CN103810095B - A kind of method and device of data comparison test - Google Patents
A kind of method and device of data comparison test Download PDFInfo
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- CN103810095B CN103810095B CN201210459305.XA CN201210459305A CN103810095B CN 103810095 B CN103810095 B CN 103810095B CN 201210459305 A CN201210459305 A CN 201210459305A CN 103810095 B CN103810095 B CN 103810095B
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Abstract
The invention provides a kind of method and device of data comparison test, the method for wherein data comparison test includes：A. the sample data of one or more is respectively sent to tested module and base modules, wherein after the tested module and the base modules are handled the sample data received, exports respective processing daily record respectively；B. according to the transformation rule being pre-configured with, the respective processing daily record of the tested module and the base modules is converted into respective eigenmatrix；C. according to the difference rule being pre-configured with, the matrix of differences between the eigenmatrix of the tested module and the eigenmatrix of the base modules is obtained；D. in the matrix of differences element carry out it is extensive, and, will be extensive after matrix of differences in mutually colleague merge.By the above-mentioned means, the precision of test can be improved.
Description
【Technical field】
The present invention relates to measuring technology, more particularly to a kind of method and device of data comparison test.
【Background technology】
Contrast test is a kind of common method of testing.Embodiments thereof is：Tested module and base modules are placed in phase
With test environment in, and using identical test data respectively as tested module and base modules input, with compare by
Survey module and base modules each export between otherness verify whether tested module meets expected design.Wherein, base
Quasi-mode block is the module for being contrasted with tested module, such as the module of issue is upgraded, to the mould after upgrading
When block carries out contrast test, the module before upgrading is exactly base modules, and the module after upgrading is exactly tested module.To computation-intensive
Pattern block and Legacy System take contrast test method often can more implement than carrying out Proactive authentication test according to Functional Design
Property, also effectively.
Contrast test usually requires substantial amounts of test data, could make it that the Test coverage of tested module is abundant enough, but
It is when the quantity of test data is very big, the data of tested module or base modules output are also quite huge, and tester is to huge
Big output data carries out the task that analysis one by one is practically impossible to complete.In existing contrast test, for substantial amounts of
Output data, tester are typically to determine whether tested module meets using the method that analysis is sampled to output data
It is expected.
Sampling analysis is due to can not analyze all data, it is possible to the problem of data omission, statistical number be present
According to showing, expected outer difference caused by 2/10000ths data, it is possible to up to being all difficult to find in the time of 2 years.
As can be seen that, because the result of output is difficult to effectively be analyzed, therefore there is test in existing contrast test method
The problem of precision difference.
【The content of the invention】
The technical problems to be solved by the invention are to provide a kind of method and device of data comparison test, to improve test
Precision.
The present invention is to provide a kind of method of data comparison test, bag to solve the technical scheme that technical problem uses
Include：A. the sample data of one or more is respectively sent to tested module and base modules, wherein the tested module and described
After base modules are handled the sample data received, respective processing daily record is exported respectively；B. basis is pre-configured with
Transformation rule, the respective processing daily record of the tested module and the base modules is converted into respective eigenmatrix；C. root
According to the difference rule being pre-configured with, obtain between the eigenmatrix of the tested module and the eigenmatrix of the base modules
Matrix of differences；D. in the matrix of differences element carry out it is extensive, and, will be extensive after matrix of differences in mutually colleague close
And.
According to one of present invention preferred embodiment, every record of the processing daily record includes a sample data, and
The result of at least one dimension obtained by the sample data.
According to one of present invention preferred embodiment, what one sample data of each element representation of the eigenmatrix obtained
The result of one dimension, also, correspond to same sample data with the element of a line, the element of same row correspond to same
The result of dimension.
Specifically wrapped according to one of present invention preferred embodiment, the step of " being carried out to the element in the matrix of differences extensive "
Include：For each element in the matrix of differences, the abstraction rule table being pre-configured with is searched, is had when in the abstraction rule table
It is during the application rule of the element, the element is extensive according to application rule progress.
