CN108874665A - A kind of test result method of calibration, device, equipment and medium - Google Patents
A kind of test result method of calibration, device, equipment and medium Download PDFInfo
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- CN108874665A CN108874665A CN201810532054.0A CN201810532054A CN108874665A CN 108874665 A CN108874665 A CN 108874665A CN 201810532054 A CN201810532054 A CN 201810532054A CN 108874665 A CN108874665 A CN 108874665A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/362—Software debugging
- G06F11/3644—Software debugging by instrumenting at runtime
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/362—Software debugging
- G06F11/366—Software debugging using diagnostics
Abstract
The embodiment of the invention discloses a kind of test result method of calibration, device, equipment and media, are related to testing field.This method includes：After executing test, calibration feature is obtained according to the operation data of the test；The calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is directed to the check results of test result.A kind of test result method of calibration, device, equipment and medium provided in an embodiment of the present invention, realize the verification to test result, while reducing verification cost, improve the accuracy rate and stability of verification.
Description
Technical field
The present embodiments relate to testing field more particularly to a kind of test result method of calibration, device, equipment and Jie
Matter.
Background technique
Automatic test cases play the role of in various regression tests it is very crucial, not only saved time manpower survey
Cost is tried, and improves regression test efficiency.The key of automatic test cases effect, in addition to Test Sample Design process
In, various parameters abnormal conditions consider whether comprehensively, the verification for returning the result correctness is also very the key link.If
The verification returned the result can not accurately be carried out, even if test case triggers problem and still cannot quickly be found, thus pole
It is big to reduce automatic test effect.
Currently, the method for calibration in automatic test mainly has：Assert (asserts) method of calibration and diff method of calibration
Two kinds.Wherein assert method of calibration is to set specific rule to each value that test returns the result to verify；And
Diff method of calibration can be described as：Two sets of identical running environment are built, by normal data in wherein a set of running environment
Operation, data to be tested are run in another set of running environment, compare the output of two sets of running environment as a result, realizing to result
Verification.
But inventor has found above-mentioned method of calibration in the implementation of the present invention that there are the following problems：1) work as test
When the value returned the result is more, assert method of calibration needs to set each value specific verification rule, so as to cause school
Test problem at high cost.2) high to the maintenance cost of two sets of running environment consistency in diff method of calibration；Due to influencing to run
The factor of environment consistency is more, poor so as to cause the stability verified to test result；Due to two sets of running environment slightly deviation
It just will affect test accuracy rate, it is low so as to cause the accuracy rate verified to test result.
Summary of the invention
The embodiment of the present invention provides a kind of test result method of calibration, device, equipment and medium, to realize to test result
Verification, while reducing verification cost, improve the accuracy rate and stability of verification.
In a first aspect, the embodiment of the invention provides a kind of test result method of calibration, this method includes：
After executing test, calibration feature is obtained according to the operation data of the test；
The calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is directed to the verification knot of test result
Fruit.
Further, before the calibration feature is inputted the result Knowledge Verification Model that training is completed in advance, further include：
Calibration feature and check results are obtained from the historical data that test case is run；
Based on setting model learning algorithm, model training is carried out according to the calibration feature and the check results, is generated
The result Knowledge Verification Model.
Further, the calibration feature is inputted the result Knowledge Verification Model that training is completed in advance includes:
The noise data in the calibration feature is deleted, wherein noise data is the feature unrelated with the verification of test result
Data；
By the calibration feature input result Knowledge Verification Model that training is completed in advance after erased noise data.
Further, the noise data deleted in the calibration feature includes：
Obtain the feature name of noise data；
Position of the noise data in the calibration feature is determined according to the feature name；
Data at position described in the calibration feature are deleted.
Further, the calibration feature is being inputted into the result Knowledge Verification Model that training is completed in advance, output is for test
As a result after check results, further include:
If check results do not square with the fact, check results are modified, and utilize modified check results and the verification
Feature is trained the result Knowledge Verification Model.
