation testing of financial product valuation and accounting systems according to the present invention.
FIG. 5 is a diagram showing the comparison of the test results of the high-simulation test method for the financial product valuation and accounting system of the present invention.
FIG. 6 is a flowchart of an embodiment of a high simulation testing method for a financial product valuation and accounting system according to the present invention.
Fig. 7 is a schematic diagram of a continuous time period business high coverage case test result of the high simulation test method of the financial product valuation and accounting system of the present invention.
Fig. 8 is a schematic diagram of a discrete-time point individual business case test result of the high-simulation testing method of the financial product valuation and accounting system of the present invention.
Detailed Description
In order to further understand the objects, structures, features and functions of the present invention, the following embodiments are described in detail.
Referring to fig. 1, fig. 2, fig. 3, fig. 4 and fig. 5, a high simulation test method for a financial product valuation and accounting system according to an embodiment of the present invention includes the following steps:
and S1, preparing test data, namely selecting a date and a group of products from the production desensitization data as test input data, wherein the date covers the service varieties to be covered by the test or produces all the service varieties.
S2: and performing estimation execution on the baseline version, and performing estimation execution on the selected test data on the baseline version to obtain corresponding accounting data and estimation data.
S3: and performing evaluation execution on the version to be tested, and performing evaluation execution on the selected test data on the version to be tested to obtain corresponding accounting data and evaluation data.
S4: and performing result comparison and difference positioning, comparing the operation results of the baseline version and the version to be tested, analyzing and positioning the difference, and obtaining a test report.
Since test data selection is the core of the test method, the test coverage and test effectiveness are directly determined, and a test data selection algorithm is further explained.
And the evaluation test data selection algorithm obtains a test data set with the optimal service coverage rate by continuously selecting the products with the most covered service varieties, and finally achieves the service coverage rate of 100% by combining with the discrete service selection.
In order to effectively count the service coverage rate of the estimation system, indexes representing service varieties need to be selected, and the estimation accounting level four subjects can effectively embody each service variety with accurate service granularity, so that the estimation accounting level four subjects are used as targets for measuring the service coverage condition.
Description of test data selection steps:
the test data selection of the step 1 and the test data preparation step comprises continuous test data selection, namely, a section of production desensitization input with continuous dates is selected as test data.
In order to preferentially select important services with high production frequency, concepts of service frequency and product service frequency are introduced, and the following main concepts related in the algorithm are described as follows:
service frequency f: service frequency f of a given service iiAs was the frequency with which this service has appeared in production over the past year.
Service frequency P of productf: product p1Frequency of traffic P relative to a given traffic list lf(p1) The sum of the service frequencies covered by the service list is as follows:
For example: service list l ═ { off-site _ marketed _ closed _ cost [3 ═ b { [ m-m]Off-site _ listed _ closed _ valuation [3]Upper exchange _ pledge _ cost [4]Deep pool _ pledge _ cost [3]}, product p1The covered service list is { Upper exchange _ pledge _ cost [4 ]]Deep pool _ pledge _ cost [3]Exchange _ listed _ exchangeable bond _ cost [4 [ ]][ wherein "] (wherein]"the frequency of the corresponding service in the product p), the product p is obtained1The service covered in the service list is { exchange-pledge-cost [4 ]]Deep pool _ pledge _ cost [3]Its traffic frequency with respect to l is 3+ 4-7.
Service coverage rate R: and testing the occupation ratio of the service covered by the data in the service complete set.
The effective product is as follows: and in a given date range, the product always keeps active, and the judgment is carried out according to whether the product has business occurrence in a given starting date and ending date.
Preferably, the algorithm for the continuous test data selection is described as follows:
inputting: a date range within which the test data is selected is limited.
