CN109324960A - Automatic test approach and terminal device based on big data analysis - Google Patents
Automatic test approach and terminal device based on big data analysis Download PDFInfo
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Abstract
The present invention is suitable for technical field of data processing, provides automatic test approach, terminal device and computer readable storage medium based on big data analysis, comprising: generates full dose data source options based on configured multiple data sources；Selection result of the tester about the full dose data source options is obtained, and determines the data source corresponding with the selection result；The data that the corresponding data source of the selection result is directed toward are extracted, set up normal data case as normal data, and based on the normal data；Data to be tested are obtained, and the data to be tested are compared with the normal data in the normal data case, if comparing the prompt for successfully exporting and being successfully tested.The present invention is based on mass datas to configure multiple data sources, and can be tested according to actual needs from multiple data source capability normal datas, and the freedom degree of data test is improved.
Description
Technical field
The invention belongs to technical field of data processing, more particularly to the automatic test approach based on big data analysis, terminal
Equipment and computer readable storage medium.
Background technique
In business scenario, the real data to business in production or new business is needed to test, to check actual number
According to accuracy.In testing process, the normal data as test object is usually generated, and constructing includes multiple criterion numerals
According to test cases, real data is tested according to test cases, checks between real data and normal data whether deposit
In difference.
But test cases is usually to face the biggish business field of some portfolios by artificially writing in the prior art
Jing Shi, the data volume of real data is likely to be breached even hundred million grades of millions, therefore test cases can not cover all real data,
Also, tester can not unrestricted choice normal data source.To sum up, in the prior art test cases to real data
Coverage is low, and the source of normal data is few and fixed, can not support selection operation.
Summary of the invention
In view of this, the embodiment of the invention provides by the automatic test approach of big data analysis, terminal device and based on
Calculation machine readable storage medium storing program for executing, to solve the problem of that test data coverage is low in the prior art and can not support freely to choose.
The first aspect of the embodiment of the present invention provides a kind of automatic test approach based on big data analysis, comprising:
Full dose data source options are generated based on configured multiple data sources, and the multiple data source includes and target service
The relevant business datum source of type, prediction data source, filling data source and self-control data source；
Selection result of the tester about the full dose data source options is obtained, and determination is corresponding with the selection result
The data source；
The data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and are based on the mark
Quasi- data set up normal data case；
Obtain data to be tested, and by the normal data in the data to be tested and the normal data case into
Row compares, if comparing the prompt for successfully exporting and being successfully tested.
The second aspect of the embodiment of the present invention provides a kind of terminal device, and the terminal device includes memory, processing
Device and storage in the memory and the computer program that can run on the processor, described in the processor execution
Following steps are realized when computer program:
Full dose data source options are generated based on configured multiple data sources, and the multiple data source includes and target service
The relevant business datum source of type, prediction data source, filling data source and self-control data source；
Selection result of the tester about the full dose data source options is obtained, and determination is corresponding with the selection result
The data source；
The data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and are based on the mark
Quasi- data set up normal data case；
Obtain data to be tested, and by the normal data in the data to be tested and the normal data case into
Row compares, if comparing the prompt for successfully exporting and being successfully tested.
The third aspect of the embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer program, and the computer program realizes following steps when being executed by processor:
Full dose data source options are generated based on configured multiple data sources, and the multiple data source includes and target service
The relevant business datum source of type, prediction data source, filling data source and self-control data source；
Selection result of the tester about the full dose data source options is obtained, and determination is corresponding with the selection result
The data source；
The data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and are based on the mark
Quasi- data set up normal data case；
Obtain data to be tested, and by the normal data in the data to be tested and the normal data case into
Row compares, if comparing the prompt for successfully exporting and being successfully tested.
Existing beneficial effect is the embodiment of the present invention compared with prior art:
The embodiment of the present invention is based on business datum relevant to target service type source, prediction data source, filling data source
And self-control data source generates full dose data source options, and full dose data source options are provided to tester, tester can
According to the needs of test scene, full dose data source options are selected, after getting the selection result of tester, are based on
The corresponding data source of selection result constructs normal data case, and data to be tested are compared with normal data case, such as
There is the normal data being consistent with data to be tested in fruit normal data case, then export the prompt being successfully tested, the present invention is real
Example is applied by building multiple data sources in advance, improves data cover degree, and by providing full dose data source options, so that
Tester can freely choose the data source of needs, improve data test in the applicability of different scenes.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is the implementation flow chart for the automatic test approach based on big data analysis that the embodiment of the present invention one provides；
Fig. 2 is the implementation flow chart of the automatic test approach provided by Embodiment 2 of the present invention based on big data analysis；
Fig. 3 is the implementation flow chart for the automatic test approach based on big data analysis that the embodiment of the present invention three provides；
Fig. 4 is the implementation flow chart for the automatic test approach based on big data analysis that the embodiment of the present invention four provides；
Fig. 5 is the implementation flow chart for the automatic test approach based on big data analysis that the embodiment of the present invention five provides；
Fig. 6 is the structural block diagram for the terminal device that the embodiment of the present invention six provides；
Fig. 7 is the schematic diagram for the terminal device that the embodiment of the present invention seven provides.
Specific embodiment
In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed
Body details, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that there is no these specific
The present invention also may be implemented in the other embodiments of details.In other situations, it omits to well-known system, device, electricity
The detailed description of road and method, in case unnecessary details interferes description of the invention.
In order to illustrate technical solutions according to the invention, the following is a description of specific embodiments.
Fig. 1 shows the implementation process of the automatic test approach provided in an embodiment of the present invention based on big data analysis, in detail
It states as follows:
In S101, full dose data source options are generated based on configured multiple data sources, the multiple data source includes
Business datum relevant to target service type source, prediction data source, filling data source and self-control data source.