Further, the calibration feature is inputted the result Knowledge Verification Model that training is completed in advance includes：
If the calibration feature is video, an at least width picture is extracted from video according to setting step-length；
It compresses the picture and generates compressed picture；
Feature vector is determined according to the quantity of the pixel of tonal gradations different in the compressed picture；
Described eigenvector is inputted into the result Knowledge Verification Model that training is completed in advance.
Further, the calibration feature includes：It is special that test request category feature, test environment category feature and test return to class
At least one of sign.
Second aspect, the embodiment of the invention also provides a kind of test result calibration equipment, which includes：
Module is obtained, for obtaining calibration feature according to the operation data of the test after executing test；
Correction verification module, for the calibration feature to be inputted the result Knowledge Verification Model that preparatory training is completed, output is for survey
The check results of test result.
The third aspect, the embodiment of the invention also provides a kind of equipment, the equipment includes：
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the test result method of calibration as described in any in the embodiment of the present invention.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program realizes the test result method of calibration as described in any in the embodiment of the present invention when program is executed by processor
The embodiment of the present invention is by obtaining calibration feature from test run data after executing test；It will verification spy
Levy the verification that input results Knowledge Verification Model carries out test result.
Opposite assert method of calibration, it is specific without being set to each value when the value that test returns the result is more
Verification rule, to reduce verification cost.
Opposite diff method of calibration runs two sets to save because need to only use a set of running environment when test
The maintenance cost of environment consistency.The factor for influencing running environment simultaneously also accordingly reduces, so that the stability of verification is improved, into
And improve verification accuracy rate.
In addition, the result Knowledge Verification Model obtained by repetition learning has the ability of self-teaching evolution, which can be with
Intelligent decision is carried out to some abnormal results.
Detailed description of the invention
Fig. 1 is a kind of flow chart for test result method of calibration that the embodiment of the present invention one provides；
Fig. 2 is a kind of flow chart of test result method of calibration provided by Embodiment 2 of the present invention；
Fig. 3 is a kind of flow chart for test result method of calibration that the embodiment of the present invention three provides；
Fig. 4 is that a kind of calibration feature that the embodiment of the present invention three provides obtains flow chart；
Fig. 5 is the flow chart of the test result method of calibration of the offer of the embodiment of the present invention three in practical applications；
Fig. 6 is a kind of structural schematic diagram for test result calibration equipment that the embodiment of the present invention four provides；
Fig. 7 is a kind of structural schematic diagram for equipment that the embodiment of the present invention five provides.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure.
Embodiment one
Fig. 1 is a kind of flow chart for test result method of calibration that the embodiment of the present invention one provides.The present embodiment is applicable
In verified to test result the case where.This method can be executed by a kind of test result calibration equipment, which can be with
It is realized by the mode of software and/or hardware.Referring to Fig. 1, a kind of test result method of calibration provided in this embodiment includes：
S110, execute test after, according to the operation data of the test obtain calibration feature.
Wherein, test can be any type of test, can be also possible to automatic test with manual test.Typically,
The test can be the regression test carried out using automatic test cases.
Specifically, the calibration feature includes：Test request category feature, test environment category feature and test return to category feature
At least one of.
Wherein, test request category feature is the characteristic for describing test target；Testing environment category feature is test luck
The characteristic of row environment；It is the characteristic that test target exports after test run environment that test, which returns to category feature,.
Illustratively, if test target is the automobile after a upgrading, running environment is a rough highway, then
Test request category feature is the data for describing the automobile, and test environment category feature is the data for describing the highway, and test returns to class
Feature is each parameter after the automobile runs through one time on the highway.
It should be noted that above-mentioned example illustrates it is only the citing to help the understanding to concept to carry out, to this reality
Applying a scheme does not have any restriction effect.
Specifically, it may include again a variety of that test request category feature, test environment category feature or test, which return in category feature,
Characteristic.Wherein, this feature data can be taking human as specifying, can also be voluntarily true during study by result Knowledge Verification Model
It is fixed.
Typically, test process can be described as:Test case execution module generates test request and is sent to tested module,
The request of tested module response test executes test in test terminal, returns to test result parameter.Wherein, test request category feature
It can be obtained from test request.Test returns to category feature and can obtain from the test result parameter that tested module returns.It surveys
Test ring border category feature can be obtained from test terminal.Test environment category feature can be the hardware running environment in test terminal
At least one of information, the running environment information of software and network information etc..
