CN111444094B - Test data generation method and system - Google Patents
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Abstract
The invention discloses a method and a system for generating test data, wherein the method for generating test data comprises the following steps: receiving metadata information, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields, and finding out field relation characteristics in the associated data; and according to the field relation characteristics, matching different data operation methods to perform test data operation on the metadata, and outputting a storage format and a field type of the test data according to a storage position of the metadata. The method and the system for generating the program performance data can utilize less operation workload to generate the data for testing the program performance so as to realize the test of the program feasibility or the adjustment of the program efficiency. The embodiment of the invention solves the defect that the random generation of the test data is adopted at the present stage, so that the test data is more similar to the real data.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a system for generating test data.
Background
At present, test data are needed for testing the feasibility of a program or adjusting the efficiency of the program, however, in a traditional closed network system, real data are difficult to be imported into a related system, or the system only needs small-scale sample data, and the system program is large-scale batch operation, so that the existing test data are difficult to meet the requirement for testing the efficiency of the program. At present, the test data is mainly produced by random numbers, and the metadata is not processed in the conventional random production, so that the test data has the following problems: the data distribution is inconsistent, the correlation between fields cannot be reflected, and the correlation between the primary key and the external key is poor, so that the random generated test data has a larger gap from the real data, and the accuracy of the test and the tuning of the program efficiency are affected.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a method and a system for generating test data, which utilize less operation workload to generate the test data with higher similarity with real data.
In order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for generating test data, including the steps of:
receiving metadata information, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields, and finding out field relation characteristics in the associated data;
and according to the field relation characteristics, matching different data operation methods to perform test data operation on the metadata, and outputting a storage format and a field type of the test data according to a storage position of the metadata.
Further, the operation of testing the metadata includes the following steps:
when the test data with the explicitly related fields in the metadata are generated, the conversion of the data types and the data values among the fields is realized by adopting an explicitly addition, subtraction, multiplication and division four-rule operation rule;
when the metadata has the operation of fields between data sets with complex association, firstly, carrying out local recognition or definition on the data sets or defining the original data sets of small samples, selecting different algorithm models and debugging, thereby outputting different types of test data and verifying the accuracy of the test data.
Further, in the process of carrying out local recognition or definition on the data set to obtain the data characteristics by selecting a clustering algorithm or a classifying algorithm, firstly selecting fields closely related to the clustering algorithm or the classifying algorithm, then randomly selecting the value of one field according to the selected result, then randomly selecting other fields, and adopting a Bayesian algorithm model or a decision tree algorithm model according to the selected fields to determine the probability of conforming to the clustering result.
Further, in the process of carrying out local recognition on the data set or definitely carrying out original data set of a small sample, when the association algorithm is selected to obtain the data characteristics, firstly, selecting the fields relevant to the association algorithm, then, according to the association result, firstly, randomly associating a certain field, and according to the association result, generating other association fields according to the preset confidence coefficient.
Further, the adjusting of the distribution of the associated fields includes: the ARM algorithm is adopted for the unassociated fields to adjust the relevant probability distribution; filling different numbers of fields into the complex associated fields by adopting isolation boxes; the method comprises the steps of enabling foreign keys of a field table to exist in a related field table by judging the association degree of main foreign keys among the fields; the field constraint is performed by the length and precision of the field.
On the other hand, the invention also provides a system for generating test data, which comprises:
the metadata processing module is used for receiving information of metadata, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields and finding out field relation characteristics in the associated data;
and the test data generation module is used for carrying out test data operation on the metadata according to the field relation characteristics and matching different data operation methods, and outputting the storage format and the field type of the test data according to the storage position of the metadata.
Further, the test data generation module comprises a field operation unit, wherein the field operation unit is used for performing test data operation on the metadata;
when the test data with the explicitly related fields in the metadata are generated, the field operation unit adopts an explicitly addition, subtraction, multiplication and division four-rule operation rule to realize conversion of data types and data values among fields;
when the metadata has the operation of the field between the data sets with complex association, the field operation unit firstly carries out local recognition or definitely confirms the original data set of a small sample on the data sets, selects different algorithm models and debugs, thereby outputting different types of test data and verifying the accuracy of the test data.
Further, in the process of carrying out local recognition or definition on the data set to obtain the data characteristics by selecting a clustering algorithm or a classifying algorithm, the field operation unit firstly selects a field closely related to the clustering algorithm or the classifying algorithm, then randomly selects the value of a certain field according to the selected result, then randomly selects other fields, and adopts a Bayesian algorithm model or a decision tree algorithm model according to the selected field to determine the probability of conforming to the clustering result.