In order to facilitate the description embodiment of the present invention, it is illustrated by insurance business of business scenario, but should know, this
The restriction to the embodiment of the present invention is not constituted.Usually, there are multiple insurance kinds for insurance business, and for each insurance kind, it deposits
In multiple users that insure, the information of insuring (such as insurer's age, the insure time limit and insured amount etc.) for the user that each insures
There are differences, therefore the order of magnitude of data relevant to insurance business is higher, and the differentiation degree between data is high.Compared to
Traditional test case of being established manually by tester tests data, and data cover degree is low, in embodiments of the present invention,
Multiple data sources relevant to target service type are configured first, and multiple data sources include business datum source, prediction data source, fill out
Fill data source and self-control data source.It is noted that for some business, it is understood that there may be multiple types of business, therefore
Target service type can be one or more of multiple types of business.Data source refer to the data medium of storing data it
Between link information, therefore configuring multiple data sources is substantially to build multiple data mediums, and obtain the multiple data mediums of connection
Link information, in embodiments of the present invention, the type of data medium includes but is not limited to database, EXCEL table and comma point
Every value (Comma-Separated Values, CSV) file.
Specifically, the multiple data mediums built include business datum medium, prediction data medium, filling data medium with
And self-control data medium.The corresponding business datum media storage in business datum source has to be handled target service type and corresponds to industry in real time
Relevant business datum during business；The corresponding prediction data media storage of prediction data source has related to target service type
Historical data and according to data prediction model generate prediction data, wherein historical data, which refers to, handles target pervious
Type of business corresponds to relevant data during business, and data prediction model is by history relevant to target service type
Data are trained preset data processing model；The corresponding filling data media storage of filling data source has and mesh
Mark the relevant multiple available data records of type of business, the available data note after being filled which includes the data to missing
Record；The self-control data for having tester to write are stored in the corresponding self-control data medium of self-control data source.On it is noted that
Business datum, historical data and the available data record stated are just for the sake of the different data medium of differentiation and different data
Source, in practical application scene, it is identical that business datum, prediction data and available data record possible identical or part.To packet
The multiple data mediums for including business datum medium, prediction data medium, filling data medium and self-control data medium have been built
Cheng Hou determines to include business datum source, prediction data source, fills data source and make multiple data sources of data source by oneself, from
And full dose data source options are generated based on multiple data sources.
In S102, selection result of the tester about the full dose data source options, and the determining and choosing are obtained
Select the corresponding data source of result.
After generating full dose data source options, full dose data source options are shown on the host that tester operates
Front end page, wherein full dose data source options can be shown in the form of graphic interface.In full dose data source options, mention
The selection mode to multiple data sources is supplied, selection mode can be the one or more of multiple data sources.Getting test
After selection result of the personnel about full dose data source options, corresponding data source is determined according to selection result.It is of course also possible to pre-
Selection result is first formulated, after full dose data source options generate, determines corresponding data source automatically according to the selection result of formulation.
In S103, the data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and base
Normal data case is set up in the normal data.
In embodiments of the present invention, since the corresponding data medium type of each data source may be different, therefore for convenience
Subsequent comparing is carried out, after the corresponding data source of selection result determines, export selection result corresponding data source first refers to
To data, using derived data as normal data, and normal data is constructed as normal data case according to reference format,
Wherein, reference format is the preset format for comparing, for example can be EXCEL table, for example, selection result is corresponding
Data source be business datum source and prediction data source, the type of the corresponding business datum medium in business datum source is database,
The type of the corresponding prediction data medium of prediction data source is EXCEL table, and reference format is EXCEL table, then first exports business
Data in data medium and prediction data medium import in newly-built EXCEL table as normal data, and by normal data, most
The EXCEL table completed will be imported afterwards as normal data case.Certainly, assembly method is not limited to above-mentioned example, for example can also answer
EXCEL table in prediction data medium processed, and the data in business datum medium are imported into the EXCEL table of duplication, it should
EXCEL table is normal data case.
In S104, data to be tested are obtained, and will be described in the data to be tested and the normal data case
Normal data is compared, if comparing the prompt for successfully exporting and being successfully tested.
In the embodiment of the present invention, data to be tested are obtained, wherein can obtain the number to be tested that tester is manually entered
According to, can also in advance in the front end page for test setting upload option, and obtain tester by upload option on
The file to be tested (such as EXCEL table) passed, and data to be tested are obtained from file to be tested, or obtain tester and pass through
The database table address that option uploads is uploaded, and obtains the database table stored and need test data from database table address.
It is noted that prompting tester if the file to be tested of the tester's upload got is different from reference format
Again it is uploaded.After obtaining data to be tested, data to be tested and the normal data in normal data case are compared
It is right, if reference format be database table, may be used structured query language (Structured Query Language,
SQL) sentence is compared；If reference format be EXCEL table, using in EXCEL table vlookup function or EXCEL look into
Inquiry tool is compared.If in normal data case exist normal data identical with data to be tested, export test at
The prompt of function；If normal data identical with data to be tested is not present in normal data case, warning note is exported.
Optionally, setting test switch, to control the upload permissions of tester.In embodiments of the present invention, it is being used for
Setting test switchs in the front end page of test, and test switch is closed under default situations, forbids typing or uploads data to be tested.
Detect normal data case set up after the completion of, automatically will test switch open so that tester can upload it is to be tested
Data, and it can be compared with normal data immediately after getting data to be tested, it improves in data to be tested
The validity of biography prevents data retention to be tested.
By embodiment illustrated in fig. 1 it is found that in embodiments of the present invention, by based on configured multiple data sources generation
Full dose data source options, multiple data sources include business datum relevant to target service type source, prediction data source, filler
According to source and self-control data source, full dose data source options are pushed into tester, and obtain the selection result of tester, really
Fixed data source corresponding with selection result, the data that the corresponding data source of selection result is directed toward are as normal data, based on mark
Quasi- data set up normal data case, finally obtain data to be tested, by the standard in data to be tested and normal data case
Data are compared, and the prompt being successfully tested are exported if comparing successfully, the embodiment of the present invention is by generating the choosing of full dose data source
, enable tester according to practical application scene unrestricted choice normal data, improves the applicability of data test.