S120, the calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is for test result
Check results.
Wherein, as a result Knowledge Verification Model can be any one machine learning model.
Accuracy rate is verified to improve, before the calibration feature is inputted the result Knowledge Verification Model that training is completed in advance,
Further include:
Delete the noise data in the calibration feature；
And/or by the Data Format Transform of the calibration feature be result Knowledge Verification Model needed for data format.
Wherein, noise data is the characteristic unrelated with the verification of test result.
Specifically, the noise data deleted in the calibration feature may include：
Obtain the feature name of noise data；
Position of the noise data in the calibration feature is determined according to the feature name；
Data at position described in the calibration feature are deleted.
Specifically, can determine the corresponding field of the feature according to the feature name, it can be determined and be made an uproar according to field
Position of the sound data in the calibration feature.
Typically, calibration feature can be stored in the form of feature name and characteristic value.Wherein, characteristic value can be with
It is numeric format, text formatting, picture format and video format.However, these formats of characteristic value are not usually result verification
Data format needed for model.Therefore, it is necessary to which the data format to characteristic value is converted.
Specifically, it is directed to numeric format, it can be directly using the numeric data as corresponding member in corresponding feature vector
Element.
For text formatting, it can use a variety of underlying semantics character representation methods and convert text to feature vector shape
Formula；The possible case that existing several limited quantities are determined in semantic analysis can also be carried out to text, by various possible cases
It is indicated respectively with different numerical value, then numerical value is integrally used as to corresponding element in corresponding feature vector.
It, can be using the corresponding matrix of picture as corresponding element in corresponding feature vector for picture format；It can also
To extract the characteristics of image of picture, feature vector is determined according to characteristics of image, wherein characteristics of image can be color characteristic, texture
At least one of feature, shape feature and spatial relation characteristics.
For video format, picture can be first extracted from video, then according to the characteristics of image of picture determine feature to
Amount.
In view of the picture for including in video is more, calculation amount is larger.Typically, if the calibration feature is video,
An at least width picture is extracted from video according to setting step-length；
It compresses the picture and generates compressed picture；
Feature vector is determined according to the quantity of the pixel of tonal gradations different in the compressed picture.
Wherein, calculation amount can be effectively reduced to the compression of picture.And tonal gradation belongs to one kind of color characteristic, is one
Kind global characteristics, describe the surface nature of scenery corresponding to image or image-region.Therefore, it is determined based on tonal gradation
Feature vector can be with effecting reaction video content.
To further increase computational efficiency, compressing the picture generation compressed picture includes：
It compresses the picture and generates the first compressed picture；
Gray compression is carried out to first compressed picture, generates the second compressed picture；
Correspondingly, determining that feature vector includes according to the quantity of the pixel of tonal gradations different in the compressed picture：
Feature vector is determined according to the quantity of the pixel of tonal gradations different in second compressed picture.
The embodiment of the present invention is by obtaining calibration feature from test run data after executing test；It will verification spy
Levy the verification that input results Knowledge Verification Model carries out test result.
Opposite assert method of calibration, it is specific without being set to each value when the value that test returns the result is more
Verification rule, to reduce verification cost.
Opposite diff method of calibration runs two sets to save because need to only use a set of running environment when test
The maintenance cost of environment consistency.The factor for influencing running environment simultaneously also accordingly reduces, so that the stability of verification is improved, into
And improve verification accuracy rate.
In addition, the result Knowledge Verification Model obtained by repetition learning has the ability of self-teaching evolution, which can be with
Intelligent decision is carried out to some abnormal results.
Embodiment two
Fig. 2 is a kind of flow chart of test result method of calibration provided by Embodiment 2 of the present invention.The present embodiment is upper
State a kind of optinal plan proposed on the basis of embodiment.Referring to fig. 2, test result method of calibration packet provided in this embodiment
It includes：
S210, execute test after, according to the operation data of the test obtain calibration feature.
S220, the calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is for test result
Check results.
