CN103810095A - Data comparison test method and device - Google Patents
Data comparison test method and device Download PDFInfo
- Publication number
- CN103810095A CN103810095A CN201210459305.XA CN201210459305A CN103810095A CN 103810095 A CN103810095 A CN 103810095A CN 201210459305 A CN201210459305 A CN 201210459305A CN 103810095 A CN103810095 A CN 103810095A
- Authority
- CN
- China
- Prior art keywords
- matrix
- differences
- sample data
- row
- extensive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention provides a data comparison test method and device, wherein the data contrast test method comprises the steps: A. respectively sending more than one piece of sample data to a measured module and a benchmark module, wherein the measured module and the benchmark module respectively output respective processing logs after processing the received sample data; B. according to pre-configured transformation rules, transforming the respective processing logs of the measured module and the benchmark module into respective characteristic matrices thereof; C. according to pre-configured difference rules, obtaining a difference matrix between the characteristic matrix of the measured module and the characteristic matrix of the benchmark module; D. generalizing elements in the difference matrix, and merging a same line in the generalized difference matrix. By applying the way, accuracy of the test can be improved.
Description
[technical field]
The present invention relates to measuring technology, particularly a kind of method and device of Data Comparison test.
[background technology]
Contrast test is a kind of common method of testing.Its embodiment is: tested module and base modules are placed in to identical test environment, and adopting identical test data respectively as the input of tested module and base modules, the otherness between exporting separately with comparison tested module and base modules verifies whether tested module meets the design of expection.Wherein, base modules is the module for contrasting with tested module, for example, the module of issuing is upgraded, and when the module after upgrading is carried out to contrast test, the module before upgrading is exactly base modules, and the module after upgrading is exactly tested module.To computation-intensive module and Legacy System take contrast test method often beguine carry out Proactive authentication test according to Functional Design and have more exploitativeness, also more efficient.
Contrast test needs a large amount of test datas conventionally, just can make the Test coverage of tested module enough abundant, but in the time that the quantity of test data is very large, the data of tested module or base modules output are also quite huge, and it is almost impossible mission that tester analyzes one by one to huge output data.In existing contrast test, for a large amount of output data, tester normally adopts method that output data are carried out to sampling analysis to determine whether tested module meets expection.
Due to can not be to all data analysis, likely there is the problem of data omission in sampling analysis, statistics shows, difference outside the expection that 2/10000ths data cause is likely all difficult to find within the time that reaches 2 years.
Can find out that existing contrast test method, because the result of output is difficult to effectively be analyzed, therefore exists the poor problem of accurate testing degree.
[summary of the invention]
Technical matters to be solved by this invention is to provide a kind of method and device of Data Comparison test, to improve the precision of test.
The present invention is that the technical scheme that technical solution problem adopts is to provide a kind of method that Data Comparison is tested, comprise: the sample data of one or more is sent to respectively tested module and base modules by A., after wherein said tested module and described base modules are processed the sample data receiving, output processing daily record separately respectively; B. according to pre-configured transformation rule, described tested module and the processing daily record separately of described base modules are converted into eigenmatrix separately; C. according to pre-configured difference rule, obtain the matrix of differences between the eigenmatrix of described tested module and the eigenmatrix of described base modules; D. the element in described matrix of differences is carried out extensive, and, colleague mutually in the matrix of differences after extensive is merged.
The preferred embodiment one of according to the present invention, every record of described processing daily record comprises a sample data, and the result of at least one dimension being obtained by this sample data.
The preferred embodiment one of according to the present invention, the result of the dimension that sample data of each element representation of described eigenmatrix obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
The preferred embodiment one of according to the present invention, the step of " carrying out extensive to the element in described matrix of differences " specifically comprises: for the each element in described matrix of differences, search pre-configured abstraction rule table, in the time having the application rule of this element in described abstraction rule table, this element is carried out extensive according to described application rule.
The preferred embodiment one of according to the present invention, the step of " colleague mutually in the matrix of differences after extensive is merged " specifically comprises: the same row element in the matrix of differences after extensive is spliced; Spliced each row is calculated respectively to the eigenwert of this row; The row identical to eigenwert merges.