It is that the corresponding data medium in business datum source is subjected to data on the basis of the embodiment of the present invention one shown in Fig. 2
A kind of implementation method that the process of storage obtains after being refined.The embodiment of the invention provides based on the automatic of big data analysis
The implementation flow chart of test method, as shown, being somebody's turn to do the automatic test approach based on big data analysis may comprise steps of:
In S201, determination is relevant to the target service type to handle the page, and the page of handling is for by user
Operation is to handle the corresponding business of the target service type.
Due in transacting business, first having to the business datum of acquisition user, such as business as insurance business, then obtain
The business datum of user includes user's gender, age and insured amount etc., therefore in embodiments of the present invention, to handling target industry
Relevant business datum is obtained during business type corresponds to business.For business, it will usually preset one
For the front end page of transacting business, by user or business personnel by the business in the paper document (such as business handling list) under line
Data are manually entered to the front end page and upload, or the business datum of other pages statistics on line is manually entered to this
Front end page simultaneously uploads, therefore determines and corresponding with target service type handle the page to obtain business datum.
In S202, page insertion monitor component is handled described, is obtained by the monitor component and is handled by described
The business datum of page input, and the business datum is stored into data medium corresponding to the business datum source.
In embodiments of the present invention, it is inserted into monitor component in the page in target service type corresponding handle, and number is set
According to storage script, which can be write based on javascript language, and is arranged to work as in monitor component and is detected business
The mechanism of trigger data storage script when data, wherein business can be detected by the upload button of business datum in front end page
Data, when upload button is clicked, identification detects business datum.Data storage script starts automatic execution after being triggered,
In the implementation procedure of data storage script, the business datum in front end page is collected, and business datum is stored to business number
According to the corresponding data medium in source, i.e. business datum medium.
Optionally, the business datum in front end page is grabbed by crawler technology, and in real time stores business datum to industry
The corresponding data medium of data source of being engaged in.In embodiments of the present invention, the uniform resource locator of front end page is obtained first
(Uniform Resource Locator, URL) is sent super by the server where the URL of the crawler technology forward end page
Text transfer protocol (HyperText Transfer Protocol, HTTP) request receives the HTTP response that server returns,
And HTTP response is parsed and (can parse library such as Beautifulsoup, pyquery etc. by regular expression or third party)
Business datum is obtained, finally stores business datum into the corresponding data medium in business datum source.By crawler technology, realize
Automatically grabbing and storing for business datum, reduces the acquisition difficulty of business datum, suitable for there are multiple target service kinds
The case where class corresponding front end page.
By embodiment illustrated in fig. 2 it is found that in embodiments of the present invention, determination is corresponding with target service type to handle page
Face, this is handled the page and handles the corresponding business of target service type for being operable to, and is handling page insertion monitor component,
The business datum by handling page input is obtained by monitor component, is finally stored business datum corresponding to business datum source
Data medium, the embodiment of the present invention by front end page be inserted into monitor component, realize the real-time acquisition of business datum, mention
The timeliness for obtaining business datum is risen.
It shown in Fig. 3, is stored on the basis of the embodiment of the present invention one, and in the corresponding data medium of prediction data source
On the basis of historical data relevant to multiple types of business, obtained one kind after being refined to the process for generating prediction data
Implementation method.The embodiment of the invention provides the implementation flow charts of the automatic test approach based on big data analysis, as shown,
The automatic test approach based on big data analysis may comprise steps of:
In S301, the historical data is obtained, and big data analysis is carried out to the historical data, filter out described go through
The first garbled data relevant to the target service type in history data.
It usually,, can will data relevant to business after the completion of business on line in order to guarantee the accuracy of business
It is stored and (in order to save memory space, is generally carried out the data of multiple types of business of business centrally stored), formation is gone through
History data, in the historical data, type of business is stored generally as field or attribute.In embodiments of the present invention, it predicts
Storage has historical data relevant to multiple types of business in the corresponding prediction data medium of data source, and in addition to target service kind
Outside class, other types of business are all non-essential, thus to historical data carry out big data analysis, filter out in historical data with
Relevant first garbled data of target service type.Specifically, historical data is added to Hive data warehouse, and used
SparkSQL carries out data screening by condition of target service type.Wherein, Hive data warehouse is the data based on Hadoop
The data file of structuring can be mapped as database table, and provide SQL query function by warehouse tool；Spark is that open source is
For the universal parallel frame of large-scale data processing, and SparkSQL is the module that Spark is used for processing structure data.
Optionally, it after filtering out the first garbled data, is deleted in addition to the first garbled data in prediction data medium
Historical data.In order to save memory space, after determining the first garbled data, except the first screening in deletion prediction data medium
Historical data outside data.It, can be after determining the first garbled data, the if the type of prediction data medium is database
Database table setting where one garbled data retains mark, after all historical datas are all analyzed, in prediction data
Only retain the database table for existing and retaining mark in medium.
In S302, first garbled data is pre-processed, removes first screening there are abnormal data
Data obtain the second garbled data.
Due to being likely to occur corrupt data during business handling on line and in data storage procedure, lead to certain go through
There is exception in history data, for example the age is negative value etc., therefore the first garbled data obtained to screening pre-processes.Specifically,
Multiple data value ranges are preset, and the first garbled data are detected according to multiple data value ranges, multiple numbers
It is corresponding with multiple data attributes of the first garbled data according to value range.For example, business is insurance business, the first screening number
According to multiple data attributes include age, annual income, number in the household and insured amount, then can insuring according to insurance business
Limitation sets multiple data value ranges and is followed successively by 20 years old to 70 years old, is greater than 50,000, is greater than 0 and is greater than 1000.When the first screening
When the data of data attribute are not located in corresponding data value range under data, assert that the data are abnormal data, then remove
Fall the first garbled data where the data.After to all first garbled datas pretreatment in prediction data medium,
Using the first garbled data retained as the second garbled data.