If S230, check results do not square with the fact, modify check results, and using modified check results with it is described
Calibration feature is trained the result Knowledge Verification Model.
Illustratively, if model learning to rule be that automobile is from origin is set to setting the used time of terminal as 30 points
Clock to 45 minutes be it is normal, then when road conditions are bad, automobile to be measured from origin is set to when setting used time of terminal as 50 minutes,
It will be verified as abnormal.And practical automobile to be measured be because of delay caused by road conditions are bad, it is not related with automobile itself.
Therefore, it can modify at this time to inspection result, using modified check results and calibration feature to result Knowledge Verification Model
It is trained.So that result Knowledge Verification Model learns road conditions unfavorable condition.
It should be noted that above-mentioned example illustrates it is only the citing to help the understanding to concept to carry out, to this reality
Applying a scheme does not have any restriction effect.
The technical solution of the embodiment of the present invention, if modifying check results, and benefit by not squaring with the fact in check results
The result Knowledge Verification Model is trained with the calibration feature with modified check results.Result is verified to realize
The optimization of model, and then improve the accuracy rate of model checking.
Embodiment three
Fig. 3 is a kind of flow chart for test result method of calibration that the embodiment of the present invention three provides.The present embodiment is upper
A kind of optinal plan for being tested using automatic test cases, provided on the basis of embodiment is provided.Referring to Fig. 3,
Test result method of calibration provided in this embodiment includes：
S310, calibration feature and check results are obtained from the historical data that test case is run.
Wherein, test case can be the automatic test cases of manual compiling, be also possible to the automation automatically generated
Test case.Calibration feature includes that test request category feature, test environment category feature and test return to category feature.
Specifically, referring to fig. 4, test case execution module receives test instruction, generates test request and be sent to tested mould
Block, the request of tested module response test execute test in test terminal, return to test result parameter.Test result calibration equipment
Using VCR (recording) function from test request, test terminal and test result parameter in acquisition request category feature, test environmental classes
Feature and test return to category feature, and the characteristic that will acquire is stored in the form of feature name and characteristic value.
Further include after obtaining calibration feature：
Each category feature in category feature is returned to request category feature, test environment category feature and test to screen out and test
Use-case implementing result verifies unrelated characteristic, and this feature data include but is not limited to that log mark, timestamp and test are asked
Seek mark；
It is knot that category feature, test environment category feature and test will be requested, which to return to the Data Format Transform of characteristic value in category feature,
Data format needed for fruit Knowledge Verification Model.
S320, it is based on setting model learning algorithm, model training is carried out according to the calibration feature and check results, is generated
The result Knowledge Verification Model.
Wherein, setting model learning algorithm can be any one machine learning algorithm, specifically can be according to actual needs
It is set.
S330, the result Knowledge Verification Model completed to training are tested.
Specifically, operation test case uses test using the result Knowledge Verification Model that training is completed to new test environment
The test result of example is judged.
S340, the test case not squared with the fact using verification result execution data result Knowledge Verification Model is instructed
Practice, optimum results Knowledge Verification Model.
Referring to Fig. 5, in practical applications, test result method of calibration be can be described as：It is run first with test case
Historical data in test request category feature, test environment category feature and test return category feature to result Knowledge Verification Model carry out
Training；After the completion of training, request category feature, the test environment category feature that operation test case is generated to new test environment are obtained
Category feature is returned with test, and is inputted in the result Knowledge Verification Model of training completion, output verification result.
It should be noted that by the technical teaching of the present embodiment, those skilled in the art have motivation by above-described embodiment
Described in any embodiment carry out the combination of scheme, to realize the verification to test result.
Example IV
Fig. 6 is a kind of structural schematic diagram for test result calibration equipment that the embodiment of the present invention four provides.Referring to Fig. 6, originally
Embodiment provide a kind of test result calibration equipment include：Obtain module 10 and correction verification module 20.
Wherein, module 10 is obtained, for obtaining calibration feature according to the operation data of the test after executing test；
Correction verification module 20, for the calibration feature to be inputted the result Knowledge Verification Model that training is completed in advance, output is directed to
The check results of test result.
The embodiment of the present invention is by obtaining calibration feature from test run data after executing test；It will verification spy
Levy the verification that input results Knowledge Verification Model carries out test result.
Opposite assert method of calibration, it is specific without being set to each value when the value that test returns the result is more
Verification rule, to reduce verification cost.
Opposite diff method of calibration runs two sets to save because need to only use a set of running environment when test
The maintenance cost of environment consistency.The factor for influencing running environment simultaneously also accordingly reduces, so that the stability of verification is improved, into
And improve verification accuracy rate.
In addition, the result Knowledge Verification Model obtained by repetition learning has the ability of self-teaching evolution, which can be with
Intelligent decision is carried out to some abnormal results.
Further, test result calibration equipment further includes：Sample collection module and model training module.
Wherein, sample collection module, for the calibration feature to be inputted the result Knowledge Verification Model that training is completed in advance
Before, calibration feature and check results are obtained from the historical data that test case is run；
Model training module is carried out for being based on setting model learning algorithm according to the calibration feature and check results
Model training generates the result Knowledge Verification Model.
Further, correction verification module includes:Delete unit and input unit.
Wherein, unit is deleted, for deleting the noise data in the calibration feature, wherein noise data is tied with test
The unrelated characteristic of the verification of fruit；
Input unit, for the result calibration mode completed to be trained in the calibration feature input after erased noise data in advance
Type.
It is specifically used for specifically, deleting unit：
Obtain the feature name of noise data；
Position of the noise data in the calibration feature is determined according to the feature name；
Data at position described in the calibration feature are deleted.
Further, test result calibration equipment further includes：Model optimization module.
Wherein, model optimization module, for the calibration feature to be inputted the result Knowledge Verification Model that training is completed in advance,
After output is for the check results of test result, if check results do not square with the fact, check results are modified, and utilize modification
Check results afterwards are trained the result Knowledge Verification Model with the calibration feature.
Further, correction verification module includes：Picture extraction unit, picture compression unit, vector determination unit and vector are defeated
Enter unit.
Wherein, picture extraction unit is extracted from video if being video for the calibration feature according to setting step-length
An at least width picture；
Picture compression unit generates compressed picture for compressing the picture；
Vector determination unit, the quantity for the pixel according to tonal gradations different in the compressed picture determine feature
Vector；
Vector input unit, for described eigenvector to be inputted the result Knowledge Verification Model that training is completed in advance.
Further, the calibration feature includes：It is special that test request category feature, test environment category feature and test return to class
At least one of sign.
Embodiment five
Fig. 7 is a kind of structural schematic diagram for equipment that the embodiment of the present invention five provides.Fig. 7, which is shown, to be suitable for being used to realizing this
The block diagram of the example devices 12 of invention embodiment.The equipment 12 that Fig. 7 is shown is only an example, should not be to of the invention real
The function and use scope for applying example bring any restrictions.
As shown in fig. 7, equipment 12 is showed in the form of universal computing device.The component of equipment 12 may include but unlimited
In：One or more processor or processing unit 16, system storage 28, connecting different system components, (including system is deposited
Reservoir 28 and processing unit 16) bus 18.
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Equipment 12 typically comprises a variety of computer system readable media.These media can be it is any can be by equipment 12
The usable medium of access, including volatile and non-volatile media, moveable and immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (RAM) 30 and/or cache memory 32.Equipment 12 may further include it is other it is removable/nonremovable,
Volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for reading and writing irremovable
, non-volatile magnetic media (Fig. 7 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 7, use can be provided
In the disc driver read and write to removable non-volatile magnetic disk (such as " floppy disk "), and to removable anonvolatile optical disk
The CD drive of (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, each driver can
To be connected by one or more data media interfaces with bus 18.Memory 28 may include at least one program product,
The program product has one group of (for example, at least one) program module, these program modules are configured to perform each implementation of the invention
The function of example.
Program/utility 40 with one group of (at least one) program module 42 can store in such as memory 28
In, such program module 42 include but is not limited to operating system, one or more application program, other program modules and
It may include the realization of network environment in program data, each of these examples or certain combination.Program module 42 is usual
Execute the function and/or method in embodiment described in the invention.