Further, in the process of carrying out local recognition or definitely determining the original data set of a small sample on the data set, when the association algorithm is selected to obtain the data characteristics, firstly selecting a field related to the association algorithm, then randomly associating a certain field according to the association result, and generating other associated fields according to the predetermined confidence coefficient through the association result.
Further, the metadata processing module includes a field distribution adjustment unit, where the field distribution adjustment unit is configured to perform distribution adjustment on the associated field, and includes: the ARM algorithm is adopted for the unassociated fields to adjust the relevant probability distribution; filling different numbers of fields into the complex associated fields by adopting isolation boxes; the method comprises the steps of enabling foreign keys of a field table to exist in a related field table by judging the association degree of main foreign keys among the fields; the field constraint is performed by the length and precision of the field.
The technical scheme provided by the embodiment of the invention has the beneficial effects that:
the embodiment of the invention provides a method and a system for generating test data, which are characterized in that in the process of generating the test data, information of metadata is received firstly, associated data of the metadata is clarified, the associated data is analyzed, the distribution of associated fields is adjusted, and field relation characteristics in the associated data are found out; and then, according to the field relation characteristics, matching different data operation methods to perform test data operation on the metadata, and outputting the storage format and field type of the test data according to the storage position of the metadata. The generating method and the generating system can generate data for testing program performance by using less operation workload so as to realize the test of program feasibility or the tuning of program efficiency. The embodiment of the invention solves the defect that the random generation of the test data is adopted at the present stage, so that the test data is more similar to the real data, and the accuracy of program test is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for generating test data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for generating test data according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a processing procedure of a field operation unit in the test data generating system according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1 to 2, the present embodiment provides a method for generating test data, including the following steps:
s1: receiving metadata information, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields, and finding out field relation characteristics in the associated data;
s2: and according to the field relation characteristics, matching different data operation methods to perform test data operation on the metadata, and outputting a storage format and a field type of the test data according to a storage position of the metadata.
Specifically, during the process of generating test data, the metadata are processed and operated before generation, so that the field relation of the metadata is more definite, the generated test data has stronger pertinence, therefore, the data for testing the program performance can be generated by using less operation workload, the test of the program feasibility or the adjustment of the program efficiency can be realized, the defect that the test data are randomly generated at the present stage is overcome, the test data are more similar to the real data, and the accuracy of the program test is improved.
Preferably, the operation of testing the metadata includes the steps of:
when the test data with the explicitly related fields in the metadata are generated, the conversion of the data types and the data values among the fields is realized by adopting an explicitly addition, subtraction, multiplication and division four-rule operation rule; for example, there are unit price fields and number fields in the table, and the total price field is mainly set as unit price. By means of the simple calculation, the test data of the fields with clear phase relation can be obtained quickly and accurately, and the efficiency of generating the test data is improved.
When the metadata has the operation of fields between data sets with complex association, firstly, carrying out local recognition or definition on the data sets or defining the original data sets of small samples, selecting different algorithm models and debugging, thereby outputting different types of test data and verifying the accuracy of the test data. The advance that this stage mainly achieves is a local knowledge of the dataset, or the raw data with a small sample. The relevant fields are selected by knowledge of the dataset. Different algorithms are selected for different recognitions to produce data. This stage is the bright spot of the method. The difficulty of this stage is the selection and debugging of the algorithm. In addition, in order to ensure the accuracy of the production data, different algorithms are required to be used to verify the readiness of the production data, specifically, in the process of locally recognizing or definitely determining the original data set of small samples on the data set, when a clustering algorithm or a classifying algorithm is selected to obtain data features, firstly, a field closely related to the clustering algorithm or the classifying algorithm is selected, then, according to the selected result, the value of a certain field is randomly selected, then, other fields are randomly selected, and according to the selected field, a bayesian algorithm model or a decision tree algorithm model is adopted to determine the probability of conforming to the clustering result. On the other hand, in the process of carrying out local recognition on the data set or definitely carrying out original data set of a small sample, when a correlation algorithm is selected to obtain data characteristics, firstly, selecting fields relevant to the correlation algorithm, then, according to a correlation result, firstly, randomly correlating certain fields, and according to the correlation result, generating other correlation fields according to a preset confidence coefficient. By adopting different operation models for different associated fields, the accuracy of test data corresponding to the associated fields can be remarkably improved, so that the test data and the real data are more similar, the accuracy in the test of the functions and the efficiency of the program is ensured, inconsistent data distribution and poor association between a main key and an external key are avoided, and meanwhile, the correlation between fields is well reflected.