In S303, based on second garbled data construct basic data collection, by the basic data collection with it is preset
Data processing model is fitted, and the data processing model that fitting is completed is exported as data prediction model.
In embodiments of the present invention, basic data collection is constructed based on the second garbled data, basic data collection includes feature ginseng
Several and result parameter, let it be assumed, for the purpose of illustration, that characteristic parameter is age, annual income and the family people in the second garbled data
Mouth number, result parameter are the insured amount in the second garbled data.After the completion of building, at basic data collection and preset data
Reason model be fitted, with training data handle model, finally will fitting complete data processing model as prediction model into
Row output.As an example it is assumed that the second garbled data, there are N number of, the basic data collection of building is (Parametercha1,
Parameterresult1), (Parametercha2, Parameterresult2)……(ParameterchaN, ParameterresultN),
Wherein, ParameterchaiIndicate the characteristic parameter in i-th of second garbled datas, ParameterresultiIt indicates i-th second
Result parameter in garbled data.During being fitted basic data collection and data processing model, setting is tied first
Fruit parameter may belong to K classification, and concentrating a characteristic parameter for basic data is ParameterchaXData, according to K
It is a classification to ParameterchaXCorresponding result parameter ParameterresultXClassify.For example, if K=3, and
Insured amount is less than or equal to 1000 for first classification, and it is the second point that insured amount, which is greater than 1000 and is less than or equal to 3000,
Class, insured amount are greater than 3000 and classify for third.If ParameterresultXValue be 2500, then to ParameterresultX
Classify, Parameter can be obtainedresultXDesired value under K classification is respectively ParameterresultX1=0,
ParameterresultX2=1, ParameterresultX3=0.
In addition, setting ParameterresultXDesired value positioned at k-th classification is respectively by F1(ParameterchaX), F2
(ParameterchaX) ... ..., FK(ParameterchaX) K function calculate, above-mentioned desired value is made by oneself when calculating first time
Justice.In data processing model, it is assumed that ParameterresultXThe probability for belonging to classification k is pk(ParameterchaX), then it calculates
The formula of probability is as follows:
In above-mentioned formula, exp () refers to using natural constant e as the exponential function at bottom.
Also, it is as follows to define departure function:
The process that basic data collection and data processing model are fitted is by calculating the value so that departure function
Process as small as possible.After all data in basic data collection are input to data processing model, final calculated F1
(ParameterchaX), F2(ParameterchaX) ... ..., FK(ParameterchaX) constitute data prediction model.
In S304, prediction data is generated by the data prediction model, and the prediction data is stored to described
The corresponding data medium of prediction data source.
After data prediction model generation, the input parameter that will be consistent with characteristic parameter format
ParameterchaInputIt is input to data prediction model, then it is maximum pre- to calculate numerical value according to data prediction model
Time value specifically calculates separately F1(ParameterchaInput), F2(ParameterchaInput) ... ..., FK
(ParameterchaInput), and by the corresponding classification of the maximum desired value of numerical value as where the output parameter of data prediction model
Classify (calculating output parameter).In embodiments of the present invention, prediction data is generated by data prediction model, wherein can
The characteristic parameter that basic data is concentrated, can also be by tester's typing or upload as the input parameter of data prediction model
Input parameter of the data as data prediction model, and the output parameter group for inputting parameter and data prediction model output is combined into
Prediction data.After prediction data generation, prediction data is stored into prediction data medium corresponding to prediction data source, is being stored
After, it include the second garbled data and prediction data in prediction data medium.
By embodiment illustrated in fig. 3 it is found that in embodiments of the present invention, obtaining the historical data in prediction data medium,
Big data analysis is carried out to historical data, filters out the first garbled data relevant to target service type in historical data, and
First garbled data is pre-processed, is removed there are the first garbled data of abnormal data, screens number for retain first
According to as the second garbled data, basic data collection is constructed based on the second garbled data, by basic data collection and data processing model
It is fitted, the data processing model that fitting is completed is exported as data prediction model, predict mould finally by data
Type generates prediction data, and prediction data is stored to prediction data medium, and the embodiment of the present invention passes through training data prediction model,
The derivative for realizing data improves the reliability of data cover degree and prediction data.
It shown in Fig. 4, is stored on the basis of the embodiment of the present invention one, and in the corresponding data medium of filling data source
Multiple available datas record relevant to target service type, and each available data record includes Data Identification and multiple
On the basis of available data attribute, to exist missing available data record carry out completion process refine after obtain one
Kind implementation method.The embodiment of the invention provides the implementation flow charts of the automatic test approach based on big data analysis, as schemed institute
Show, being somebody's turn to do the automatic test approach based on big data analysis may comprise steps of:
In S401, multiple available data records are traversed to find out target data record, the target data note
It records the data under target data attribute and there is missing, the target data attribute is one in the multiple available data attribute
It is a or multiple.
Available data record is identical as the concept of above-mentioned historical data, is intended merely to distinguish in the upper difference of name different
Data medium and data source.Similarly, during data inputting and data store, it is possible to because error leads to filler
It is lacked according to the data stored in the corresponding filling data medium in source.And usually, available data record includes multiple existing
Have a data attribute, for example, the corresponding multiple available data attributes of insurance business may include the age, annual income, number in the household and
Insured amount, therefore in embodiments of the present invention, multiple available datas record in traversal filling data medium, to find out target
There is missing in data record, data of the target data record in target data attribute, target data attribute can be multiple existing
There is one or more of data attribute.
In S402, the Data Identification of the target data record is obtained, is recorded from multiple available datas
Found out in multiple Data Identifications with the immediate Data Identification of the Data Identification of the target data record,
And the corresponding available data of the immediate Data Identification is recorded in the data in the target data attribute
As the data lacked in the target data record.
The Data Identification for including in available data record can be set according to practical application scene, and Data Identification can also
Think one or more of multiple available data attributes.After determining target data record, target data record is obtained
Data Identification, and lookup and target data from multiple Data Identifications that multiple available datas except target data record record
The immediate Data Identification of the Data Identification of record.Wherein, immediate Data Identification is the data with target data record
The smallest Data Identification of difference value between mark if Data Identification is one in multiple available data attributes, for example is year
Age, then directly using the difference of data under Data Identification as difference value, for example, under the Data Identification of target data record
Data are 17, and the data under the Data Identification of some available data record are 20, then difference value is 3；If Data Identification is multiple
It is multiple in available data attribute, for example be age and number in the household, then multiple weights can be arranged to Data Identification in advance, and
The difference of data under Data Identification is weighted summation according to multiple weights, obtains difference value, for example, preset
The weight at age is 2, and the weight of number in the household is 1, and the data under the age of target data record are 21, under number in the household
Data be 4, the data under the age of some available data record are 17, and the data under number in the household are 3, then difference value is
2* (21-17)+1* (4-3)=9, it is worth mentioning at this point that, in the case where calculating Data Identification after the difference of data, difference can be carried out
It takes absolute value, then is weighted summation, to prevent the sign symbol of difference from impacting to the accuracy of difference value.
It is noted that exporting change prompt if Data Identification is identical as target data attribute, prompting tester
Data Identification is reset.Find with after the immediate Data Identification of the Data Identification of target data record, really
Determine the corresponding available data record of immediate Data Identification, and the available data is recorded in the data in target data attribute
It is filled in target data record in the data of target data attribute missing.
By embodiment illustrated in fig. 4 it is found that in embodiments of the present invention, traversing multiple existing numbers in prediction data medium
According to record, there is the target data record of missing to find out the data under target data attribute, and obtain target data note
The Data Identification of record is found out and target from multiple Data Identifications that multiple available datas in addition to target data record record
The immediate Data Identification of the Data Identification of data record, and the corresponding available data of the immediate Data Identification is recorded
In the data in target data attribute as the data lacked in target data record, the embodiment of the present invention is scarce by searching for existing
The available data of mistake records, and is filled to the data of missing, improves the integrality of available data record.
It is to indicate the basis of multiple data sources on the basis of the embodiment of the present invention one, and in selection result shown in Fig. 5
On, to the data for extracting the corresponding data source direction of the selection result, as normal data, and it is based on the criterion numeral
A kind of implementation method obtained after being refined according to the process for setting up normal data case.The embodiment of the invention provides based on big
The implementation flow chart of the automatic test approach of data analysis, as shown, being somebody's turn to do the automatic test approach based on big data analysis can
With the following steps are included:
In S501, obtain preset priority sequence, the priority sequence be stored with it is configured multiple described
Data source multiple priority correspondingly.
In embodiments of the present invention, can multiple data sources be pre-configured with priority sequence, include in priority sequence
With multiple data sources multiple priority correspondingly, wherein each data source corresponds to a priority.
In S502, from multiple data sources that the selection result indicates, successively according to the priority sequence
The data of the data source direction of the priority from high to low are extracted as the normal data, and set up multiple marks
Quasi- data case.
In embodiments of the present invention, multiple data sources of selection result instruction are determined, and according to excellent in priority sequence
First grade sequence successively extracts the data that multiple data sources are directed toward, and sets up multiple normal data cases according to the difference of affiliated data source
Example.For example, multiple data sources of selection result instruction are business datum source and prediction data source, and business datum source is preferential
Priority in grade sequence is higher than prediction data source, therefore extracts one normal data case of data and establishment that business datum source is directed toward
Then example extracts the data that prediction data source is directed toward and sets up another normal data case.
In S503, by the data to be tested according to the priority sequence successively with multiple normal data cases
In the normal data be compared, if the mark in the data to be tested and one of them described normal data case
Quasi- comparing success, the then prompt being successfully tested described in output.
After having set up multiple normal data cases, the normal data in data to be tested and normal data case is carried out
It compares, substantially by data to be tested according to the priority orders of priority sequence successively and in multiple normal data cases
Normal data is compared, i.e., after the completion of comparing with the normal data in a normal data case, continues and next mark
Normal data in quasi- data case compares.If the normal data ratio in data to be tested and one of normal data case
To success, then the prompt being successfully tested is exported；If the normal data in data to be tested and multiple normal data cases all compares
To failure, then the prompt of test crash is exported.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
Fig. 6 shows the structural block diagram of terminal device provided in an embodiment of the present invention, each unit which includes
For executing each step in the corresponding embodiment of Fig. 1.It is retouched referring specifically to Fig. 1 is related in embodiment corresponding to Fig. 1
It states.For ease of description, only the parts related to this embodiment are shown.
Referring to Fig. 6, the terminal device includes:
Generation unit 61, for generating full dose data source options, the multiple data based on configured multiple data sources
Source includes business datum relevant to target service type source, prediction data source, filling data source and self-control data source；
Determination unit 62, for obtaining selection result of the tester about the full dose data source options, and determine with
The corresponding data source of the selection result；
Unit 63 is set up, the data being directed toward for extracting the corresponding data source of the selection result, as criterion numeral
According to, and normal data case is set up based on the normal data；
Comparing unit 64, for obtaining data to be tested, and will be in the data to be tested and the normal data case
The normal data be compared, if comparing the prompt for successfully exporting and being successfully tested.
Optionally, the generation unit 61, further includes:
Determination unit, for determine it is corresponding with the target service type handle the page, the page of handling is used for quilt
Operation is to handle the corresponding business of the target service type；
It is inserted into unit, for handling page insertion monitor component described, is obtained by the monitor component by described
The business datum of page input is handled, and the business datum is stored into data medium corresponding to the business datum source.