Equipment 12 can also be communicated with one or more external equipments 14 (such as keyboard, sensing equipment, display 24 etc.),
Can also be enabled a user to one or more equipment interacted with the equipment 12 communication, and/or with enable the equipment 12 with
One or more of the other any equipment (such as network interface card, modem etc.) communication for calculating equipment and being communicated.It is this logical
Letter can be carried out by input/output (I/O) interface 22.Also, equipment 12 can also by network adapter 20 and one or
The multiple networks of person (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet) communication.As shown,
Network adapter 20 is communicated by bus 18 with other modules of equipment 12.It should be understood that although not shown in the drawings, can combine
Equipment 12 uses other hardware and/or software module, including but not limited to：Microcode, device driver, redundant processing unit,
External disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize a kind of test result method of calibration provided by the embodiment of the present invention, this method includes：
After executing test, calibration feature is obtained according to the operation data of the test；
The calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is directed to the verification knot of test result
Fruit.
Embodiment six
The embodiment of the present invention six additionally provides a kind of computer readable storage medium, is stored thereon with computer program, should
Realize that the test result method of calibration as described in any in the embodiment of the present invention, this method include when program is executed by processor：
After executing test, calibration feature is obtained according to the operation data of the test；
The calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is directed to the verification knot of test result
Fruit.
The computer storage medium of the embodiment of the present invention, can be using any of one or more computer-readable media
Combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.It is computer-readable
Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or
Device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes：Tool
There are electrical connection, the portable computer diskette, hard disk, random access memory (RAM), read-only memory of one or more conducting wires
(ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage
Medium can be any tangible medium for including or store program, which can be commanded execution system, device or device
Using or it is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
Further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (10)
1. a kind of test result method of calibration, which is characterized in that including：
After executing test, calibration feature is obtained according to the operation data of the test；
The calibration feature is inputted into the result Knowledge Verification Model that training is completed in advance, output is directed to the check results of test result.
2. the method according to claim 1, wherein the calibration feature is inputted the knot that training is completed in advance
Before fruit Knowledge Verification Model, further include：
Calibration feature and check results are obtained from the historical data that test case is run；
Based on setting model learning algorithm, model training is carried out according to the calibration feature and the check results, described in generation
As a result Knowledge Verification Model.
3. the method according to claim 1, wherein the calibration feature is inputted the result that training is completed in advance
Knowledge Verification Model includes:
The noise data in the calibration feature is deleted, wherein noise data is the characteristic unrelated with the verification of test result
According to；
By the calibration feature input result Knowledge Verification Model that training is completed in advance after erased noise data.
4. according to the method described in claim 3, it is characterized in that, the noise data deleted in the calibration feature includes：
Obtain the feature name of noise data；
Position of the noise data in the calibration feature is determined according to the feature name；
Data at position described in the calibration feature are deleted.
5. the method according to claim 1, wherein the calibration feature is inputted the knot that training is completed in advance
Fruit Knowledge Verification Model, output are directed to after the check results of test result, further include:
If check results do not square with the fact, check results are modified, and utilize modified check results and the calibration feature
The result Knowledge Verification Model is trained.
6. the method according to claim 1, wherein the calibration feature is inputted the result that training is completed in advance
Knowledge Verification Model includes：
If the calibration feature is video, an at least width picture is extracted from video according to setting step-length；
It compresses the picture and generates compressed picture；
Feature vector is determined according to the quantity of the pixel of tonal gradations different in the compressed picture；
Described eigenvector is inputted into the result Knowledge Verification Model that training is completed in advance.
7. the method according to claim 1, wherein the calibration feature includes：Test request category feature, test
Environment category feature and test return at least one of category feature.
8. a kind of test result calibration equipment, which is characterized in that including：
Module is obtained, for obtaining calibration feature according to the operation data of the test after executing test；
Correction verification module, for the calibration feature to be inputted the result Knowledge Verification Model that training is completed in advance, output is for test knot
The check results of fruit.
9. a kind of equipment, which is characterized in that the equipment includes：
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now test result method of calibration as described in any in claim 1-7.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The test result method of calibration as described in any in claim 1-7 is realized when execution.
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