The present invention also provides a kind of device of Data Comparison test, comprise: log acquisition unit, for the sample data of one or more is sent to respectively to tested module and base modules, after wherein said tested module and described base modules are processed the sample data receiving, output processing daily record separately respectively; Conversion unit, for according to pre-configured transformation rule, is converted into eigenmatrix separately by described tested module and the processing daily record separately of described base modules;
Difference acquiring unit, for according to pre-configured difference rule, obtains the matrix of differences between the eigenmatrix of described tested module and the eigenmatrix of described base modules; Extensive unit, for carrying out extensive to the element of described matrix of differences; Merge cells, for merging the colleague mutually of the matrix of differences after extensive.
The preferred embodiment one of according to the present invention, every record of described processing daily record comprises a sample data, and, the result of at least one dimension being obtained by this sample data.
The preferred embodiment one of according to the present invention, the result of the dimension that sample data of each element representation of described eigenmatrix obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
The preferred embodiment one of according to the present invention, described extensive unit carries out extensive mode to the element in described matrix of differences and specifically comprises: for the each element in described matrix of differences, search pre-configured abstraction rule table, in the time having the application rule of this element in described abstraction rule table, this element is carried out extensive according to described application rule.
The preferred embodiment one of according to the present invention, described merge cells specifically comprises: concatenation unit, for the same row element of the matrix of differences after extensive is spliced; Computing unit, for calculating respectively the eigenwert of this row to spliced each row; Row merge cells, for merging the identical row of eigenwert.
As can be seen from the above technical solutions, the present invention is by being converted into eigenmatrix by the mode of matrix modeling respectively by the processing daily record of tested module in contrast test and base modules, and obtain matrix of differences by the eigenmatrix of tested module and the eigenmatrix of base modules, can carry out automatic data analysis to matrix of differences, wherein the each row element in matrix of differences is by the extensive classification duplicate removal of data, can effectively simplify, therefore, the present invention is compared with traditional contrast test, no matter the quantity of test data has much, can accomplish the data analysis of full dose, rather than observe tested module by the mode of sampling of data and whether meet expection, this has not only greatly reduced the degree of manpower intervention in test process, also can improve the precision of test.
[accompanying drawing explanation]
Fig. 1 is the schematic flow sheet of the embodiment of the method for comparing test in the present invention;
Fig. 2 a is the schematic diagram of the eigenmatrix of tested module in the present invention;
Fig. 2 b is 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;
Fig. 4 is the schematic diagram of matrix of differences after extensive in the present invention;
Fig. 5 is the structural representation block diagram of the embodiment of the device of comparing test in the present invention;
Fig. 6 is the structural representation block diagram of the embodiment of merge cells 205 in the present invention.
[embodiment]
In order to make the object, technical solutions and advantages of the present invention clearer, describe the present invention below in conjunction with the drawings and specific embodiments.
Please refer to Fig. 1, Fig. 1 is the schematic flow sheet of the embodiment of the method for Data Comparison test in the present invention.As shown in Figure 1, the method comprises:
Step S101: the sample data of one or more is sent to respectively to tested module and base modules, after wherein tested module and base modules are processed the sample data receiving, output processing daily record separately respectively.
Step S102: according to pre-configured transformation rule, tested module and base modules processing daily record are separately converted into eigenmatrix separately.
Step S103: according to pre-configured difference rule, obtain the matrix of differences between the eigenmatrix of tested module and the eigenmatrix of base modules;
Step S104: the element in matrix of differences is carried out extensive, and, colleague mutually in the matrix of differences after extensive is merged.
Below above-mentioned steps is specifically described.
In step S101, a sample data is that tested module or base modules complete the needed primitive of single treatment process.For example, the function of tested module or base modules is that the page is classified, and its primitive is the URL address of a page.
In the present invention, the sample data that is sent to tested module and base modules is identical, and same sample data can be sent to respectively tested module and base modules.After tested module is processed each the sample data receiving, by the processing daily record of output oneself, after base modules is processed each the sample data receiving, also can export the processing daily record of oneself.
In above-mentioned processing daily record, every record comprises a sample data, and the result of at least one dimension being obtained by this sample data.
Please refer to false code piece below:
Above-mentioned modules A can be tested module or base modules.
Corresponding with above-mentioned false code, the processing daily record of tested module please refer to log recording below:
Article 2, record:
Article 3, record:
Corresponding with above-mentioned false code, the processing daily record of base modules please refer to log recording below:
url=www.sina.com,redir=www.sina.com/index.html,type=C,value=20,weight=40
Article 2, record:
Article 3, record:
In above-mentioned log recording, " url " field corresponding record be exactly sample data, " redir ", " type ", " value ", " weight " field corresponding record be exactly the result of a dimension.