Preferably, the adjusting of the distribution of the association field includes: the ARM (Acceptance-reject method) algorithm is adopted for the uncorrelated fields to adjust the correlation probability distribution, and is an analog method in nature, rather than a direct mathematical method. It guarantees that its accepted probability obeys the specified PDF by another random number after each generation of a new random number. The adaptability is wider, and ARM is a good choice when the inverse function of CDF can not be obtained; filling different numbers of fields into the complex associated fields by adopting isolation boxes, wherein the data distribution at this stage mainly adjusts the complex associated distribution of the fields among the data sets, and mainly adopts the boxes according to a certain standard, and the number of the filled fields in different boxes is different; the method comprises the steps of judging the association degree of main foreign keys among fields, enabling foreign keys of a field table to exist in a related field table, and ensuring that fields appear in the related table for a plurality of times according to a preset probability; the field constraint is performed by the length and precision of the field.
Embodiment two:
on the other hand, the invention also provides a system for generating test data, which comprises:
the metadata processing module is used for receiving information of metadata, defining associated data of the metadata, analyzing the associated data, adjusting distribution of the associated fields and finding out field relation characteristics in the associated data;
and the test data generation module is used for carrying out test data operation on the metadata according to the field relation characteristics and matching different data operation methods, and outputting the storage format and the field type of the test data according to the storage position of the metadata.
Specifically, when the test data is generated by the test data generation system, the metadata are processed and calculated before generation, so that the field relation of the metadata is more definite, the generated test data has stronger pertinence, and therefore, the data for testing the program performance can be generated by using less operation workload, the test of the program feasibility or the adjustment of the program efficiency is realized, the defect that the test data is randomly generated at the present stage is overcome, the test data is more similar to the real data, and the accuracy of the program test is improved.
Preferably, the test data generating module includes a field operation unit, as shown in fig. 3, for performing an operation of test data on the metadata;
when the test data with the explicitly related fields in the metadata are generated, the field operation unit adopts an explicitly addition, subtraction, multiplication and division four-rule operation rule to realize conversion of data types and data values among fields; for example, there are unit price fields and number fields in the table, and the total price field is mainly set as unit price. By means of the simple calculation, the test data of the fields with clear phase relation can be obtained quickly and accurately, and the efficiency of generating the test data is improved.
When the metadata has the operation of the field between the data sets with complex association, the field operation unit firstly carries out local recognition or definitely confirms the original data set of a small sample on the data sets, selects different algorithm models and debugs, thereby outputting different types of test data and verifying the accuracy of the test data.
Preferably, the field operation unit selects a field closely related to the clustering algorithm or the classifying algorithm when the clustering algorithm or the classifying algorithm is selected to obtain the data feature in the process of locally recognizing or definitely determining the original data set of the small sample, then randomly selects a value of a certain field according to the selected result, randomly selects other fields, and adopts a bayesian algorithm model or a decision tree algorithm model according to the selected field to determine the probability of conforming to the clustering result. Further, in the process of carrying out local recognition or definitely determining the original data set of a small sample on the data set, when the association algorithm is selected to obtain the data characteristics, firstly selecting a field related to the association algorithm, then randomly associating a certain field according to the association result, and generating other associated fields according to the predetermined confidence coefficient through the association result. By adopting different operation models for different associated fields, the accuracy of test data corresponding to the associated fields can be remarkably improved, so that the test data and the real data are more similar, the accuracy in the test of the functions and the efficiency of the program is ensured, inconsistent data distribution and poor association between a main key and an external key are avoided, and meanwhile, the correlation between fields is well reflected.
Preferably, the metadata processing module includes a field distribution adjustment unit, where the field distribution adjustment unit is configured to perform distribution adjustment on the associated field, and includes: the ARM (Acceptance-reject method) algorithm is adopted for the uncorrelated fields to adjust the correlation probability distribution, and is an analog method in nature, rather than a direct mathematical method. It guarantees that its accepted probability obeys the specified PDF by another random number after each generation of a new random number. The adaptability is wider, and ARM is a good choice when the inverse function of CDF can not be obtained; filling different numbers of fields into the complex associated fields by adopting isolation boxes, wherein the data distribution at this stage mainly adjusts the complex associated distribution of the fields among the data sets, and mainly adopts the boxes according to a certain standard, and the number of the filled fields in different boxes is different; the method comprises the steps of judging the association degree of main foreign keys among fields, enabling foreign keys of a field table to exist in a related field table, and ensuring that fields appear in the related table for a plurality of times according to a preset probability; the field constraint is performed by the length and precision of the field. Any combination of the above optional solutions may be adopted to form an optional embodiment of the present invention, which is not described herein.