It is specific the step of " the mutually colleague in the matrix of differences after will be extensive merges " according to one of present invention preferred embodiment
Including：Same row element in matrix of differences after will be extensive is spliced；The feature of the row is calculated spliced each row respectively
Value；Characteristic value identical row is merged.
Present invention also offers a kind of device of data comparison test, including：Log acquisition unit, for by one or more
Sample data be respectively sent to tested module and base modules, wherein the tested module and the base modules are to receiving
Sample data handled after, export respective processing daily record respectively；Conversion unit, for according to the conversion rule being pre-configured with
Then, the respective processing daily record of the tested module and the base modules is converted into respective eigenmatrix；
Difference acquiring unit, for according to the difference rule being pre-configured with, obtain the eigenmatrix of the tested module with
Matrix of differences between the eigenmatrix of the base modules；Extensive unit, for being carried out to the element in the matrix of differences
It is extensive；Combining unit, merge for the mutually colleague in the matrix of differences after will be extensive.
According to one of present invention preferred embodiment, every record of the processing daily record includes a sample data, and,
The result of at least one dimension obtained by the sample data.
According to one of present invention preferred embodiment, what one sample data of each element representation of the eigenmatrix obtained
The result of one dimension, also, correspond to same sample data with the element of a line, the element of same row correspond to same
The result of dimension.
According to one of present invention preferred embodiment, the extensive unit carries out extensive to the element in the matrix of differences
Mode specifically includes：For each element in the matrix of differences, the abstraction rule table being pre-configured with is searched, when described extensive
It is when having the application rule of the element in rule list, the element is extensive according to application rule progress.
According to one of present invention preferred embodiment, the combining unit specifically includes：Concatenation unit, after will be extensive
Same row element in matrix of differences is spliced；Computing unit, for calculating spliced each row the characteristic value of the row respectively；
Row combining unit, for being merged to characteristic value identical row.
As can be seen from the above technical solutions, the present invention is by by the processing of tested module in contrast test and base modules
Daily record is converted into eigenmatrix by way of matrix modeling respectively, and passes through the eigenmatrix of tested module and base modules
Eigenmatrix obtains matrix of differences, can carry out automatic data analysis to matrix of differences, each row member wherein in matrix of differences
Element sorts out duplicate removal by data generaliza-tion, can effectively simplify, therefore, of the invention compared with traditional contrast test, no matter tests
The quantity of data has much, can accomplish the data analysis of full dose, rather than tested mould is observed by way of sampling of data
Whether block meets expection, and this not only significantly reduces the degree of manpower intervention in test process, can also improve the essence of test
Accuracy.
【Brief description of the drawings】
Fig. 1 is the schematic flow sheet of the embodiment for the method that comparing is tested in the present invention；
Fig. 2 a are the schematic diagram of the eigenmatrix of tested module in the present invention；
Fig. 2 b are the schematic diagram of the eigenmatrix of base modules in the present invention；
Fig. 3 is the schematic diagram of matrix of differences in the present invention；
Schematic diagrames of the Fig. 4 for matrix of differences in the present invention after extensive；
Fig. 5 is the structural schematic block diagram of the embodiment for the device that comparing is tested in the present invention；
Fig. 6 is the structural schematic block diagram of the embodiment of combining unit 205 in the present invention.
【Embodiment】
In order that the object, technical solutions and advantages of the present invention are clearer, below in conjunction with the accompanying drawings with specific embodiment pair
The present invention is described in detail.
Fig. 1 is refer to, Fig. 1 is the schematic flow sheet of the embodiment for the method that data comparison is tested in the present invention.Such as Fig. 1 institutes
Show, this method includes：
Step S101：The sample data of one or more is respectively sent to tested module and base modules, wherein tested mould
After block and base modules are handled the sample data received, respective processing daily record is exported respectively.
Step S102：According to the transformation rule being pre-configured with, the respective processing daily record of tested module and base modules is turned
Turn to respective eigenmatrix.
Step S103：According to the difference rule being pre-configured with, the eigenmatrix of tested module and the spy of base modules are obtained
Levy the matrix of differences between matrix；
Step S104：To in matrix of differences element carry out it is extensive, and, will be extensive after matrix of differences in mutually colleague
Merge.
Above-mentioned steps are specifically described below.
In step S101, a sample data is the base required for tested module or base modules completion single treatment process
Notebook data unit.For example, the function of tested module or base modules is that the page is classified, its primitive is one
The URL addresses of the page.
In the present invention, it is identical to send to the sample data of tested module and base modules, i.e. same sample data
Tested module and base modules can be respectively sent to., will be defeated after tested module is handled each bar sample data received
Go out the processing daily record of oneself, after base modules are handled each bar sample data received, can also export the processing of oneself
Daily record.
In above-mentioned processing daily record, every record includes a sample data, and is obtained at least by the sample data
The result of one dimension.
It refer to following pseudo-code block：
Above-mentioned modules A can be tested module or base modules.
Corresponding with above-mentioned false code, the processing daily record of tested module refer to following log recording：
1st article of record：url=www.sina.com,redir=null,type=A,value=10,weight=30
2nd article of record：…
3rd article of record：…
Corresponding with above-mentioned false code, the processing daily record of base modules refer to following log recording：
1st article of record：
url=www.sina.com,redir=www.sina.com/index.html,type=C,value=20,weight
=40
2nd article of record：…
3rd article of record：…
In above-mentioned log recording, " url " field corresponding record is exactly sample data, " redir ", " type ",
" value ", " weight " field corresponding record be exactly a dimension result.
In step S102, according to the processing daily record of tested module, it can convert to obtain the eigenmatrix of tested module, according to
The processing daily record of base modules, it can convert to obtain the eigenmatrix of base modules.
Specifically, each element in eigenmatrix, the result for the dimension that a sample data obtains is represented,
Also, the element with a line corresponds to same sample data, and the element of same row corresponds to the result of same dimension.
When the field handled in daily record is converted into the element in eigenmatrix, according to the field type being pre-configured with,
Search transformation rule corresponding with the type, you can obtain the result during element that the field is converted into eigenmatrix.Assuming that
Field type in the tested module of above-mentioned signal and the processing daily record of base modules is both configured to primary type, corresponding primary
The transformation rule of type is directly using the content of the field as the element content in eigenmatrix, then by above-mentioned tested module
The eigenmatrix that processing daily record obtains refers to Fig. 2 a, and the eigenmatrix obtained by the processing daily record of said reference module refers to
Fig. 2 b.
Each field in processing daily record can also be respectively configured as different types in advance, corresponding each type, there is one
Kind transformation rule.The transformation rule that can be used in the present invention can be found in table 1：
Table 1
By step S102, obtained two eigenmatrixes, in step s 103, then can according to difference rule determine this two
Matrix of differences between individual eigenmatrix.
Each element content in matrix of differences is that the eigenmatrix of tested module and the eigenmatrix of base modules correspond to
Difference between the element of position.
Table 2 is refer to, table 2 is adoptable difference rule declaration in the present invention：
Table 2
By taking two matrixes shown in Fig. 2 a and Fig. 2 b as an example, it is assumed that primary difference, matrix are configured to matrix first row
Two row are configured to similarities and differences difference, and the row of matrix the 3rd are configured to Hamming difference, and the row of matrix the 4th are configured to distance difference, then Fig. 2 a and
The element of the first row takes difference as follows in Fig. 2 b：
First column element difference is null | www.sina.com/index.html（By null and www.sina.com/
Index.html is spliced）, the second column element difference is 1（Because A and C is different）, the 3rd column element difference is 4（Because 10
Binary number be 00001010,20 binary number be the 00010100, the 4th to the 7th difference, different digits is 4）, the
Four column element differences are -10（Because 30-40=- 10）.
The generation process of a row element in matrix of differences is foregoing illustrated, according to similar process, matrix of differences can
To obtain some row elements.Fig. 3 is refer to, Fig. 3 is the schematic diagram of matrix of differences in the present invention.
It is extensive to the element progress in matrix of differences in step S 104, specifically include：
For each element in matrix of differences, the abstraction rule table being pre-configured with is searched, is had when in the abstraction rule table
It is during the application rule of the element, the element is extensive according to application rule progress.
Abstraction rule can represent with regular expression, such as by " [0-9a-zA-Z /]->This canonical of SOME_URL "
Fig. 3 matrix, can be generalized for the form shown in Fig. 4 by the abstraction rule that expression formula represents.
After extensive to matrix of differences progress, it is understood that there may be some identical rows, due to the test data number in contrast test
Measure huge, therefore matrix of differences may include the data of ten million row, if the columns of matrix of differences is also a lot, by directly right
The mode that often each element of row is compared, which merges, mutually goes together, and the computing resource of consuming and time are all huge.
It is introduced below to merging the mode mutually gone together in step S104 of the present invention.Specifically, the difference after will be extensive
The step of mutually colleague in matrix merges includes：
Step S1041：Same row element in matrix of differences after will be extensive is spliced.
Step S1042：The characteristic value of the row is calculated spliced each row respectively.
Step S1043：Characteristic value identical row is merged.
Such as the first row of the matrix of differences shown in Fig. 4, obtained after each element is spliced " null | SOME_URL14-10 ",
Then characteristic value is asked using MD5 algorithms to the splicing string, and this feature value is stored in Hash table.It is appreciated that include identical member
The row of element, its characteristic value tried to achieve is identical.Every row element in matrix of differences is spliced successively and seeks characteristic value, and
When characteristic value is stored in into Hash table, determine whether have this feature value in table, if it is, will be current corresponding to this feature value
Row abandons, so as to realize the purpose quickly merged to characteristic value identical row.
Exported the matrix of differences after merging as test result, and in output, be highlighted difference element（I.e. by
The difference member that the eigenmatrix of tested module and the eigenmatrix of base modules obtain in the element that correspondence position has differences
Element）, tester can be helped quickly to determine to cause tested module and base modules the sample data of difference and the sample occur
The variance data stream of data（The result of i.e. one dimension）, so, tester's can is further to the data of difference
Stream is analyzed, to determine that tested module whether there is defect.
Fig. 5 is refer to, Fig. 5 is the structural schematic block diagram of the embodiment for the device that comparing is tested in the present invention.Such as Fig. 5
Shown, the device includes：Log acquisition unit 201, conversion unit 202, difference acquiring unit 203, extensive unit 204 and merging
Unit 205.
Wherein, log acquisition unit 201, for the sample data of one or more to be respectively sent into tested module and benchmark
After module, wherein tested module and base modules are handled the sample data received, respective processing day is exported respectively
Will.
One sample data is the primitive required for tested module or base modules completion single treatment process.
For example, the function of tested module or base modules is that the page is classified, its primitive is the URL of a page
Location.
It is identical that log acquisition unit 201, which is sent to the sample data of tested module and base modules, i.e., same galley proof
Notebook data can be respectively sent to tested module and base modules.Tested module is handled each bar sample data received
Afterwards, the processing daily record of oneself will be exported, after base modules are handled each bar sample data received, can also export oneself
Processing daily record.
In the processing daily record that log acquisition unit 201 exports, every record includes a sample data, and by the sample
The result at least one dimension that data obtain.
It refer to following pseudo-code block：
Above-mentioned modules A can be tested module or base modules.
Corresponding with above-mentioned false code, the processing daily record of tested module refer to following log recording：
1st article of record：url=www.sina.com,redir=null,type=A,value=10,weight=30
2nd article of record：…
3rd article of record：…
Corresponding with above-mentioned false code, the processing daily record of base modules refer to following log recording：
1st article of record：
url=www.sina.com,redir=www.sina.com/index.html,type=C,value=20,weight
=40
2nd article of record：…
3rd article of record：…
In above-mentioned log recording, " url " field corresponding record is exactly sample data, " redir ", " type ",
" value ", " weight " field corresponding record be exactly a dimension result.
Conversion unit 202, the transformation rule being pre-configured with for basis, by tested module and the respective processing of base modules
Daily record is converted into respective eigenmatrix.
Specifically, each element in eigenmatrix, the result for the dimension that a sample data obtains is represented,
Also, the element with a line corresponds to same sample data, and the element of same row corresponds to the result of same dimension.
When the field handled in daily record is converted into the element in eigenmatrix, according to the field type being pre-configured with,
Search transformation rule corresponding with the type, you can obtain the result during element that the field is converted into eigenmatrix.Assuming that
Field type in the tested module of above-mentioned signal and the processing daily record of base modules is both configured to primary type, corresponding primary
The transformation rule of type is directly using the content of the field as the element content in eigenmatrix, then by above-mentioned tested module
The eigenmatrix that processing daily record obtains refers to Fig. 2 a, and the eigenmatrix obtained by the processing daily record of said reference module refers to
Fig. 2 b.
Each field in processing daily record can also be respectively configured as different types in advance, corresponding each type, there is one
Kind transformation rule.The transformation rule that can be used in the present invention can be found in table 1.
Difference acquiring unit 203, for according to the difference rule being pre-configured with, obtaining the eigenmatrix and base of tested module
Matrix of differences between the eigenmatrix of quasi-mode block.
Each element content in matrix of differences is that the eigenmatrix of tested module and the eigenmatrix of base modules correspond to
Difference between the element of position.
Adoptable difference rule can be found in table 2 in the present invention.
By taking two matrixes shown in Fig. 2 a and Fig. 2 b as an example, it is assumed that primary difference, matrix are configured to matrix first row
Two row are configured to similarities and differences difference, and the row of matrix the 3rd are configured to Hamming difference, and the row of matrix the 4th are configured to distance difference, then Fig. 2 a and
The element of the first row takes difference as follows in Fig. 2 b：
First column element difference is null | www.sina.com/index.html（By null and www.sina.com/
Index.html is spliced）, the second column element difference is 1（Because A and C is different）, the 3rd column element difference is 4（Because 10
Binary number be 00001010,20 binary number be the 00010100, the 4th to the 7th difference, different digits is 4）, the
Four column element differences are -10（Because 30-40=- 10）.
The generation process of a row element in matrix of differences is foregoing illustrated, according to similar process, matrix of differences can
To obtain some row elements.Fig. 3 is refer to, Fig. 3 is the schematic diagram of matrix of differences in the present invention.
Extensive unit 204, it is extensive for being carried out to the element in matrix of differences.Specifically, extensive unit 204 is to difference square
Element in battle array, which carries out extensive mode, to be included：For each element in matrix of differences, the abstraction rule being pre-configured with is searched
Table, it is when having the application rule of the element in the abstraction rule table, the element is extensive according to application rule progress.
Combining unit 205, merge for the mutually colleague in the matrix of differences after will be extensive.Matrix of differences is carried out extensive
Afterwards, it is understood that there may be some identical rows, due to the test data enormous amount in contrast test, therefore matrix of differences may include
The data of ten million row, if the columns of matrix of differences is also a lot, pass through the side being directly compared to each element of every row
Formula, which merges, mutually goes together, and the computing resource of consuming and time are all huge.
It shown below is a kind of embodiment of combining unit 205.
Fig. 6 is refer to, Fig. 6 is the structural schematic block diagram of the embodiment of combining unit 205 in the present invention.As shown in fig. 6, close
And unit 205 includes：Concatenation unit 2051, computing unit 2052 and row combining unit 2053.Wherein concatenation unit 2051, is used for
Same row element in matrix of differences after will be extensive is spliced.Computing unit 2052, by spliced each row respectively based on
Calculate the characteristic value of the row.Row combining unit 2053, for being merged to characteristic value identical row.