In step S102, according to the processing daily record of tested module, the eigenmatrix that obtains tested module can be transformed, according to the processing daily record of base modules, the eigenmatrix that obtains base modules can be transformed.
Particularly, the each element in eigenmatrix, represents the result of the dimension that a sample data obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
When processing field in daily record and be converted into the element in eigenmatrix, according to pre-configured field type, search the transformation rule corresponding with the type, the result can obtain this field and be converted into the element in eigenmatrix time.Suppose that the field type in the tested module of above-mentioned signal and the processing daily record of base modules is all configured to primary type, the transformation rule of corresponding primary type is the direct element content in eigenmatrix using the content of this field, the eigenmatrix being obtained by the processing daily record of above-mentioned tested module can be with reference to figure 2a, and the eigenmatrix being obtained by the processing daily record of said reference module can be with reference to figure 2b.
Each field of processing in daily record also can be configured to respectively different types in advance, corresponding every type, all has a kind of transformation rule.The transformation rule that can adopt in the present invention can be referring to table 1:
Table 1
By step S102, obtain two eigenmatrixes, in step S103, can determine the matrix of differences between these two eigenmatrixes according to difference rule.
Each element content in matrix of differences is the difference between the eigenmatrix of tested module and the element of the eigenmatrix correspondence position of base modules.
Please refer to table 2, table 2 is adoptable difference rule declarations in the present invention:
Table 2
Take two matrixes shown in Fig. 2 a and Fig. 2 b as example, suppose matrix first row to be configured to primary difference, matrix secondary series is configured to similarities and differences difference, and matrix the 3rd row are configured to Hamming difference, matrix the 4th row are configured to apart from difference, in Fig. 2 a and Fig. 2 b the element of the first row to get difference as follows:
First row element difference is that null|www.sina.com/index.html(is spliced by null and www.sina.com/index.html), secondary series element difference is that 1(is because A is different with C), the 3rd column element difference is that 4(is because 10 binary number is 00001010,20 binary number is 00010100, the the 4th to the 7th difference, different figure places is 4), the 4th column element difference is-10(is because 30-40=-10).
Below the production process that for example understands a row element in matrix of differences, according to similar process, matrix of differences can obtain several rows element.Please refer to Fig. 3, Fig. 3 is the schematic diagram of matrix of differences in the present invention.
In step S 104, the element in matrix of differences is carried out extensive, comprises particularly:
For the each element in matrix of differences, search pre-configured abstraction rule table, in the time having the application rule of this element in this abstraction rule table, this element is carried out extensive according to application rule.
Abstraction rule can represent with regular expression, for example by " [0-9a-zA-Z. /]->SOME_URL " abstraction rule that represents of this regular expression, can by extensive the matrix of Fig. 3 be the form shown in Fig. 4.
To matrix of differences carry out extensive after, may there are some identical row, due to the test data enormous amount in contrast test, therefore matrix of differences may comprise the data of ten million row, if when the columns of matrix of differences is also a lot, merge colleague mutually by the mode directly each element of every row being compared, the computational resource expending and time are all huge.
Be introduced merging colleague's mode mutually in step S104 of the present invention below.Particularly, the step that colleague merges mutually in the matrix of differences after extensive is comprised:
Step S1041: the same row element in the matrix of differences after extensive is spliced.
Step S1042: the eigenwert of spliced each row being calculated respectively to this row.
Step S1043: the row identical to eigenwert merges.
The first row of example matrix of differences as shown in Figure 4, by obtaining " null|SOME_URL14-10 " after each element splicing, then adopts MD5 algorithm to ask eigenwert to this splicing string, and deposits this eigenwert in Hash table.Be appreciated that the row that comprises identical element, its eigenwert of trying to achieve is identical.Successively the every row element in matrix of differences spliced and ask eigenwert, and in the time depositing eigenwert in Hash table, in definite table, whether have this eigenwert, if, current line corresponding this eigenwert is abandoned, thereby realize, the identical row of eigenwert is carried out to the object merging fast.