Optionally, the corresponding data medium of the prediction data source is stored with history number relevant to multiple types of business
According to the generation unit 61, further includes:
Acquiring unit carries out big data analysis for obtaining the historical data, and to the historical data, filters out institute
State the first garbled data relevant to the target service type in historical data；
Pretreatment unit removes that there are described the of abnormal data for pre-processing to first garbled data
One garbled data obtains the second garbled data；
Construction unit, for constructing basic data collection based on second garbled data, by the basic data collection and in advance
If data processing model be fitted, and will fitting complete the data processing model as data prediction model progress it is defeated
Out；
Storage unit, for by the data prediction model generation prediction data, and by the prediction data store to
The corresponding data medium of the prediction data source.
Optionally, the corresponding data medium of the filling data source is stored with relevant multiple to the target service type
Available data record, each available data record includes Data Identification and multiple available data attributes, and the generation is single
Member 61, further includes:
Traversal Unit, for traversing multiple available data records to find out target data record, the number of targets
There is missing according to the data being recorded under target data attribute, the target data attribute is in the multiple available data attribute
One or more；
Searching unit is remembered for obtaining the Data Identification of the target data record from multiple available datas
It is found out and the immediate data of the Data Identification of the target data record in multiple Data Identifications of record
Mark, and the corresponding available data of the immediate Data Identification is recorded in the target data attribute
Data are as the data lacked in the target data record.
Optionally, the selection result indicates multiple data sources, the establishment unit 63, comprising:
Retrieval unit, for obtaining preset priority sequence, the priority sequence be stored with it is configured
Multiple data sources multiple priority correspondingly；
Subelement is set up, for from multiple data sources that the selection result indicates, according to the priority sequence
Leie time extracts the data of the data source direction of the priority from high to low as the normal data, and sets up multiple
The normal data case；
The comparing unit 64, comprising:
Successively comparing unit, for by the data to be tested according to the priority sequence successively with multiple standards
The normal data in data case is compared, if in the data to be tested and one of them described normal data case
The normal data compare successfully, then the prompt that is successfully tested described in output.
Therefore, terminal device provided in an embodiment of the present invention enables tester by generating full dose data source options
The source of enough unrestricted choice normal datas, improves test operation in the applicability of different scenes.
Fig. 7 is the schematic diagram of terminal device provided in an embodiment of the present invention.As shown in fig. 7, the terminal device 7 of the embodiment
Include: processor 70, memory 71 and is stored in the calculating that can be run in the memory 71 and on the processor 70
Machine program 72.The processor 70 realizes above-mentioned each automatic survey based on big data analysis when executing the computer program 72
Step in method for testing embodiment, such as step S101 to S104 shown in FIG. 1.Alternatively, the processor 70 executes the meter
The function of each unit in above-mentioned each terminal device embodiment, such as the function of unit 61 to 64 shown in Fig. 6 are realized when calculation machine program 72
Energy.
Illustratively, the computer program 72 can be divided into one or more units, one or more of
Unit is stored in the memory 71, and is executed by the processor 70, to complete the present invention.One or more of lists
Member can be the series of computation machine program instruction section that can complete specific function, and the instruction segment is for describing the computer journey
Implementation procedure of the sequence 72 in the terminal device 7.For example, the computer program 72 can be divided into generation unit, really
Order member, establishment unit and comparing unit, each unit concrete function are as follows:
Generation unit, for generating full dose data source options, the multiple data source based on configured multiple data sources
Including business datum relevant to target service type source, prediction data source, filling data source and self-control data source；
Determination unit, for obtaining selection result of the tester about the full dose data source options, and determining and institute
State the corresponding data source of selection result；
Unit is set up, the data being directed toward for extracting the corresponding data source of the selection result, as normal data,
And normal data case is set up based on the normal data；
Comparing unit, for obtaining data to be tested, and will be in the data to be tested and the normal data case
The normal data is compared, if comparing the prompt for successfully exporting and being successfully tested.
The terminal device 7 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The terminal device may include, but be not limited only to, processor 70, memory 71.It will be understood by those skilled in the art that Fig. 7
The only example of terminal device 7 does not constitute the restriction to terminal device 7, may include than illustrating more or fewer portions
Part perhaps combines certain components or different components, such as the terminal device can also include input-output equipment, net
Network access device, bus etc..
Alleged processor 70 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 71 can be the internal storage unit of the terminal device 7, such as the hard disk or interior of terminal device 7
It deposits.The memory 71 is also possible to the External memory equipment of the terminal device 7, such as be equipped on the terminal device 7
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..Further, the memory 71 can also both include the storage inside list of the terminal device 7
Member also includes External memory equipment.The memory 71 is for storing needed for the computer program and the terminal device
Other programs and data.The memory 71 can be also used for temporarily storing the data that has exported or will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit division progress for example, in practical application, can according to need and by above-mentioned function distribution by different functions
Unit is completed, i.e., the internal structure of the terminal device is divided into different functional units, to complete whole described above
Or partial function.Each functional unit in embodiment can integrate in one processing unit, be also possible to each unit list
It is solely physically present, can also be integrated in one unit with two or more units, above-mentioned integrated unit can both use
Formal implementation of hardware can also be realized in the form of software functional units.In addition, the specific name of each functional unit also only
It is the protection scope that is not intended to limit this application for the ease of mutually distinguishing.The specific work process of unit in above system,
It can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed terminal device and method can pass through it
Its mode is realized.For example, terminal device embodiment described above is only schematical, for example, the unit is drawn
Point, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can
To combine or be desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or beg for
The mutual coupling or direct-coupling or communication connection of opinion can be through some interfaces, the INDIRECT COUPLING of device or unit
Or communication connection, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-described embodiment side
All or part of the process in method can also instruct relevant hardware to complete, the computer by computer program
Program can be stored in a computer readable storage medium, and the computer program is when being executed by processor, it can be achieved that above-mentioned each
The step of a embodiment of the method.Wherein, the computer program includes computer program code, and the computer program code can
Think source code form, object identification code form, executable file or certain intermediate forms etc..The computer-readable medium can be with
It include: any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disk, light that can carry the computer program code
Disk, computer storage, read-only memory (Read-Only Memory, ROM), random access memory (Random Access
Memory, RAM), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described computer-readable
The content that medium includes can carry out increase and decrease appropriate according to the requirement made laws in jurisdiction with patent practice, such as at certain
A little jurisdictions do not include electric carrier signal and telecommunication signal according to legislation and patent practice, computer-readable medium.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of automatic test approach based on big data analysis characterized by comprising
Full dose data source options are generated based on configured multiple data sources, the multiple data source includes and target service type
Relevant business datum source, prediction data source, filling data source and self-control data source；
Selection result of the tester about the full dose data source options is obtained, and determines institute corresponding with the selection result
State data source；
The data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and are based on the criterion numeral
According to establishment normal data case；
Data to be tested are obtained, and the data to be tested and the normal data in the normal data case are compared
It is right, if comparing the prompt for successfully exporting and being successfully tested.