1) Initialization
a) Calculating a service complete set list L and service frequency fi of each service, wherein i belongs to L;
b) calculating an effective product list Lv in a given date range and the business frequency Pf (p1) of each product, wherein p1 belongs to Lv;
c) initializing a service list to be covered L0 into a service complete set list, namely L0 is L;
d) setting the test data set to be null, and setting the covered service list L1 to be null;
e) calculating the maximum service coverage rate Rmax which can be reached by the data within the given date range, and taking the maximum service coverage rate Rmax as the end condition of the algorithm, namely when R is equal to Rmax, the algorithm is terminated;
note: if the test data is not required to reach the maximum service coverage rate, setting a service coverage rate threshold value as algorithm input, and taking the service coverage rate threshold value as an algorithm ending condition;
2) select the list L of valid productsvAdding a test data set into a product with the highest internal service frequency;
3) in the covered service list L1Adding the service covered by the product; list of simultaneous pending coverage services L0Deleting the service covered by the product;
4) in the active product list LvDeleting the product according to the updated service list L to be covered0Recalculating the service frequency of each product;
5) calculating the service coverage rate of the current test data set, and if the service coverage rate reaches the maximum service coverage rate or a given threshold value, finishing the algorithm; otherwise, repeating the steps 2-5 until the algorithm is finished;
and (3) outputting: test data set, covered service list L1List of services to be covered L0And a service coverage rate R.
Preferably, the test data selection in the test data preparation step further includes discrete test data selection, and for a service which cannot be covered by continuous data, the coverage is further completed by selecting discrete date data.
Preferably, the algorithm for selecting the discrete test data is described as follows:
inputting: a service list to be covered;
1) initialization: the discrete test data set is empty;
2) traversing the covered service list, inquiring the date of the service and the corresponding product in the desensitized production data, and adding the date, product pair into the discrete test data set
And (3) outputting: the test data set is discretized.
As shown in fig. 6, the specific implementation of an embodiment of the present invention is as follows:
i. continuous time period business high coverage case testing
Description of a test scenario: and selecting a test case for testing according to the requirement of the service frequency and the product service frequency coverage rate according to the set continuous time period.
The test purpose is as follows: and selecting a service high-coverage product through an algorithm to perform continuous time testing.
And simulating a real production operation mode, and finding the version problem by comparing the output results of the baseline version and the version to be detected after being processed by the estimation system.
Test example: the test results are shown in fig. 7.
Discrete time point individual service case testing
Testing scene: and removing the services covered by the test cases in the continuous time period from the whole service, and selecting the discrete dates as the test cases by an algorithm according to the mode that a certain product covers the part of the services at a certain time point.
The test purpose is as follows: the test purpose of the discrete time point individual service case test is as follows: and comparing the baseline version of the test case at the discrete time point with the output result of the version to be tested after being processed by the evaluation system to find the version problem.
Test example: the test results are shown in fig. 8.
The high-simulation test method of the financial product valuation and accounting system provided by the invention has the advantages that production desensitization data are scientifically and reasonably selected as test input, valuation execution is respectively carried out on a valuation system baseline version and a version to be tested, valuation results of the two versions are compared, and defects are positioned by analyzing differences; the current situation that the existing valuation system tests seriously depend on valuation and accounting business knowledge, the business coverage rate is insufficient and the test execution efficiency is low is solved, so that the valuation system has an efficient, real and reliable regression test mode, the system defects are effectively found, and the valuation business continuity is guaranteed.
The present invention has been described in relation to the above embodiments, which are only exemplary of the implementation of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the invention. Rather, it is intended that all such modifications and variations be included within the spirit and scope of this invention.