Matrix of differences after merging is exported as test result, and in the time of output, highlighted demonstration difference element (the difference element that the element being there are differences at correspondence position by the eigenmatrix of tested module and the eigenmatrix of base modules obtains), can help tester to determine fast and cause that the sample data of difference and the variance data of this sample data stream (i.e. the result of a dimension) appear in tested module and base modules, like this, tester just can further analyze the data stream of difference, to determine whether tested module exists defect.
Please refer to Fig. 5, Fig. 5 is the structural representation block diagram of the embodiment of the device of comparing test in the present invention.As shown in Figure 5, this device comprises: log acquisition unit 201, conversion unit 202, difference acquiring unit 203, extensive unit 204 and merge cells 205.
Wherein, log acquisition unit 201, for the sample data of one or more is sent to respectively to tested module and base modules, after wherein tested module and base modules are processed the sample data receiving, output processing daily record separately respectively.
Article one, sample data is that tested module or base modules complete the needed primitive of single treatment process.For example, the function of tested module or base modules is that the page is classified, and its primitive is the URL address of a page.
The sample data that log acquisition unit 201 is sent to tested module and base modules is identical, and same sample data can be sent to respectively tested module and base modules.After tested module is processed each the sample data receiving, by the processing daily record of output oneself, after base modules is processed each the sample data receiving, also can export the processing daily record of oneself.
In the processing daily record that log acquisition unit 201 is exported, every record comprises a sample data, and the result of at least one dimension being obtained by this sample data.
Please refer to false code piece below:
Above-mentioned modules A can be tested module or base modules.
Corresponding with above-mentioned false code, the processing daily record of tested module please refer to log recording below:
Article 2, record:
Article 3, record:
Corresponding with above-mentioned false code, the processing daily record of base modules please refer to log recording below:
url=www.sina.com,redir=www.sina.com/index.html,type=C,value=20,weight=40
Article 2, record:
Article 3, record:
In above-mentioned log recording, " url " field corresponding record be exactly sample data, " redir ", " type ", " value ", " weight " field corresponding record be exactly the result of a dimension.
Particularly, the each element in eigenmatrix, represents the result of the dimension that a sample data obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
When processing field in daily record and be converted into the element in eigenmatrix, according to pre-configured field type, search the transformation rule corresponding with the type, the result can obtain this field and be converted into the element in eigenmatrix time.Suppose that the field type in the tested module of above-mentioned signal and the processing daily record of base modules is all configured to primary type, the transformation rule of corresponding primary type is the direct element content in eigenmatrix using the content of this field, the eigenmatrix being obtained by the processing daily record of above-mentioned tested module can be with reference to figure 2a, and the eigenmatrix being obtained by the processing daily record of said reference module can be with reference to figure 2b.
Each field of processing in daily record also can be configured to respectively different types in advance, corresponding every type, all has a kind of transformation rule.The transformation rule that can adopt in the present invention can be referring to table 1.
Each element content in matrix of differences is the difference between the eigenmatrix of tested module and the element of the eigenmatrix correspondence position of base modules.
In the present invention, adoptable difference rule can be referring to table 2.
Take two matrixes shown in Fig. 2 a and Fig. 2 b as example, suppose matrix first row to be configured to primary difference, matrix secondary series is configured to similarities and differences difference, and matrix the 3rd row are configured to Hamming difference, matrix the 4th row are configured to apart from difference, in Fig. 2 a and Fig. 2 b the element of the first row to get difference as follows:
First row element difference is that null|www.sina.com/index.html(is spliced by null and www.sina.com/index.html), secondary series element difference is that 1(is because A is different with C), the 3rd column element difference is that 4(is because 10 binary number is 00001010,20 binary number is 00010100, the the 4th to the 7th difference, different figure places is 4), the 4th column element difference is-10(is because 30-40=-10).
Below the production process that for example understands a row element in matrix of differences, according to similar process, matrix of differences can obtain several rows element.Please refer to Fig. 3, Fig. 3 is the schematic diagram of matrix of differences in the present invention.
Merge cells 205, for merging the colleague mutually of the matrix of differences after extensive.To matrix of differences carry out extensive after, may there are some identical row, due to the test data enormous amount in contrast test, therefore matrix of differences may comprise the data of ten million row, if when the columns of matrix of differences is also a lot, merge colleague mutually by the mode directly each element of every row being compared, the computational resource expending and time are all huge.
Provide a kind of embodiment of merge cells 205 below.