2. automatic test approach as described in claim 1, which is characterized in that described to be generated based on configured multiple data sources
Before full dose data source options, further includes:
Determination is corresponding with the target service type to handle the page, and the page of handling is for being operable to handle the target
The corresponding business of type of business；
Page insertion monitor component is handled described, is obtained by the monitor component through the business for handling page input
Data, and the business datum is stored into data medium corresponding to the business datum source.
3. automatic test approach as described in claim 1, which is characterized in that the corresponding data medium of the prediction data source is deposited
Historical data relevant to multiple types of business is contained, it is described to generate full dose data source options based on configured multiple data sources
Before, further includes:
Obtain the historical data, and big data analysis carried out to the historical data, filter out in the historical data with institute
State relevant first garbled data of target service type；
First garbled data is pre-processed, removes there are first garbled data of abnormal data, obtains second
Garbled data；
Construct basic data collection based on second garbled data, by the basic data collection and preset data processing model into
Row fitting, and the data processing model that fitting is completed is exported as data prediction model；
Prediction data is generated by the data prediction model, and the prediction data is stored to the prediction data source and is corresponded to
Data medium.
4. automatic test approach as described in claim 1, which is characterized in that the corresponding data medium of the filling data source is deposited
Multiple available data records relevant to the target service type are contained, each available data record includes Data Identification
And multiple available data attributes, it is described generate full dose data source options based on configured multiple data sources before, further includes:
Multiple available data records are traversed to find out target data record, the target data record is in target data category
Property under data there is missing, the target data attribute is one or more of the multiple available data attribute；
The Data Identification for obtaining the target data record, the multiple data marks recorded from multiple available datas
Found out in knowledge with the immediate Data Identification of the Data Identification of the target data record, and will be described closest
The corresponding available data of the Data Identification be recorded in the data in the target data attribute as the number of targets
According to the data lacked in record.
5. automatic test approach as described in claim 1, which is characterized in that if the selection result indicates multiple data
Source, then the data extracting the corresponding data source of the selection result and being directed toward, as normal data, and are based on the mark
Quasi- data set up normal data case, comprising:
Preset priority sequence is obtained, the priority sequence is stored with to be corresponded with configured multiple data sources
Multiple priority；
From multiple data sources that the selection result indicates, the priority is successively extracted according to the priority sequence
The data that the data source from high to low is directed toward set up multiple normal data cases as the normal data；
It is described that the data to be tested are compared with the normal data in the normal data case, if compare at
Function then exports the prompt being successfully tested, comprising:
By the data to be tested according to the priority sequence successively with the standard in multiple normal data cases
Data are compared, if the normal data in the data to be tested and one of them described normal data case is compared into
Function, the then prompt being successfully tested described in output.
6. a kind of terminal device, which is characterized in that the terminal device includes memory, processor and is stored in the storage
In device and the computer program that can run on the processor, the processor are realized as follows when executing the computer program
Step:
Full dose data source options are generated based on configured multiple data sources, the multiple data source includes and target service type
Relevant business datum source, prediction data source, filling data source and self-control data source；
Selection result of the tester about the full dose data source options is obtained, and determines institute corresponding with the selection result
State data source；
The data that the corresponding data source of the selection result is directed toward are extracted, as normal data, and are based on the criterion numeral
According to establishment normal data case；
Data to be tested are obtained, and the data to be tested and the normal data in the normal data case are compared
It is right, if comparing the prompt for successfully exporting and being successfully tested.
7. terminal device as claimed in claim 6, which is characterized in that described to generate full dose based on configured multiple data sources
Before data source options, further includes:
Determination is corresponding with the target service type to handle the page, and the page of handling is for being operable to handle the target
The corresponding business of type of business；
Page insertion monitor component is handled described, is obtained by the monitor component through the business for handling page input
Data, and the business datum is stored into data medium corresponding to the business datum source.
8. terminal device as claimed in claim 6, which is characterized in that the corresponding data medium of the prediction data source is stored with
Historical data relevant to multiple types of business, it is described based on configured multiple data sources generate full dose data source options it
Before, further includes:
Obtain the historical data, and big data analysis carried out to the historical data, filter out in the historical data with institute
State relevant first garbled data of target service type；
First garbled data is pre-processed, removes there are first garbled data of abnormal data, obtains second
Garbled data；
Construct basic data collection based on second garbled data, by the basic data collection and preset data processing model into
Row fitting, and the data processing model that fitting is completed is exported as data prediction model；
Prediction data is generated by the data prediction model, and the prediction data is stored to the prediction data source and is corresponded to
Data medium.
9. terminal device as claimed in claim 6, which is characterized in that the corresponding data medium of the filling data source is stored with
Multiple available datas record relevant to the target service type, each available data record comprising Data Identification and
Multiple available data attributes, it is described generate full dose data source options based on configured multiple data sources before, further includes:
Multiple available data records are traversed to find out target data record, the target data record is in target data category
Property under data there is missing, the target data attribute is one or more of the multiple available data attribute；
The Data Identification for obtaining the target data record, the multiple data marks recorded from multiple available datas
Found out in knowledge with the immediate Data Identification of the Data Identification of the target data record, and will be described closest
The corresponding available data of the Data Identification be recorded in the data in the target data attribute as the number of targets
According to the data lacked in record.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In the step of realization automatic test approach as described in any one of claim 1 to 5 when the computer program is executed by processor
Suddenly.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109829096A (en) * | 2019-03-15 | 2019-05-31 | 北京金山数字娱乐科技有限公司 | A kind of collecting method, device, electronic equipment and storage medium |
| CN110162459A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Test cases generation method, device and computer readable storage medium |
| CN110278201A (en) * | 2019-06-12 | 2019-09-24 | 深圳市腾讯计算机系统有限公司 | Security strategy evaluation method and device, computer-readable medium and electronic equipment |
| CN112749190A (en) * | 2019-10-31 | 2021-05-04 | 中国移动通信集团重庆有限公司 | Data query method and device, computing equipment and computer storage medium |
| CN112765044A (en) * | 2021-04-06 | 2021-05-07 | 上海钐昆网络科技有限公司 | Abnormal data detection method, device, equipment and storage medium |
| CN114968408A (en) * | 2022-05-31 | 2022-08-30 | 中国船舶重工集团公司第七0七研究所 | Configurable marine multi-source multi-channel information screening system and method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060174174A1 (en) * | 2005-02-02 | 2006-08-03 | International Business Machines Corporation | Method, system, and storage medium for estimating and improving test case generation |
| CN101984416A (en) * | 2010-11-02 | 2011-03-09 | 中兴通讯股份有限公司 | Method and device for generating test case |
| CN102999320A (en) * | 2011-09-15 | 2013-03-27 | 阿里巴巴集团控股有限公司 | Automatic test case generation method and system |
| CN104331366A (en) * | 2014-10-30 | 2015-02-04 | 北京思特奇信息技术股份有限公司 | Test case design efficiency promotion method and system |
| CN105302718A (en) * | 2015-10-26 | 2016-02-03 | 浪潮电子信息产业股份有限公司 | Reusable software-oriented test model design method |
| US20160246705A1 (en) * | 2015-02-23 | 2016-08-25 | International Business Machines Corporation | Data fabrication based on test requirements |
| CN106484605A (en) * | 2015-08-28 | 2017-03-08 | 中兴通讯股份有限公司 | A kind of method and system realizing software automated testing |
| CN106991140A (en) * | 2017-03-17 | 2017-07-28 | 国网江苏省电力公司扬州供电公司 | monitoring information table checking method and system |
| CN107291582A (en) * | 2017-06-13 | 2017-10-24 | 常熟天合信电子科技有限公司 | Intelligent integration method of testing based on standard big data |
| CN107885660A (en) * | 2017-10-31 | 2018-04-06 | 平安科技（深圳）有限公司 | Fund system automatic test management method, device, equipment and storage medium |
| CN108319719A (en) * | 2018-02-12 | 2018-07-24 | 腾讯科技（深圳）有限公司 | Database data method of calibration, device, computer equipment and storage medium |
-
2018
- 2018-08-13 CN CN201810915913.4A patent/CN109324960A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060174174A1 (en) * | 2005-02-02 | 2006-08-03 | International Business Machines Corporation | Method, system, and storage medium for estimating and improving test case generation |
| CN101984416A (en) * | 2010-11-02 | 2011-03-09 | 中兴通讯股份有限公司 | Method and device for generating test case |
| CN102999320A (en) * | 2011-09-15 | 2013-03-27 | 阿里巴巴集团控股有限公司 | Automatic test case generation method and system |
| CN104331366A (en) * | 2014-10-30 | 2015-02-04 | 北京思特奇信息技术股份有限公司 | Test case design efficiency promotion method and system |
| US20160246705A1 (en) * | 2015-02-23 | 2016-08-25 | International Business Machines Corporation | Data fabrication based on test requirements |
| CN106484605A (en) * | 2015-08-28 | 2017-03-08 | 中兴通讯股份有限公司 | A kind of method and system realizing software automated testing |
| CN105302718A (en) * | 2015-10-26 | 2016-02-03 | 浪潮电子信息产业股份有限公司 | Reusable software-oriented test model design method |
| CN106991140A (en) * | 2017-03-17 | 2017-07-28 | 国网江苏省电力公司扬州供电公司 | monitoring information table checking method and system |
| CN107291582A (en) * | 2017-06-13 | 2017-10-24 | 常熟天合信电子科技有限公司 | Intelligent integration method of testing based on standard big data |
| CN107885660A (en) * | 2017-10-31 | 2018-04-06 | 平安科技（深圳）有限公司 | Fund system automatic test management method, device, equipment and storage medium |
| CN108319719A (en) * | 2018-02-12 | 2018-07-24 | 腾讯科技（深圳）有限公司 | Database data method of calibration, device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 胡蓉 等: "一种新的基于Web服务超市平台的QoS测试方法", 电子设计工程, no. 03, 5 February 2011 (2011-02-05), pages 104 - 107 * |
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| CN109829096A (en) * | 2019-03-15 | 2019-05-31 | 北京金山数字娱乐科技有限公司 | A kind of collecting method, device, electronic equipment and storage medium |
| CN109829096B (en) * | 2019-03-15 | 2021-11-26 | 北京金山数字娱乐科技有限公司 | Data acquisition method and device, electronic equipment and storage medium |
| CN110162459A (en) * | 2019-04-15 | 2019-08-23 | 深圳壹账通智能科技有限公司 | Test cases generation method, device and computer readable storage medium |
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| CN112749190A (en) * | 2019-10-31 | 2021-05-04 | 中国移动通信集团重庆有限公司 | Data query method and device, computing equipment and computer storage medium |
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