Claims (5)
1. A high simulation test method for a financial product valuation and accounting system is characterized by comprising the following steps:
s1, preparing test data, selecting a date and a group of products from the production desensitization data as test input data, wherein the date covers the service variety which the test is expected to cover or produces all the service varieties; the test data selection of the test data preparation step comprises continuous test data selection, namely, a section of production desensitization input with continuous dates is selected as test data; the test data selection of the test data preparation step also comprises discrete test data selection, and for the services which cannot be covered by continuous data, the discrete date data is selected to further complete the coverage;
s2: performing estimation execution on the baseline version, and performing estimation execution on the selected test data on the baseline version to obtain corresponding accounting data and estimation data;
s3: performing valuation execution on the version to be tested, and performing valuation execution on the selected test data on the version to be tested to obtain corresponding accounting data and valuation data;
s4: comparing the results and positioning the differences, comparing the operation results of the baseline version and the version to be tested, analyzing and positioning the differences to obtain a test report;
the method for selecting continuous test data to determine production desensitization data of the continuous date through the service frequency and the product service frequency so as to ensure that important service production data with high production frequency is preferentially selected comprises the following steps:
inputting: a date range within which the selection of test data is to be limited;
initialization
Calculating a service complete set list L and service frequency fi of each service, wherein i belongs to L;
calculating an effective product list Lv in a given date range and the business frequency Pf (p1) of each product, wherein p1 belongs to Lv;
initializing a service list to be covered L0 into a service complete set list, namely L0 is L;
setting the test data set to be null, and setting the covered service list L1 to be null;
calculating the maximum service coverage rate Rmax which can be reached by the data within the given date range, and taking the maximum service coverage rate Rmax as the end condition of the algorithm, namely when R is equal to Rmax, the algorithm is terminated;
if the test data is not required to reach the maximum service coverage rate, setting a service coverage rate threshold value as algorithm input, and taking the service coverage rate threshold value as an algorithm ending condition;
selecting a product with the highest service frequency in the effective product list Lv, and adding the product into a test data set;
adding the services covered by the product into the covered service list L1; meanwhile, deleting the service covered by the product in a service list L0 to be covered;
deleting the product in the effective product list Lv, and recalculating the service frequency of each product according to the updated service list to be covered L0;
calculating the service coverage rate of the current test data set, and if the service coverage rate reaches the maximum service coverage rate or a given threshold value, finishing the algorithm; otherwise, repeating the steps 2-5 until the algorithm is finished;
and (3) outputting: the method comprises the steps of testing a data set, a covered service list L1, a service list to be covered L0 and a service coverage rate R;
wherein, the service frequency f: service frequency f of a given service iiThe frequency with which the service has appeared in production over the past year; service frequency P of productf: product p1Frequency of traffic P relative to a given traffic list lf(p1) The sum of the service frequencies covered by the service list is as follows:
where i ∈ (l)p1∩l)，lp1Is p1A list of covered services; service coverage rate R: testing the occupation ratio of the data covered service in the service full set; the effective product is as follows: in a given date range, the product always keeps active, and whether a service occurs in a given start-stop date or not is judged according to the product;
the discrete test data selection method comprises the following steps:
inputting: a service list to be covered;
initialization: the discrete test data set is empty;
and traversing the covered service list, inquiring the date of the service and the corresponding product in the desensitized production data, and adding the < date, product > into the discrete test data set in a pairing manner.
2. The method of claim 1, wherein the test scenario for continuous time period business high coverage case test is: and selecting a test case for testing according to the requirement of the service frequency and the product service frequency coverage rate according to the set continuous time period.
3. The method of claim 2, wherein the test purpose of the continuous time period business high coverage case test is: selecting a service high coverage product through an algorithm to perform continuous time testing; and simulating a real production operation mode, and finding the version problem by comparing the output results of the baseline version and the version to be detected after being processed by the estimation system.
4. The method as claimed in claim 1, wherein the test scenario of the discrete-time individual case test is as follows: and removing the services covered by the test cases in the continuous time period from the whole service, and selecting the discrete dates as the test cases by an algorithm according to the mode that a certain product covers the part of the services at a certain time point.
5. The method as claimed in claim 1, wherein the discrete-time individual case tests are performed by: and comparing the baseline version of the test case at the discrete time point with the output result of the version to be tested after being processed by the evaluation system to find the version problem.
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