Please refer to Fig. 6, Fig. 6 is the structural representation block diagram of the embodiment of merge cells 205 in the present invention.As shown in Figure 6, merge cells 205 comprises: concatenation unit 2051, computing unit 2052 and row merge cells 2053.Wherein concatenation unit 2051, for splicing the same row element of the matrix of differences after extensive.Computing unit 2052, for calculating respectively the eigenwert of this row to spliced each row.Row merge cells 2053, for merging the identical row of eigenwert.
The first row of example matrix of differences as shown in Figure 4, concatenation unit 2051 will obtain " null|SOME_URL14-10 " after each element splicing, then computing unit 2052 adopts MD5 algorithm to ask eigenwert to this splicing string, and deposits this eigenwert in Hash table by row merge cells 2053.Be appreciated that the row that comprises identical element, its eigenwert of trying to achieve is identical.Concatenation unit 2051 and computing unit 2052 splice the every row element in matrix of differences successively and ask eigenwert, and when the merge cells 2053 of being expert at deposits eigenwert in Hash table, in definite table, whether have this eigenwert, if, current line corresponding this eigenwert is abandoned, thereby realize, the identical row of eigenwert is carried out to the object merging fast.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of making, be equal to replacement, improvement etc., within all should being included in the scope of protection of the invention.
Claims (10)
1. a method for Data Comparison test, comprising:
A. the sample data of one or more is sent to respectively to tested module and base modules, after wherein said tested module and described base modules are processed the sample data receiving, output processing daily record separately respectively;
B. according to pre-configured transformation rule, described tested module and the processing daily record separately of described base modules are converted into eigenmatrix separately;
C. according to pre-configured difference rule, obtain the matrix of differences between the eigenmatrix of described tested module and the eigenmatrix of described base modules;
D. the element in described matrix of differences is carried out extensive, and, colleague mutually in the matrix of differences after extensive is merged.
2. method according to claim 1, is characterized in that, every record of described processing daily record comprises a sample data, and the result of at least one dimension being obtained by this sample data.
3. method according to claim 2, it is characterized in that, the result of the dimension that sample data of each element representation of described eigenmatrix obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
4. method according to claim 1, is characterized in that, the step of " carrying out extensive to the element in described matrix of differences " specifically comprises:
For the each element in described matrix of differences, search pre-configured abstraction rule table, in the time having the application rule of this element in described abstraction rule table, this element is carried out extensive according to described application rule.
5. method according to claim 1, is characterized in that, the step of " colleague mutually in the matrix of differences after extensive is merged " specifically comprises:
Same row element in matrix of differences after extensive is spliced;
Spliced each row is calculated respectively to the eigenwert of this row;
The row identical to eigenwert merges.
6. a device for Data Comparison test, comprising:
Log acquisition unit, for the sample data of one or more is sent to respectively to tested module and base modules, after wherein said tested module and described base modules are processed the sample data receiving, output processing daily record separately respectively;
Conversion unit, for according to pre-configured transformation rule, is converted into eigenmatrix separately by described tested module and the processing daily record separately of described base modules;
Difference acquiring unit, for according to pre-configured difference rule, obtains the matrix of differences between the eigenmatrix of described tested module and the eigenmatrix of described base modules;
Extensive unit, for carrying out extensive to the element of described matrix of differences;
Merge cells, for merging the colleague mutually of the matrix of differences after extensive.
7. device according to claim 6, is characterized in that, every record of described processing daily record comprises a sample data, and, the result of at least one dimension being obtained by this sample data.
8. device according to claim 7, it is characterized in that, the result of the dimension that sample data of each element representation of described eigenmatrix obtains, and, with the corresponding same sample data of element of a line, the result of the corresponding same dimension of element of same row.
9. device according to claim 6, is characterized in that, described extensive unit carries out extensive mode to the element in described matrix of differences and specifically comprises:
For the each element in described matrix of differences, search pre-configured abstraction rule table, in the time having the application rule of this element in described abstraction rule table, this element is carried out extensive according to described application rule.