It should be noted that: in the test data generating system provided in the above embodiment, only the division of the functional modules is used for illustration when outputting test data, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the test data generating system is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the system for generating test data and the method for generating test data provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the system and the method embodiment are detailed in the detailed description of the method embodiment, which is not repeated here.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method of generating test data, comprising the steps of:
receiving metadata information, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields, and finding out field relation characteristics in the associated data;
according to the field relation characteristics, matching different data operation methods to perform test data operation on the metadata, and outputting a storage format and a field type of the test data according to a storage position of the metadata;
wherein the operation of testing the metadata comprises:
when the metadata has the operation of fields among data sets with complex association, in the process of carrying out local recognition or definition on the data sets or defining the original data sets of small samples, when a clustering algorithm or a classifying algorithm is selected to obtain data characteristics, firstly selecting the fields closely related to the clustering algorithm or the classifying algorithm, then randomly selecting the value of a certain field according to the selected result, randomly selecting other fields, and adopting a Bayesian algorithm model or a decision tree algorithm model according to the selected fields to determine the probability conforming to the clustering result;
when the association algorithm is selected to obtain the data characteristics, firstly selecting a field which is related to the association algorithm, then randomly associating a certain field according to the association result, and generating other association fields according to the predetermined confidence coefficient through the association result;
and selecting and debugging different algorithm models so as to output different types of test data and verify the accuracy of the test data.
2. The method of generating test data according to claim 1, wherein performing the operation of the test data on the metadata further comprises:
when the test data with the definition related fields in the metadata are generated, the conversion of the data types and the data values among the fields is realized by adopting the explicit arithmetic rule of adding, subtracting, multiplying and dividing.
3. The method of generating test data according to claim 1, wherein the adjusting of the distribution of the associated fields comprises: the ARM algorithm is adopted for the unassociated fields to adjust the relevant probability distribution; filling different numbers of fields into the complex associated fields by adopting isolation boxes; the method comprises the steps of enabling foreign keys of a field table to exist in a related field table by judging the association degree of main foreign keys among the fields; the field constraint is performed by the length and precision of the field.
4. A system for generating test data, comprising:
the metadata processing module is used for receiving information of metadata, defining associated data of the metadata, analyzing the associated data, adjusting distribution of associated fields and finding out field relation characteristics in the associated data;
the test data generation module is used for carrying out test data operation on the metadata according to the field relation characteristics and matching different data operation methods, and outputting a storage format and a field type of the test data according to the storage position of the metadata;
the test data generation module comprises a field operation unit, wherein the field operation unit is used for performing test data operation on the metadata;
when the metadata has the operation of fields among data sets with complex association, in the process of carrying out local recognition or definition on the data sets or defining the original data sets of small samples, when a clustering algorithm or a classifying algorithm is selected to obtain data characteristics, firstly selecting the fields closely related to the clustering algorithm or the classifying algorithm, then randomly selecting the value of a certain field according to the selected result, randomly selecting other fields, and adopting a Bayesian algorithm model or a decision tree algorithm model according to the selected fields to determine the probability conforming to the clustering result; when the association algorithm is selected to obtain the data characteristics, firstly selecting a field which is related to the association algorithm, then randomly associating a certain field according to the association result, and generating other association fields according to the predetermined confidence coefficient through the association result; and selecting and debugging different algorithm models so as to output different types of test data and verify the accuracy of the test data.
5. The system for generating test data according to claim 4, wherein the field operation unit is further configured to:
when the test data with the definition related fields in the metadata are generated, the field operation unit adopts the explicit arithmetic rules of addition, subtraction, multiplication and division to realize conversion of data types and data values among fields.
6. The system for generating test data according to claim 4, wherein the metadata processing module includes a field distribution adjustment unit for performing distribution adjustment on the associated field, comprising: the ARM algorithm is adopted for the unassociated fields to adjust the relevant probability distribution; filling different numbers of fields into the complex associated fields by adopting isolation boxes; the method comprises the steps of enabling foreign keys of a field table to exist in a related field table by judging the association degree of main foreign keys among the fields; the field constraint is performed by the length and precision of the field.
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