Such as the first row of the matrix of differences shown in Fig. 4, concatenation unit 2051 obtained after each element is spliced " null |
SOME_URL14-10 ", then computing unit 2052 characteristic value is asked using MD5 algorithms to the splicing string, and by row combining unit
This feature value is stored in Hash table by 2053.It is appreciated that the row comprising identical element, its characteristic value tried to achieve is identical.Spell
Order member 2051 and computing unit 2052 are spliced to every row element in matrix of differences and seek characteristic value successively, and conjunction of being expert at
And unit 2053 determines in table whether to have this feature value when characteristic value is stored in into Hash table, if it is, by this feature value pair
The current line answered abandons, so as to realize the purpose quickly merged to characteristic value identical row.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
God any modification, equivalent substitution and improvements done etc., should be included within the scope of protection of the invention with principle.
Claims (10)
1. a kind of method of data comparison test, including：
A. the sample data of one or more is respectively sent to tested module and base modules, wherein the tested module and described
After base modules are handled the sample data received, respective processing daily record is exported respectively；
B. according to the transformation rule being pre-configured with, the respective processing daily record of the tested module and the base modules is converted into
Respective eigenmatrix；
C. according to the difference rule being pre-configured with, the eigenmatrix of the tested module and the feature square of the base modules are obtained
Matrix of differences between battle array；
D. it is extensive to the element progress in the matrix of differences according to the abstraction rule table being pre-configured with, and, after extensive
Mutually colleague in matrix of differences merges.
2. according to the method for claim 1, it is characterised in that every record of the processing daily record includes a sample number
According to, and the result of at least one dimension obtained by the sample data.
3. according to the method for claim 2, it is characterised in that one sample number of each element representation of the eigenmatrix
Same sample data, the element pair of same row are corresponded to according to the result of an obtained dimension, also, with the element of a line
Answer the result of same dimension.
4. according to the method for claim 1, it is characterised in that " according to the abstraction rule table being pre-configured with to the difference
Element in matrix carries out extensive " the step of specifically include：
For each element in the matrix of differences, the abstraction rule table being pre-configured with is searched, when in the abstraction rule table
It is when having the application rule of the element, the element is extensive according to application rule progress.
5. according to the method for claim 1, it is characterised in that " the mutually colleague in the matrix of differences after will be extensive merges "
Step specifically includes：
Same row element in matrix of differences after will be extensive is spliced；
The characteristic value of the row is calculated spliced each row respectively；
Characteristic value identical row is merged.
6. a kind of device of data comparison test, including：
Log acquisition unit, for the sample data of one or more to be respectively sent into tested module and base modules, wherein institute
State tested module and after the base modules are handled the sample data received, export respective processing daily record respectively；
Conversion unit, the transformation rule being pre-configured with for basis, by the tested module and the respective place of the base modules
Reason daily record is converted into respective eigenmatrix；
Difference acquiring unit, for according to the difference rule being pre-configured with, obtain the eigenmatrix of the tested module with it is described
Matrix of differences between the eigenmatrix of base modules；
Extensive unit, it is extensive for being carried out according to the abstraction rule table being pre-configured with to the element in the matrix of differences；
Combining unit, merge for the mutually colleague in the matrix of differences after will be extensive.
7. device according to claim 6, it is characterised in that every record of the processing daily record includes a sample number
According to, and, the result of at least one dimension obtained by the sample data.
8. device according to claim 7, it is characterised in that one sample number of each element representation of the eigenmatrix
Same sample data, the element pair of same row are corresponded to according to the result of an obtained dimension, also, with the element of a line
Answer the result of same dimension.
9. device according to claim 6, it is characterised in that the extensive unit is according to the abstraction rule table being pre-configured with
Extensive mode is carried out to the element in the matrix of differences to specifically include：
For each element in the matrix of differences, the abstraction rule table being pre-configured with is searched, when in the abstraction rule table
It is when having the application rule of the element, the element is extensive according to application rule progress.
10. device according to claim 6, it is characterised in that the combining unit specifically includes：
Concatenation unit, spliced for the same row element in the matrix of differences after will be extensive；
Computing unit, for calculating spliced each row the characteristic value of the row respectively；
Row combining unit, for being merged to characteristic value identical row.
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