10. device according to claim 6, is characterized in that, described merge cells specifically comprises:
Concatenation unit, for splicing the same row element of the matrix of differences after extensive;
Computing unit, for calculating respectively the eigenwert of this row to spliced each row;
Row merge cells, for merging the identical row of eigenwert.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210459305.XA CN103810095B (en) | 2012-11-15 | 2012-11-15 | A kind of method and device of data comparison test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210459305.XA CN103810095B (en) | 2012-11-15 | 2012-11-15 | A kind of method and device of data comparison test |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103810095A true CN103810095A (en) | 2014-05-21 |
| CN103810095B CN103810095B (en) | 2018-01-05 |
Family
ID=50706894
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210459305.XA Active CN103810095B (en) | 2012-11-15 | 2012-11-15 | A kind of method and device of data comparison test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103810095B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716901A (en) * | 2019-09-25 | 2020-01-21 | 苏宁云计算有限公司 | Performance test data processing method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6986125B2 (en) * | 2001-08-01 | 2006-01-10 | International Business Machines Corporation | Method and apparatus for testing and evaluating a software component using an abstraction matrix |
| CN101452068A (en) * | 2009-01-04 | 2009-06-10 | 信息产业部通信计量中心 | Test method and system for enhancing calibration efficiency of wireless comprehensive test instrument |
| CN102541736A (en) * | 2011-11-30 | 2012-07-04 | 北京航空航天大学 | Acceleration test method in software reliability execution process |
-
2012
- 2012-11-15 CN CN201210459305.XA patent/CN103810095B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6986125B2 (en) * | 2001-08-01 | 2006-01-10 | International Business Machines Corporation | Method and apparatus for testing and evaluating a software component using an abstraction matrix |
| CN101452068A (en) * | 2009-01-04 | 2009-06-10 | 信息产业部通信计量中心 | Test method and system for enhancing calibration efficiency of wireless comprehensive test instrument |
| CN102541736A (en) * | 2011-11-30 | 2012-07-04 | 北京航空航天大学 | Acceleration test method in software reliability execution process |
Non-Patent Citations (2)
| Title |
|---|
| 曹胜玉 等: "非负矩阵分解及其在基因表达数据分析中的应用", 《北京师范大学学报（自然科学版）》 * |
| 王宝龙 等: "基于依赖矩阵的测试性分析", 《计算机测量与控制》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716901A (en) * | 2019-09-25 | 2020-01-21 | 苏宁云计算有限公司 | Performance test data processing method and device, computer equipment and storage medium |
| CN110716901B (en) * | 2019-09-25 | 2023-04-28 | 苏宁云计算有限公司 | Performance test data processing method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103810095B (en) | 2018-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101945009B (en) | Positioning method and device of power communication network fault based on case and pattern matching | |
| CN103198010A (en) | Software testing method, device and system | |
| CN107329933B (en) | Fault detection method and device based on optical fiber sensing vibration signal | |
| CN103684898A (en) | Method and device for monitoring operation of user request in distributed system | |
| CN106646315B (en) | A kind of Auto-Test System and its test method of digital measuring instruments | |
| CN110244185A (en) | A kind of multi-source harmonic contributions division methods, terminal device and storage medium | |
| CN105630656A (en) | Log model based system robustness analysis method and apparatus | |
| CN111123873B (en) | Production data acquisition method and system based on stream processing technology | |
| CN111274056B (en) | Self-learning method and device for fault library of intelligent electric energy meter | |
| CN102043720A (en) | Method and device for generating test data automatically by utilizing structured query language (SQL) sentences | |
| CN103235757B (en) | Several apparatus and method that input domain tested object is tested are made based on robotization | |
| CN110532612A (en) | The operation data processing method and processing device of ship power system | |
| CN108287876B (en) | Power quality data service quality detection method and device supporting multiple formats | |
| CN106227790A (en) | A kind of method using Apache Spark classification and parsing massive logs | |
| CN103810095A (en) | Data comparison test method and device | |
| CN103368970A (en) | Automation security detection method for network target | |
| CN109240903A (en) | A kind of method and apparatus assessed automatically | |
| CN108959574A (en) | A kind of test data analysis method and system | |
| CN116701406A (en) | Data comparison method and device, computer equipment and storage medium | |
| CN112950908B (en) | Data monitoring and early warning method, system, medium and electronic terminal | |
| CN115576831A (en) | Test case recommendation method, device, equipment and storage medium | |
| Abidin et al. | Comparative analysis on techniques for big data testing | |
| CN103914027A (en) | Method for monitoring and managing industrial on-site bottom layer device through expression analysis | |
| CN113688877B (en) | Test data processing method and device, storage medium, instrument and vehicle | |
| Maier et al. | Package ‘redas’ |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |